U.S. patent application number 16/932689 was filed with the patent office on 2020-11-05 for warehouse batch product picking optimization using high density areas to minimize travel.
The applicant listed for this patent is COUPANG CORP.. Invention is credited to Zijian HU, Jinxing LU, Wenting MO, Kai WEI, Dong YANG.
Application Number | 20200349478 16/932689 |
Document ID | / |
Family ID | 1000004961323 |
Filed Date | 2020-11-05 |
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United States Patent
Application |
20200349478 |
Kind Code |
A1 |
MO; Wenting ; et
al. |
November 5, 2020 |
WAREHOUSE BATCH PRODUCT PICKING OPTIMIZATION USING HIGH DENSITY
AREAS TO MINIMIZE TRAVEL
Abstract
The disclosed embodiments provide computer-implemented systems
and methods for batch picking optimization. The system may include
one or more memory devices storing instructions and one or more
processors configured to execute the instructions to receive an
order comprising one or more items for picking. Additionally, the
system may calculate one or more high density areas in a
fulfillment center by calculating distances between a first item in
the one or more items and at least one other item in the one or
more items using a search algorithm. Additionally, the system may
calculate nearest neighboring items for the one or more items and
generate a high density area by choosing a plurality of the nearest
neighboring items.
Inventors: |
MO; Wenting; (Beijing,
CN) ; WEI; Kai; (Shanghai, CN) ; LU;
Jinxing; (Shanghai, CN) ; HU; Zijian;
(Beijing, CN) ; YANG; Dong; (Shanghai,
CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
COUPANG CORP. |
Seoul |
|
KR |
|
|
Family ID: |
1000004961323 |
Appl. No.: |
16/932689 |
Filed: |
July 17, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16386948 |
Apr 17, 2019 |
10783462 |
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16932689 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/047 20130101;
G06N 20/00 20190101; G06Q 10/06316 20130101; G06F 16/90335
20190101; G06Q 10/0875 20130101 |
International
Class: |
G06Q 10/04 20060101
G06Q010/04; G06N 20/00 20060101 G06N020/00; G06Q 10/06 20060101
G06Q010/06; G06F 16/903 20060101 G06F016/903; G06Q 10/08 20060101
G06Q010/08 |
Claims
1. A computer-implemented system for batch picking optimization,
the system comprising: one or more memory devices storing
instructions; and one or more processors configured to execute the
instructions to: receive an order over a communication network, the
order comprising one or more items; calculate one or more high
density areas in a fulfillment center by: calculating distances
between a first item in the one or more items and at least one
other item in the one or more items using a search algorithm,
calculating nearest neighboring items for the one or more items,
and generating a high density area by choosing a plurality of the
nearest neighboring items; store, for the one or more items, a
distance between a respective item and a closest second item;
create a batch based on the calculated one or more high density
areas; add items from the one or more high density areas into the
batch based on an item increasing an average distance least among
pickable items, using a digital map to determine an exchange of
items that will increase a quality of the batch; provide, over the
communications network, the digital map and instructions with a
list of items for gathering in the batch to a user device for
display; receive, over the communications network, detail
information for the batch and scanned item data; and update the
digital map based on a change associated with stowed items and the
detail information for the batch.
2. The system of claim 1, wherein the high density areas are
calculated based on optimizing choosing of the nearest neighboring
items to incur minimal travel distance.
3. The system of claim 1, wherein the processor is further
configured to execute the instructions to execute a gradient
descent algorithm that adds items from the one or more high density
areas into the batch and uses the digital map to determine an
exchange of items that will increase a quality of the batch.
4. The system of claim 1, wherein: the user device is one of a
mobile user device, PDA, a smart phone, a tablet, a laptop, or
other computer device; and wherein the system further comprises a
database comprising at least one record associating the user device
with a user identifier and providing the digital map and
instructions with a list of items for gathering in the batch to the
user device for display is in response to the system querying the
database for the user device associated with the user
identifier.
5. The system of claim 1, wherein choosing the plurality of the
nearest neighboring items comprises choosing three nearest
neighboring items.
6. The system of claim 1, wherein the items in the batch for
gathering include a package identifier associated with a Stock
Keeping Unit (SKU) for display on the user device.
7. The system of claim 1, wherein calculating distances between
items comprises: retrieving the digital map comprising location
pairs, each pair representing two pickable items; and calculating
distances between items using a plurality of the retrieved location
pairs.
8. A computer-implemented system for batch picking optimization,
the system comprising: one or more memory devices storing
instructions; a database comprising at least one record associating
a user device with a user identifier; and one or more processors
configured to execute the instructions to: receive an order over a
communication network, the order comprising one or more items;
calculate one or more high density areas in a fulfillment center
by: calculating distances between a first item in the one or more
items and at least one other item in the one or more items using a
search algorithm, calculating nearest neighboring items for the one
or more items, and generating a high density area by choosing a
plurality of the nearest neighboring items; store, for the one or
more items, a distance between a respective item and a closest
second item; create a batch based on the calculated one or more
high density areas; add items from the one or more high density
areas into the batch based on an item increasing an average
distance least among pickable items, using a digital map to
determine an exchange of items that will increase a quality of the
batch; provide, over the communications network, the digital map
and instructions with a list of items for gathering in the batch to
the user device for display in response to querying the database
for the user device associated with the user identifier; receive,
over the communications network, detail information for the batch
and scanned item data; and update the digital map based on a change
associated with stowed items and the detail information for the
batch.
9. The system of claim 8, wherein the high density areas are
calculated based on optimizing choosing of the nearest neighboring
items to incur minimal travel distance.
10. The system of claim 8, wherein the processor is further
configured to execute the instructions to execute a gradient
descent algorithm that adds items from the one or more high density
areas into the batch and uses the digital map to determine an
exchange of items that will increase a quality of the batch.
11. The system of claim 8, wherein the user device is one of a
mobile user device, a PDA, a smart phone, a tablet, a laptop, or
other computer device.
12. The system of claim 8, wherein choosing the plurality of the
nearest neighboring items is three nearest neighboring items.
13. The system of claim 8, wherein items in the batch include a
barcode associated with the Stock Keeping Units (SKUs) provided in
the list of items for gathering in the batch on the user device for
display.
14. The system of claim 8, wherein the digital map comprises
location pairs, each pair representing two pickable items.
15. A computer-implemented method for batch picking optimization,
the method comprising: receiving an order over a communication
network, the order comprising one or more items; calculating one or
more high density areas in a fulfillment center by: calculating
distances between a first item in the one or more items and at
least one other item in the one or more items using a search
algorithm, calculating nearest neighboring items for the one or
more items, and generating a high density area by choosing a
plurality of the nearest neighboring items; storing, for the one or
more items, a distance between a respective item and a closest
second item; creating a batch based on the calculated one or more
high density areas; add items from the one or more high density
areas into the batch based on an item increasing an average
distance least among pickable items, using a digital map to
determine an exchange of items that will increase a quality of the
batch; providing, over the communications network, the digital map
and instructions with a list of items for gathering in the batch to
a user device for display; receiving, over the communications
network, detail information for the batch and scanned item data;
and updating the digital map based on a change associated with
stowed items and the detail information for the batch.
16. The system of claim 15, wherein the high density areas are
calculated based on optimizing choosing of the nearest neighboring
items to incur minimal travel distance.
17. The system of claim 15, wherein the processor is further
configured to execute the instructions to execute a gradient
descent algorithm that adds items from the one or more high density
areas into the batch and uses the digital map to determine an
exchange of items that will increase a quality of the batch.
18. The system of claim 15, wherein the user device is one of a
mobile user device, a PDA, a smart phone, a tablet, a laptop, or
other computer device.
19. The system of claim 15, wherein choosing the plurality of the
nearest neighboring items is three nearest neighboring items.
20. The system of claim 15, wherein choosing the plurality of the
nearest neighboring items comprises choosing three nearest
neighboring items.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 16/386,948, filed Apr. 17, 2019 (now allowed). The contents of
the above are incorporated herein by reference.
TECHNICAL FIELD
[0002] The present disclosure generally relates to computerized
systems and methods for artificial intelligence batch picking
optimization and communication. In particular, embodiments of the
present disclosure relate to inventive and unconventional systems
which may calculate one or more high density areas in a fulfillment
center, create one or more batches, add items from the one or more
high density areas using a gradient descent algorithm into a single
batch, and provide a list of items for adding/gathering.
BACKGROUND
[0003] Present systems for batch optimization and communication
define paths along aisles and shelves in a fulfillment center and
assign items for picking along the defined paths to a picker.
Pickers select a batch of items that includes parts or all of one
or more orders placed by customers. Pickers may be assigned the
batch and may be sent a path (via a user device) that instructs the
picker on how to walk up and down the aisles of a fulfillment
center to gather all of the items in that batch. This system is
inefficient because it causes delays in assigning orders to
pickers.
[0004] Consequently, pickers must walk a significant amount of time
and distance to pick up items and may delay fulfillment of any
order in the batch. Such delay in batch picking causes additional
interruptions in the shipment process in many respects. For
example, delaying an order shipment until an entire batch with
distant items is picked causes delays in the processing of multiple
orders.
[0005] In view of the shortcomings of current electronic systems
and methods for batch optimization and communication, a system for
enhancing the shipping, transportation, and logistics operation of
shipping orders using batch optimization--calculating high density
areas in the fulfillment center and creating batches based on those
areas--is desired. More specifically, a computer-implemented system
and method for artificial intelligence batch picking optimization
and communication is desired to provide efficiency by finishing
orders faster since items in an optimized batch are closer to each
other and may be picked more quickly. Such a system would allow for
efficiently grouping items by a density algorithm, getting more
orders through the system faster, taking in more orders, and
cutting down wasted time walking to distant items. Therefore, there
is a need for improved electronic methods and systems for
artificial intelligence batch picking optimization and
communication.
SUMMARY
[0006] One aspect of the present disclosure is directed to a
computer-implemented system for batch picking optimization. For
example, certain embodiments may include one or more memory devices
storing instructions and one or more processors configured to
execute the instructions. In some embodiments, the one or more
processors are configured to execute the instructions to receive an
order comprising one or more items for picking and calculate one or
more high density areas in a fulfillment center by: calculating
distances between a first item in the one or more items and at
least one other item in the one or more items using a search
algorithm, calculating nearest neighboring items for the one or
more items, and generating a high density area of the one or more
high density areas by choosing a plurality of the nearest
neighboring items. In some embodiments, the one or more processors
are configured to execute the instructions to store, for the one or
more items, a distance between the first item and a closest second
item and create a batch based on the calculated one or more high
density areas. Additionally, the one or more processors are
configured to add items from the one or more high density areas
into the batch using a gradient descent algorithm, the adding based
on an item increasing an average distance least among pickable
items and provide a list of items for gathering in the batch and a
location on a user device for display.
[0007] Another aspect of the present disclosure is directed to a
computer-implemented system for batch picking optimization. For
example, certain embodiments may include one or more memory devices
storing instructions and one or more processors configured to
execute the instructions. In some embodiments, the one or more
processors are configured to execute the instructions to receive an
order comprising one or more items for picking and a digital map
segmented into multiple zones and calculate one or more high
density areas in a fulfillment center by: calculating distances
between a first item in the one or more items and at least one
other item in the one or more items using a search algorithm in a
single zone of the multiple zones, calculating nearest neighboring
items for the one or more items, and generating a high density area
of the one or more high density areas by choosing a plurality of
the nearest neighboring items. In some embodiments, the one or more
processors are configured to store, for the one or more items, a
distance between the first item and a closest second item and
create a batch based on the calculated one or more high density
areas from items in the single zone of the multiple zones.
Additionally, the one or more processors are configured to add
items from the one or more high density areas into the batch using
a gradient descent algorithm, the adding based on an item
increasing an average distance least among pickable items and
provide a list of items for gathering in the batch and a location
on a user device for display.
[0008] Yet another aspect of the present disclosure is directed to
a computer-implemented method for batch picking optimization. For
example, certain embodiments of the method may include receiving an
order comprising one or more items for picking and a digital map
segmented into multiple zones, wherein the digital map comprises
location pairs, each pair representing two pickable items and
calculating one or more high density areas in a fulfillment center
by: calculating distances between a first item in the one or more
items and at least one other item in the one or more items using a
search algorithm in a single zone of the multiple zones,
calculating nearest neighboring items for the one or more items,
and generating a high density area of the one or more high density
areas by choosing a plurality of the nearest neighboring items. In
some embodiments, the method may further include storing, for the
one or more items, a distance between the first item and a closest
second item and creating a batch based on the calculated one or
more high density areas from items in the single zone of the
multiple zones. Additionally, the method may include adding items
from the one or more high density areas into the batch using a
gradient descent algorithm, the adding based on an item increasing
an average distance least among pickable items and providing a list
of items for gathering in the batch and a location on a user device
for display.
[0009] Other systems, methods, and computer-readable media are also
discussed herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1A is a schematic block diagram illustrating an
exemplary embodiment of a network comprising computerized systems
for communications enabling shipping, transportation, and logistics
operations, consistent with the disclosed embodiments.
[0011] FIG. 1B depicts a sample Search Result Page (SRP) that
includes one or more search results satisfying a search request
along with interactive user interface elements, consistent with the
disclosed embodiments.
[0012] FIG. 1C depicts a sample Single Display Page (SDP) that
includes a product and information about the product along with
interactive user interface elements, consistent with the disclosed
embodiments.
[0013] FIG. 1D depicts a sample Cart page that includes items in a
virtual shopping cart along with interactive user interface
elements, consistent with the disclosed embodiments.
[0014] FIG. 1E depicts a sample Order page that includes items from
the virtual shopping cart along with information regarding purchase
and shipping, along with interactive user interface elements,
consistent with the disclosed embodiments.
[0015] FIG. 2 is a diagrammatic illustration of an exemplary
fulfillment center configured to utilize disclosed computerized
systems, consistent with the disclosed embodiments.
[0016] FIG. 3 is a block diagram of an exemplary process including
batch creation, consistent with the disclosed embodiments.
[0017] FIG. 4 is a diagrammatic illustration of an exemplary
process including data flow of batch creation, consistent with the
disclosed embodiments.
[0018] FIG. 5 is a diagrammatic illustration of an exemplary
process including a batch visualization tool, consistent with the
disclosed embodiments.
[0019] FIG. 6 depicts results of a gradient descent algorithm for
walking distance optimization consistent with the disclosed
embodiments.
[0020] FIG. 7 is a block diagram of an exemplary process for batch
optimization, consistent with disclosed embodiments.
DETAILED DESCRIPTION
[0021] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar parts. While several illustrative
embodiments are described herein, modifications, adaptations and
other implementations are possible. For example, substitutions,
additions, or modifications may be made to the components and steps
illustrated in the drawings, and the illustrative methods described
herein may be modified by substituting, reordering, removing, or
adding steps to the disclosed methods. Accordingly, the following
detailed description is not limited to the disclosed embodiments
and examples. Instead, the proper scope of the invention is defined
by the appended claims.
[0022] Embodiments of the present disclosure are directed to
systems and methods configured for batch picking optimization. For
example, certain embodiments may include one or more memory devices
storing instructions and one or more processors configured to
execute the instructions. In some embodiments, the one or more
processors are configured to receive an order comprising one or
more items for picking and calculate one or more high density areas
in a fulfillment center by: calculating distances between a first
item in the one or more items and at least one other item in the
one or more items using a search algorithm, calculating nearest
neighboring items for the one or more items, and generating a high
density area by choosing a plurality of the nearest neighboring
items. Additionally, the one or more processors are configured to
store, for the one or more items, a distance between the first item
and a closest second item and create a batch based on the
calculated one or more high density areas. Moreover, the one or
more processors are configured to add items from the one or more
high density areas into the batch using a gradient descent
algorithm, the adding based on an item increasing an average
distance least among pickable items and provide a list of items for
gathering in the batch and a location on a user device for
display.
[0023] Furthermore, the present disclosure is directed to systems
and methods for enhancing the shipping, transportation, and
logistics operation of shipping orders using batch
optimization--calculating high density areas in the fulfillment
center and creating batches based on those areas. More
specifically, the disclosed computer-implemented system and method
for artificial intelligence batch picking optimization and
communication provides efficiency by finishing orders faster since
items in an optimized batch are closer to each other and may be
picked more quickly. The present system allows for efficiently
grouping items by a density algorithm, getting more orders through
the system faster, taking in more orders, and cutting down wasted
time walking to distant items.
[0024] Referring to FIG. 1A, a schematic block diagram illustrating
an exemplary embodiment of a system 100 comprising computerized
systems for communications enabling shipping, transportation, and
logistics operations is shown. As illustrated in FIG. 1A, system
100 may include a variety of systems, each of which may be
connected to one another via one or more networks. The systems may
also be connected to one another via a direct connection, for
example, using a cable. The depicted systems include a shipment
authority technology (SAT) system 101, an external front end system
103, an internal front end system 105, a transportation system 107,
mobile devices 107A, 107B, and 107C, seller portal 109, shipment
and order tracking (SOT) system 111, fulfillment optimization (FO)
system 113, fulfillment messaging gateway (FMG) 115, supply chain
management (SCM) system 117, warehouse management system 119,
mobile devices 119A, 119B, and 119C (depicted as being inside of
fulfillment center (FC) 200), 3.sup.rd party fulfillment systems
121A, 121B, and 121C, fulfillment center authorization system (FC
Auth) 123, and labor management system (LMS) 125.
[0025] SAT system 101, in some embodiments, may be implemented as a
computer system that monitors order status and delivery status. For
example, SAT system 101 may determine whether an order is past its
Promised Delivery Date (PDD) and may take appropriate action,
including initiating a new order, reshipping the items in the
non-delivered order, canceling the non-delivered order, initiating
contact with the ordering customer, or the like. SAT system 101 may
also monitor other data, including output (such as a number of
packages shipped during a particular time period) and input (such
as the number of empty cardboard boxes received for use in
shipping). SAT system 101 may also act as a gateway between
different devices in system 100, enabling communication (e.g.,
using store-and-forward or other techniques) between devices such
as external front end system 103 and FO system 113.
[0026] External front end system 103, in some embodiments, may be
implemented as a computer system that enables external users to
interact with one or more systems in system 100. For example, in
embodiments where system 100 enables the presentation of systems to
enable users to place an order for an item, external front end
system 103 may be implemented as a web server that receives search
requests, presents item pages, and solicits payment information.
For example, external front end system 103 may be implemented as a
computer or computers running software such as the Apache HTTP
Server, Microsoft Internet Information Services (IIS), NGINX, or
the like. In other embodiments, external front end system 103 may
run custom web server software designed to receive and process
requests from external devices (e.g., mobile device 102A or
computer 102B), acquire information from databases and other data
stores based on those requests, and provide responses to the
received requests based on acquired information.
[0027] In some embodiments, external front end system 103 may
include one or more of a web caching system, a database, a search
system, or a payment system. In one aspect, external front end
system 103 may comprise one or more of these systems, while in
another aspect, external front end system 103 may comprise
interfaces (e.g., server-to-server, database-to-database, or other
network connections) connected to one or more of these systems.
[0028] An illustrative set of steps, illustrated by FIGS. 1B, 1C,
1D, and 1E, may help to describe some operations of external front
end system 103. External front end system 103 may receive
information from systems or devices in system 100 for presentation
and/or display. For example, external front end system 103 may host
or provide one or more web pages, including a Search Result Page
(SRP) (e.g., FIG. 1B), a Single Detail Page (SDP) (e.g., FIG. 1C),
a Cart page (e.g., FIG. 1D), or an Order page (e.g., FIG. 1E). A
user device (e.g., using mobile device 102A or computer 102B) may
navigate to external front end system 103 and request a search by
entering information into a search box. External front end system
103 may request information from one or more systems in system 100.
For example, external front end system 103 may request information
from FO System 113 that satisfies the search request. External
front end system 103 may also request and receive (from FO System
113) a Promised Delivery Date or "PDD" for each product included in
the search results. The PDD, in some embodiments, may represent an
estimate of when a package containing the product may arrive at the
user's desired location or a date by which the product is promised
to be delivered at the user's desired location if ordered within a
particular period of time, for example, by the end of the day
(11:59 PM). (PDD is discussed further below with respect to FO
System 113.)
[0029] External front end system 103 may prepare an SRP (e.g., FIG.
1B) based on the information. The SRP may include information that
satisfies the search request. For example, this may include
pictures of products that satisfy the search request. The SRP may
also include respective prices for each product, or information
relating to enhanced delivery options for each product, PDD,
weight, size, offers, discounts, or the like. External front end
system 103 may send the SRP to the requesting user device (e.g.,
via a network).
[0030] A user device may then select a product from the SRP, e.g.,
by clicking or tapping a user interface, or using another input
device, to select a product represented on the SRP. The user device
may formulate a request for information on the selected product and
send it to external front end system 103. In response, external
front end system 103 may request information related to the
selected product. For example, the information may include
additional information beyond that presented for a product on the
respective SRP. This could include, for example, shelf life,
country of origin, weight, size, number of items in package,
handling instructions, or other information about the product. The
information could also include recommendations for similar products
(based on, for example, big data and/or machine learning analysis
of customers who bought this product and at least one other
product), answers to frequently asked questions, reviews from
customers, manufacturer information, pictures, or the like.
[0031] External front end system 103 may prepare an SDP (Single
Detail Page) (e.g., FIG. 1C) based on the received product
information. The SDP may also include other interactive elements
such as a "Buy Now" button, a "Add to Cart" button, a quantity
field, a picture of the item, or the like. The SDP may further
include a list of sellers that offer the product. The list may be
ordered based on the price each seller offers such that the seller
that offers to sell the product at the lowest price may be listed
at the top. The list may also be ordered based on the seller
ranking such that the highest ranked seller may be listed at the
top. The seller ranking may be formulated based on multiple
factors, including, for example, the seller's past track record of
meeting a promised PDD. External front end system 103 may deliver
the SDP to the requesting user device (e.g., via a network).
[0032] The requesting user device may receive the SDP which lists
the product information. Upon receiving the SDP, the user device
may then interact with the SDP. For example, a user of the
requesting user device may click or otherwise interact with a
"Place in Cart" button on the SDP. This adds the product to a
shopping cart associated with the user. The user device may
transmit this request to add the product to the shopping cart to
external front end system 103.
[0033] External front end system 103 may generate a Cart page
(e.g., FIG. 1D). The Cart page, in some embodiments, lists the
products that the user has added to a virtual "shopping cart." A
user device may request the Cart page by clicking on or otherwise
interacting with an icon on the SRP, SDP, or other pages. The Cart
page may, in some embodiments, list all products that the user has
added to the shopping cart, as well as information about the
products in the cart such as a quantity of each product, a price
for each product per item, a price for each product based on an
associated quantity, information regarding PDD, a delivery method,
a shipping cost, user interface elements for modifying the products
in the shopping cart (e.g., deletion or modification of a
quantity), options for ordering other product or setting up
periodic delivery of products, options for setting up interest
payments, user interface elements for proceeding to purchase, or
the like. A user at a user device may click on or otherwise
interact with a user interface element (e.g., a button that reads
"Buy Now") to initiate the purchase of the product in the shopping
cart. Upon doing so, the user device may transmit this request to
initiate the purchase to external front end system 103.
[0034] External front end system 103 may generate an Order page
(e.g., FIG. 1E) in response to receiving the request to initiate a
purchase. The Order page, in some embodiments, re-lists the items
from the shopping cart and requests input of payment and shipping
information. For example, the Order page may include a section
requesting information about the purchaser of the items in the
shopping cart (e.g., name, address, e-mail address, phone number),
information about the recipient (e.g., name, address, phone number,
delivery information), shipping information (e.g., speed/method of
delivery and/or pickup), payment information (e.g., credit card,
bank transfer, check, stored credit), user interface elements to
request a cash receipt (e.g., for tax purposes), or the like.
External front end system 103 may send the Order page to the user
device.
[0035] The user device may enter information on the Order page and
click or otherwise interact with a user interface element that
sends the information to external front end system 103. From there,
external front end system 103 may send the information to different
systems in system 100 to enable the creation and processing of a
new order with the products in the shopping cart.
[0036] In some embodiments, external front end system 103 may be
further configured to enable sellers to transmit and receive
information relating to orders.
[0037] Internal front end system 105, in some embodiments, may be
implemented as a computer system that enables internal users (e.g.,
employees of an organization that owns, operates, or leases system
100) to interact with one or more systems in system 100. For
example, in embodiments where network 101 enables the presentation
of systems to enable users to place an order for an item, internal
front end system 105 may be implemented as a web server that
enables internal users to view diagnostic and statistical
information about orders, modify item information, or review
statistics relating to orders. For example, internal front end
system 105 may be implemented as a computer or computers running
software such as the Apache HTTP Server, Microsoft Internet
Information Services (IIS), NGINX, or the like. In other
embodiments, internal front end system 105 may run custom web
server software designed to receive and process requests from
systems or devices depicted in system 100 (as well as other devices
not depicted), acquire information from databases and other data
stores based on those requests, and provide responses to the
received requests based on acquired information.
[0038] In some embodiments, internal front end system 105 may
include one or more of a web caching system, a database, a search
system, a payment system, an analytics system, an order monitoring
system, or the like. In one aspect, internal front end system 105
may comprise one or more of these systems, while in another aspect,
internal front end system 105 may comprise interfaces (e.g.,
server-to-server, database-to-database, or other network
connections) connected to one or more of these systems.
[0039] Transportation system 107, in some embodiments, may be
implemented as a computer system that enables communication between
systems or devices in system 100 and mobile devices 107A-107C.
Transportation system 107, in some embodiments, may receive
information from one or more mobile devices 107A-107C (e.g., mobile
phones, smart phones, PDAs, or the like). For example, in some
embodiments, mobile devices 107A-107C may comprise devices operated
by delivery workers. The delivery workers, who may be permanent,
temporary, or shift employees, may utilize mobile devices 107A-107C
to effect delivery of packages containing the products ordered by
users. For example, to deliver a package, the delivery worker may
receive a notification on a mobile device indicating which package
to deliver and where to deliver it. Upon arriving at the delivery
location, the delivery worker may locate the package (e.g., in the
back of a truck or in a crate of packages), scan or otherwise
capture data associated with an identifier on the package (e.g., a
barcode, an image, a text string, an RFID tag, or the like) using
the mobile device, and deliver the package (e.g., by leaving it at
a front door, leaving it with a security guard, handing it to the
recipient, or the like). In some embodiments, the delivery worker
may capture photo(s) of the package and/or may obtain a signature
using the mobile device. The mobile device may send information to
transportation system 107 including information about the delivery,
including, for example, time, date, GPS location, photo(s), an
identifier associated with the delivery worker, an identifier
associated with the mobile device, or the like. Transportation
system 107 may store this information in a database (not pictured)
for access by other systems in system 100. Transportation system
107 may, in some embodiments, use this information to prepare and
send tracking data to other systems indicating the location of a
particular package.
[0040] In some embodiments, certain users may use one kind of
mobile device (e.g., permanent workers may use a specialized PDA
with custom hardware such as a barcode scanner, stylus, and other
devices) while other users may use other kinds of mobile devices
(e.g., temporary or shift workers may utilize off-the-shelf mobile
phones and/or smartphones).
[0041] In some embodiments, transportation system 107 may associate
a user with each device. For example, transportation system 107 may
store an association between a user (represented by, e.g., a user
identifier, an employee identifier, or a phone number) and a mobile
device (represented by, e.g., an International Mobile Equipment
Identity (IMEI), an International Mobile Subscription Identifier
(IMSI), a phone number, a Universal Unique Identifier (UUID), or a
Globally Unique Identifier (GUID)). Transportation system 107 may
use this association in conjunction with data received on
deliveries to analyze data stored in the database in order to
determine, among other things, a location of the worker, an
efficiency of the worker, or a speed of the worker.
[0042] Seller portal 109, in some embodiments, may be implemented
as a computer system that enables sellers or other external
entities to electronically communicate with one or more systems in
system 100. For example, a seller may utilize a computer system
(not pictured) to upload or provide product information, order
information, contact information, or the like, for products that
the seller wishes to sell through system 100 using seller portal
109.
[0043] Shipment and order tracking system 111, in some embodiments,
may be implemented as a computer system that receives, stores, and
forwards information regarding the location of packages containing
products ordered by customers (e.g., by a user using devices
102A-102B). In some embodiments, shipment and order tracking system
111 may request or store information from web servers (not
pictured) operated by shipping companies that deliver packages
containing products ordered by customers.
[0044] In some embodiments, shipment and order tracking system 111
may request and store information from systems depicted in system
100. For example, shipment and order tracking system 111 may
request information from transportation system 107. As discussed
above, transportation system 107 may receive information from one
or more mobile devices 107A-107C (e.g., mobile phones, smart
phones, PDAs, or the like) that are associated with one or more of
a user (e.g., a delivery worker) or a vehicle (e.g., a delivery
truck). In some embodiments, shipment and order tracking system 111
may also request information from warehouse management system (WMS)
119 to determine the location of individual products inside of a
fulfillment center (e.g., fulfillment center 200). Shipment and
order tracking system 111 may request data from one or more of
transportation system 107 or WMS 119, process it, and present it to
a device (e.g., user devices 102A and 102B) upon request.
[0045] Fulfillment optimization (FO) system 113, in some
embodiments, may be implemented as a computer system that stores
information for customer orders from other systems (e.g., external
front end system 103 and/or shipment and order tracking system
111). FO system 113 may also store information describing where
particular items are held or stored. For example, certain items may
be stored only in one fulfillment center, while certain other items
may be stored in multiple fulfillment centers. In still other
embodiments, certain fulfillment centers may be designed to store
only a particular set of items (e.g., fresh produce or frozen
products). FO system 113 stores this information as well as
associated information (e.g., quantity, size, date of receipt,
expiration date, etc.).
[0046] FO system 113 may also calculate a corresponding PDD
(promised delivery date) for each product. The PDD, in some
embodiments, may be based on one or more factors. For example, FO
system 113 may calculate a PDD for a product based on a past demand
for a product (e.g., how many times that product was ordered during
a period of time), an expected demand for a product (e.g., how many
customers are forecast to order the product during an upcoming
period of time), a network-wide past demand indicating how many
products were ordered during a period of time, a network-wide
expected demand indicating how many products are expected to be
ordered during an upcoming period of time, one or more counts of
the product stored in each fulfillment center 200, which
fulfillment center stores each product, expected or current orders
for that product, or the like.
[0047] In some embodiments, FO system 113 may determine a PDD for
each product on a periodic basis (e.g., hourly) and store it in a
database for retrieval or sending to other systems (e.g., external
front end system 103, SAT system 101, shipment and order tracking
system 111). In other embodiments, FO system 113 may receive
electronic requests from one or more systems (e.g., external front
end system 103, SAT system 101, shipment and order tracking system
111) and calculate the PDD on demand.
[0048] Fulfilment messaging gateway (FMG) 115, in some embodiments,
may be implemented as a computer system that receives a request or
response in one format or protocol from one or more systems in
system 100, such as FO system 113, converts it to another format or
protocol, and forward it in the converted format or protocol to
other systems, such as WMS 119 or 3.sup.rd party fulfillment
systems 121A, 121B, or 121C, and vice versa.
[0049] Supply chain management (SCM) system 117, in some
embodiments, may be implemented as a computer system that performs
forecasting functions. For example, SCM system 117 may forecast a
level of demand for a particular product based on, for example,
based on a past demand for products, an expected demand for a
product, a network-wide past demand, a network-wide expected
demand, a count of products stored in each fulfillment center 200,
expected or current orders for each product, or the like. In
response to this forecasted level and the amount of each product
across all fulfillment centers, SCM system 117 may generate one or
more purchase orders to purchase and stock a sufficient quantity to
satisfy the forecasted demand for a particular product.
[0050] Warehouse management system (WMS) 119, in some embodiments,
may be implemented as a computer system that monitors workflow. For
example, WMS 119 may receive event data from individual devices
(e.g., devices 107A-107C or 119A-119C) indicating discrete events.
For example, WMS 119 may receive event data indicating the use of
one of these devices to scan a package. As discussed below with
respect to fulfillment center 200 and FIG. 2, during the
fulfillment process, a package identifier (e.g., a barcode or RFID
tag data) may be scanned or read by machines at particular stages
(e.g., automated or handheld barcode scanners, RFID readers,
high-speed cameras, devices such as tablet 119A, mobile device/PDA
119B, computer 119C, or the like). WMS 119 may store each event
indicating a scan or a read of a package identifier in a
corresponding database (not pictured) along with the package
identifier, a time, date, location, user identifier, or other
information, and may provide this information to other systems
(e.g., shipment and order tracking system 111).
[0051] WMS 119, in some embodiments, may store information
associating one or more devices (e.g., devices 107A-107C or
119A-119C) with one or more users associated with system 100. For
example, in some situations, a user (such as a part- or full-time
employee) may be associated with a mobile device in that the user
owns the mobile device (e.g., the mobile device is a smartphone).
In other situations, a user may be associated with a mobile device
in that the user is temporarily in custody of the mobile device
(e.g., the user checked the mobile device out at the start of the
day, may use it during the day, and may return it at the end of the
day).
[0052] WMS 119, in some embodiments, may maintain a work log for
each user associated with system 100. For example, WMS 119 may
store information associated with each employee, including any
assigned processes (e.g., unloading trucks, picking items from a
pick zone, sorting apparatus work, packing items), a user
identifier, a location (e.g., a floor or zone in a fulfillment
center 200), a number of units moved through the system by the
employee (e.g., number of items picked, number of items packed), an
identifier associated with a device (e.g., devices 119A-119C), or
the like. In some embodiments, WMS 119 may receive check-in and
check-out information from a timekeeping system, such as a
timekeeping system operated on a device 119A-119C.
[0053] 3.sup.rd party fulfillment (3PL) systems 121A-121C, in some
embodiments, represent computer systems associated with third-party
providers of logistics and products. For example, while some
products are stored in fulfillment center 200 (as discussed below
with respect to FIG. 2), other products may be stored off-site, may
be produced on demand, or may be otherwise unavailable for storage
in fulfillment center 200. 3PL systems 121A-121C may be configured
to receive orders from FO system 113 (e.g., through FMG 115) and
may provide products and/or services (e.g., delivery or
installation) to customers directly. In some embodiments, one or
more of 3PL systems 121A-121C may be part of system 100, while in
other embodiments, one or more of 3PL systems 121A-121C may be
outside of system 100 (e.g., owned or operated by a third-party
provider).
[0054] Fulfillment Center Auth system (FC Auth) 123, in some
embodiments, may be implemented as a computer system with a variety
of functions. For example, in some embodiments, FC Auth 123 may act
as a single-sign on (SSO) service for one or more other systems in
system 100. For example, FC Auth 123 may enable a user to log in
via internal front end system 105, determine that the user has
similar privileges to access resources at shipment and order
tracking system 111, and enable the user to access those privileges
without requiring a second log in process. FC Auth 123, in other
embodiments, may enable users (e.g., employees) to associate
themselves with a particular task. For example, some employees may
not have an electronic device (such as devices 119A-119C) and may
instead move from task to task, and zone to zone, within a
fulfillment center 200, during the course of a day. FC Auth 123 may
be configured to enable those employees to indicate what task they
are performing and what zone they are in at different times of
day.
[0055] Labor management system (LMS) 125, in some embodiments, may
be implemented as a computer system that stores attendance and
overtime information for employees (including full-time and
part-time employees). For example, LMS 125 may receive information
from FC Auth 123, WMS 119, devices 119A-119C, transportation system
107, and/or devices 107A-107C.
[0056] The particular configuration depicted in FIG. 1A is an
example only. For example, while FIG. 1A depicts FC Auth system 123
connected to FO system 113, not all embodiments require this
particular configuration. Indeed, in some embodiments, the systems
in system 100 may be connected to one another through one or more
public or private networks, including the Internet, an Intranet, a
WAN (Wide-Area Network), a MAN (Metropolitan-Area Network), a
wireless network compliant with the IEEE 802.11a/b/g/n Standards, a
leased line, or the like. In some embodiments, one or more of the
systems in system 100 may be implemented as one or more virtual
servers implemented at a data center, server farm, or the like.
[0057] FIG. 2 depicts a fulfillment center 200. Fulfillment center
200 is an example of a physical location that stores items for
shipping to customers when ordered. Fulfillment center (FC) 200 may
be divided into multiple zones, each of which are depicted in FIG.
2. These "zones," in some embodiments, may be thought of as virtual
divisions between different stages of a process of receiving items,
storing the items, retrieving the items, and shipping the items. So
while the "zones" are depicted in FIG. 2, other divisions of zones
are possible, and the zones in FIG. 2 may be omitted, duplicated,
or modified in some embodiments.
[0058] Inbound zone 203 represents an area of FC 200 where items
are received from sellers who wish to sell products using system
100 from FIG. 1A. For example, a seller may deliver items 202A and
202B using truck 201. Item 202A may represent a single item large
enough to occupy its own shipping pallet, while item 202B may
represent a set of items that are stacked together on the same
pallet to save space.
[0059] A worker may receive the items in inbound zone 203 and may
optionally check the items for damage and correctness using a
computer system (not pictured). For example, the worker may use a
computer system to compare the quantity of items 202A and 202B to
an ordered quantity of items. If the quantity does not match, that
worker may refuse one or more of items 202A or 202B. If the
quantity does match, the worker may move those items (using, e.g.,
a dolly, a handtruck, a forklift, or manually) to buffer zone 205.
Buffer zone 205 may be a temporary storage area for items that are
not currently needed in the picking zone, for example, because
there is a high enough quantity of that item in the picking zone to
satisfy forecasted demand. In some embodiments, forklifts 206
operate to move items around buffer zone 205 and between inbound
zone 203 and drop zone 207. If there is a need for items 202A or
202B in the picking zone (e.g., because of forecasted demand), a
forklift may move items 202A or 202B to drop zone 207.
[0060] Drop zone 207 may be an area of FC 200 that stores items
before they are moved to picking zone 209. A worker assigned to the
picking task (a "picker") may approach items 202A and 202B in the
picking zone, scan a barcode for the picking zone, and scan
barcodes associated with items 202A and 202B using a mobile device
(e.g., device 119B). The picker may then take the item to picking
zone 209 (e.g., by placing it on a cart or carrying it).
[0061] Picking zone 209 may be an area of FC 200 where items 208
are stored on storage units 210. In some embodiments, storage units
210 may comprise one or more of physical shelving, bookshelves,
boxes, totes, refrigerators, freezers, cold stores, or the like. In
some embodiments, picking zone 209 may be organized into multiple
floors. In some embodiments, workers or machines may move items
into picking zone 209 in multiple ways, including, for example, a
forklift, an elevator, a conveyor belt, a cart, a handtruck, a
dolly, an automated robot or device, or manually. For example, a
picker may place items 202A and 202B on a handtruck or cart in drop
zone 207 and walk items 202A and 202B to picking zone 209.
[0062] A picker may receive an instruction to place (or "stow") the
items in particular spots in picking zone 209, such as a particular
space on a storage unit 210. For example, a picker may scan item
202A using a mobile device (e.g., device 119B). The device may
indicate where the picker should stow item 202A, for example, using
a system that indicate an aisle, shelf, and location. The device
may then prompt the picker to scan a barcode at that location
before stowing item 202A in that location. The device may send
(e.g., via a wireless network) data to a computer system such as
WMS 119 in FIG. 1A indicating that item 202A has been stowed at the
location by the user using device 119B.
[0063] Once a user places an order, a picker may receive an
instruction on device 1196 to retrieve one or more items 208 from
storage unit 210. The picker may retrieve item 208, scan a barcode
on item 208, and place it on transport mechanism 214. While
transport mechanism 214 is represented as a slide, in some
embodiments, transport mechanism may be implemented as one or more
of a conveyor belt, an elevator, a cart, a forklift, a handtruck, a
dolly, or the like. Item 208 may then arrive at packing zone
211.
[0064] Packing zone 211 may be an area of FC 200 where items are
received from picking zone 209 and packed into boxes or bags for
eventual shipping to customers. In packing zone 211, a worker
assigned to receiving items (a "rebin worker") may receive item 208
from picking zone 209 and determine what order it corresponds to.
For example, the rebin worker may use a device, such as computer
119C, to scan a barcode on item 208. Computer 119C may indicate
visually which order item 208 is associated with. This may include,
for example, a space or "cell" on a wall 216 that corresponds to an
order. Once the order is complete (e.g., because the cell contains
all items for the order), the rebin worker may indicate to a
packing worker (or "packer") that the order is complete. The packer
may retrieve the items from the cell and place them in a box or bag
for shipping. The packer may then send the box or bag to a hub zone
213, e.g., via forklift, cart, dolly, handtruck, conveyor belt,
manually, or otherwise.
[0065] Hub zone 213 may be an area of FC 200 that receives all
boxes or bags ("packages") from packing zone 211. Workers and/or
machines in hub zone 213 may retrieve package 218 and determine
which portion of a delivery area each package is intended to go to,
and route the package to an appropriate camp zone 215. For example,
if the delivery area has two smaller sub-areas, packages may go to
one of two camp zones 215. In some embodiments, a worker or machine
may scan a package (e.g., using one of devices 119A-119C) to
determine its eventual destination. Routing the package to camp
zone 215 may comprise, for example, determining a portion of a
geographical area that the package is destined for (e.g., based on
a postal code) and determining a camp zone 215 associated with the
portion of the geographical area.
[0066] Camp zone 215, in some embodiments, may comprise one or more
buildings, one or more physical spaces, or one or more areas, where
packages are received from hub zone 213 for sorting into routes
and/or sub-routes. In some embodiments, camp zone 215 is physically
separate from FC 200 while in other embodiments camp zone 215 may
form a part of FC 200.
[0067] Workers and/or machines in camp zone 215 may determine which
route and/or sub-route a package 220 should be associated with, for
example, based on a comparison of the destination to an existing
route and/or sub-route, a calculation of workload for each route
and/or sub-route, the time of day, a shipping method, the cost to
ship the package 220, a PDD associated with the items in package
220, or the like. In some embodiments, a worker or machine may scan
a package (e.g., using one of devices 119A-119C) to determine its
eventual destination. Once package 220 is assigned to a particular
route and/or sub-route, a worker and/or machine may move package
220 to be shipped. In exemplary FIG. 2, camp zone 215 includes a
truck 222, a car 226, and delivery workers 224A and 224B. In some
embodiments, truck 222 may be driven by delivery worker 224A, where
delivery worker 224A is a full-time employee that delivers packages
for FC 200 and truck 222 is owned, leased, or operated by the same
company that owns, leases, or operates FC 200. In some embodiments,
car 226 may be driven by delivery worker 224B, where delivery
worker 224B is a "flex" or occasional worker that is delivering on
an as-needed basis (e.g., seasonally). Car 226 may be owned,
leased, or operated by delivery worker 2246.
[0068] FIG. 3 is a block diagram of an exemplary process 300
including batch creation, consistent with the disclosed
embodiments. In some embodiments, "picking" entails selecting items
from individual orders of a batch and placing them into totes.
Picking processes may be performed by a machine (e.g., a robot or
other device with appropriate apparatuses, including scanning
devices and machinery to move totes or items), a human worker, or
some combination (e.g., using machine-assisted labor).
[0069] In one embodiment, a batch includes items from multiple
orders. Each of the orders of the batch may include items in the
order represented by Stock Keeping Units (SKUs). In some
embodiments, items of each order may have been placed by users at
devices mobile device 102A or computer 102B of FIG. 1A through a
website hosted on external front end system 103 of FIG. 1A. In some
embodiments, automated scanning equipment (e.g., associated with
computer 119C) may scan a barcode associated with the SKUs for
storing information regarding the order parts for the picking
process. In yet other embodiments, the SKUs allow a worker (as
described above in FIG. 2) to read the order parts for the picking
process.
[0070] In some embodiments, aspects of process 300 take place in
picking zone 209 of FIG. 2, where a picker--workers or
machines--picks items from individual orders of a batch and places
them into totes. SAT system 101, WMS 119, or other devices depicted
in FIG. 1A may perform one or more operations in process 300, as
appropriate. For example, as discussed below, SAT system 101 may
generate instructions to pick items based on the picker's location
or other conditions, and send those instructions to a mobile device
operated by a picker.
[0071] In prior art methods, pickers were required to walk a
significant distance and take a significant amount of time to pick
up items, which delayed fulfillment of orders in the batch and
delayed assignment of items to pickers, which decreased throughput
of the system. Some prior methods would assign items along a path
to a single picker (static path). Such delay and unorganized method
in batch picking causes additional interruptions in the shipment
process in many respects. For example, delaying an order shipment
until an entire batch with distant items is picked causes delays in
the processing of multiple orders. As described below, by
calculating high density areas in the fulfillment center and
creating batches based on those areas, the present system creates
efficiency in the batch process of shipping. More specifically, the
computer-implemented system and method for artificial intelligence
batch picking optimization and communication provides efficiency by
finishing orders faster since items in an optimized batch are
closer to each other and may be picked more quickly. The present
system allows for efficiency through grouping items by a density
algorithm, getting more orders through the system faster, taking in
more orders, and cutting down wasted time walking to distant
items.
[0072] Process 300 of FIG. 3 depicts data from DB (database) 301,
batch job prediction 302, and digital map 303, each of which are
used as input to density algorithm 304.
[0073] Data from DB 301 includes data related to shipments (e.g.,
orders made by users), units (e.g., storage units), locations
(e.g., item positions), cutlines, a value MS representing batch
maximum size (e.g., a numeric value indicating the maximum number
of items to be gathered into a batch), a value T representing
shipments thresholds (e.g., a number of shipments for each zone of
FC 200), and other data.
[0074] Batch job prediction data 302 includes an exponential
smoothing five-minute prediction to increase picking speed. For
example, this prediction may be related to a time series prediction
method for univariate data that may be extended to support data
with a systematic trend or seasonal component. Batch job prediction
data 302 uses an exponential smoothing method to predict the batch
job consumed speed next period. Data 302 may also include data
relating to safety stock, that is, the number of each item that
should be in FC 200 to avoid the item going out of stock (00S).
Data 302 may also include a value N, representing the number of
batch jobs needed to fulfill of current set of orders for picking.
Digital map 303 includes A* shortest path algorithm which
calculates distance of location pairs and stores all shortest
distances in a matrix (DM) to be used directly.
[0075] Density algorithm 304, which in some embodiments is executed
by SAT system 101 (though in other embodiments may be executed by
other systems such as transportation system 107 or fulfillment
optimization system 113), receives data 301, 302, and 303. Density
algorithm 304 may include several steps, for example: step 305
(Active Zonetypes & Shipments Selection), step 306 (KNN or
K-Nearest Neighbors--identify dense area), and step 307 (gradient
descent algorithm).
[0076] In step 305 (Active Zonetypes & Shipments Selection),
SAT system 101 creates batches in less zone types which contain at
least T shipments and creating batch in active zone types by
selected shipments. In some embodiments, the threshold T is a
shipment threshold that indicates an opportunity to create a
high-quality batch based on the shipments. In some embodiments, T
may be set to 3 times the maximum batch size. Because a PP (process
path) may cover multiple zone types, a batch in that PP may cover
multiple zone types as well. Accordingly, it may be advantageous to
create a batch that covers less zone types (less batch jobs)
because less batch jobs for a batch means less picking cycle time.
In some embodiments, active zone types contain more items and have
high density.
[0077] In some embodiments, SAT system 101 may create batches in
one or more zone types (e.g., separate areas of the fulfillment
center) in order to provide additional efficiencies to the system
by making the batching process quicker (e.g., because only items
from the same zone may be batched together). SAT system 101
calculates distances between location pairs (e.g., all possible
locations for items to be stored in a single floor of the
fulfillment center) using an A* search algorithm. In some
embodiments, A* search algorithm may be scheduled and run
periodically, for example, once per day as it may be
computationally expensive. In some embodiments, A* search algorithm
may be run each time an arrangement of items in FC 200 changes
(e.g., if items are moved around). Next, SAT system 101 receives
orders (e.g., from external front end system 103) and consolidates
the items of the orders into a list of items.
[0078] In step 306 (KNN--identify dense area) SAT system may use
K-means clustering and select a first shipment. SAT system 101 may
calculate the "K" nearest neighbors (K being a static or dynamic
integer) for all unbatched items, in order to determine high
density areas of unbatched items in FC 200. In some embodiments,
determining the "K" nearest neighbors comprises choosing K items,
such as 3 items, and classifying those items as being part of a
high density area--e.g., an area in the fulfillment center with
items that are densely packed and closer to each other than
remaining items in the fulfillment center. For example, in some
embodiments, items that are nearest to each other may be determined
to be in a high density area. In some embodiments, the value K may
be chosen as input size/maximum batch size and maximum batch size
may be set by the number of slots in the rebin wall. In some
embodiments, the K-Nearest Neighbors (KNN) process in step 306 may
be used to identify high density areas. In some embodiments, high
density areas may create a high-quality batch.
[0079] In step 307 (gradient descent algorithm), SAT system 101 may
calculate the picking distance increase per item for all available
items and select the item with minimum picking distance by calling
the exchange operator described below with respect to FIG. 6. SAT
system 101 may, using the exchange operator of FIG. 6 for example,
exchange one or more "worst" items (e.g., in terms of how much the
item increases the average travel distance for picking) in the
batch for one or more "best" items which were not originally
selected if the average picking distance per item would be reduced
in the batch by picking one or more "best" items. Accordingly,
updating the number of batch jobs may be performed by subtracting
the number of batch jobs increased in this cycle of density
algorithm 304 from the number of batch jobs required (N=N-n). In
step 307, SAT system 101 gathers one or more items from a high
density area into a single batch by gradient descent algorithm 307.
In some embodiment, this includes finding the centroid of an area
(e.g., the centroid of an area defined by a set of items), then
adding a number of items that are nearest to that centroid. SAT
system 101 performs adding of items until maximum batch size is
reached. The next item to add may be chosen by determining which
item may increase the average distance (number of items in
batch/total distance traveled) least. After SAT system 101 has
determined a batch, density algorithm 304 may calculate KNN and
gather some items from a high density area into a single batch
(KNN--identify dense area 306 and Gradient descent algorithm 307)
again with the remaining unbatched items to create a new batch. In
some embodiments, such processes may be run every five minutes to
generate batches. This value is based on the PP (Process Pass)
which is the average time for pickers to pick an average batch.
[0080] In some embodiments, gradient descent algorithm 307 may be
used to create a batch with short picking distance. Specifically,
gradient descent algorithm 307 may choose a shipment to add to a
batch which increases the minimum picking distance the least.
Gradient descent algorithm 307 may repeat this process until a
maximum batch size is reached.
[0081] After using the gradient descent algorithm in step 307,
process 300 proceeds to step 308, where SAT system 101 determines
if N (the remaining number of batches needed to fulfill the current
orders) is greater than 0. If yes, process 300 returns back to step
305 (Active Zonetypes & Shipments Selection) to select more
items and create more batches. If no, the density algorithm outputs
the created batches at step 309. Outputting the created batches may
comprise, in some embodiments, sending data relating to the items
in each batch to mobile devices (e.g., 119A/119B) with instructions
to display an indication to pick the items.
[0082] In some embodiments, density algorithm 304 prevents the
batching of three times the maximum number of items per batch (the
maximum number of items per batch is varied in different PP) in
order to maintain some items for a next batch. This maximizes the
processing efficiency of SAT system 101 because there may be items
that may be batched when a new order comes in. This also maximizes
pickers' efficiency and utilization and minimizes distance
traveled. In some embodiments, the maximum number of items per
batch may be two, four, five, twenty, or other values.
[0083] FIG. 4 is a diagrammatic illustration of an exemplary
process 400 including data flow of batch creation, consistent with
the disclosed embodiments.
[0084] Process 400 depicts control server 401 for server-to-server
network connections to be connected to one or more of the systems
of FIG. 1A. In some embodiments, mobile device 102A or computer
102B of FIG. 1A may send order information (comprising one or more
desired items) through a website hosted on external front end
system 103 of FIG. 1A (e.g., as described above with regard to
FIGS. 1B-1E). In process 400, external front end system 103 may
receive orders 402 and forward them to shipment and order tracking
system 111 of FIG. 1A, which in turn may store and forward them to
field control server 403. (In some embodiments, field control
server 403 may be implemented as described above with respect to
SAT system 101.) Field control server 403 may then create batch
jobs 404 (as described and shown in FIG. 3) using density algorithm
304 of FIG. 3.
[0085] Process 400 further depicts that instructions for picking
may be sent by SAT system 101 and/or WMS 119 to a user device
(e.g., mobile device/PDA 119B of FIG. 1A) providing a list of the
items of the batch for adding/gathering in the single batch and a
location of those items in fulfillment center 405. At step 406, SAT
system 101 sends information from batch jobs 404 to a device (e.g.
mobile device/PDA 119B). For example, picking takes place in
picking zone 209 of FIG. 2, where a picker 407--workers or
machines--picks items from individual orders of a batch and places
them into totes, into boxes, onto carts, or into/onto another
movable container or vehicle. Upon completed picking, SAT system
101 may send an instruction to a mobile device (e.g. operated by
the picker) to send the picked items for rebatch in step 408.
During the rebatch step, the items in the totes may be reorganized
to be prepared for shipment. In some embodiments, picking entails
selecting items from individual orders of a batch and placing them
into totes. In some embodiments rebatch entails collecting all the
totes for one batch and reorganizing the totes by recombining the
totes 410 to have items from one order in the same tote. In some
embodiments, rebin entails categorizing the totes into the
shipment. In some embodiments, packing entails preparing and boxing
up the rebinned orders for shipment. SAT system 101 may determine
appropriate operations for picking/rebatching/rebinning, generate
instructions corresponding to the operations, and send the
instructions to a mobile device to order a picker to perform a
particular task.
[0086] FIG. 5 is a diagrammatic illustration of an exemplary
process 500 including a batch visualization tool, consistent with
the disclosed embodiments.
[0087] In process 500, SAT system 101 receives a layout drawing 501
and create location pairs (a,b) 503 with coordinate a 504 and
coordinate b 505 using computer 502. In some embodiments, layout
drawing 501 is a digital map that includes data representing
locations that items in FC 200 are stored. In some embodiments,
layout drawing 501 may be prepared by an operator. In some
examples, layout drawing 501 may be provided be in various formats
including plaintext, XML (eXtensible Markup Language), KML (Keyhole
Markup Language), GML (Geography Markup Language), or the like.
[0088] In step 503, SAT system 101 performs a coordinate
calculation via computer 502 to create coordinates a (x1, y1) and b
(x2, y2) 506. Process 500 further depicts SAT system 101 using A*
Shortest Path Algorithm via computer 502 to create distance (a,b)
507. In some embodiments, SAT system 101 calculates shortest path
of all location pairs for each floor of the fulfillment center with
A* algorithm.
[0089] In some embodiments, A* is a high efficiency shortest path
algorithm. A* may be a computer algorithm used in pathfinding and
graph traversal (the process of finding a path between multiple
points called nodes). In some embodiments, A* may have high
performance and accuracy. However, in practical travel-routing
systems, A* may be outperformed by algorithms which can pre-process
the graph to attain better performance. In some embodiments, A* may
be used to find shortest path between all location pairs. In other
embodiments, alternative methods and algorithms may be used for
shortest path searching algorithm, such as Dijkstra algorithm.
[0090] SAT system 101 stores, for the one or more items, a distance
between the item and a closest second item--all shortest distance
pairs 508. In some embodiments, SAT System 101 stores the shortest
distance pairs 508 in a file or other data storage (e.g., a
database). The shortest distance may be saved in a file instead of
being computed during batch creation. Specifically, shortest
distance pairs 508 are stored in memory to in order to be accessed
quickly. In separated files for different fulfillment centers and
floors, the files may be in raw format (binary files). SAT system
101 then provides the shortest distance pairs to density algorithm
509 (same as density algorithm 304 of FIG. 3) and to batch
visualization tool 510 which shows all items in a batch in a
digital map. The digital map includes detail information (e.g.,
distance) for a batch. In some embodiments, the digital map may be
sent with instructions for picking to a user device (e.g., mobile
device/PDA 119B of FIG. 1A) providing a list of items for gathering
in the single batch and a location of those items in the
fulfillment center.
[0091] In some embodiments, the process 500 is maintained. Whenever
the layout drawing 501 or digital map is changed, SAT system 101
(or another system) may trigger a directed acyclic graph (DAG) to
generate a new digital map. In some embodiments, the DAG may be a
collection of tasks organized to reflect relationships,
dependencies, and other properties. For example, a DAG could
comprise four tasks (A, B, C, and D); the DAG indicates the order
of operations of those tasks as well as dependencies (e.g., B must
be complete before C may run, but A may run whenever). The DAG may
be used to instantiate task clusters (or "jobs"). Such clusters may
be used for a single batch job, an interactive session with
multiple jobs, or a long-lived server continually satisfying
requests. In some embodiments, DAG task may submit Spark jobs to
calculate shortest paths in yarn cluster.
[0092] In some embodiments, SAT system 101 uses the digital map to
measure quality of a batch. In some embodiments, SAT system 101
measures the quality of the batch by how close the items in the
batch are to each other. In some embodiments, batch picking
distance may be calculated using a digital map. In some
embodiments, the shorter the picking distance per item, the batch
may be of higher quality.
[0093] FIG. 6 depicts results of a gradient descent algorithm for
walking distance optimization consistent with the disclosed
embodiments.
[0094] The gradient descent algorithm (step 307 of FIG. 3)
evaluates score candidate of items to be added to a batch. For
example, a score s.sub.s may be calculated as s.sub.d/s.sub.u.
Gradient descent algorithm 307 tries to add every item into the
batch. s.sub.u denotes how many units or items in a shipment.
s.sub.d is the calculated picking distance increased by an item.
Gradient descent algorithm 307 selects the item with minimum
s.sub.s, and adds the item into the batch. In some embodiments,
selecting the item with minimum s.sub.s includes selecting the item
that has the lowest score. In some embodiments, selecting the item
with minimum s.sub.s includes selecting the item that increases the
score the least. In some embodiments, gradient descent algorithm
307 selects the shipment (which may contain many items) that has
lowest score which means it takes lowest efforts (picking distance)
to pick all items in the shipment.
[0095] Gradient descent algorithm 307 also evaluates score of worst
shipments in batch, s.sub.w. Moreover, evaluating does not involve
the score of best items in batch, s.sub.b. Gradient descent
algorithm 307 determines if s.sub.b>s.sub.w. If so, then
gradient descent algorithm 307 exchange the two shipments and the
total picking distance may decrease, which increases the quality of
the batch. In some embodiments, the exchange operator of FIG. 6 may
be used after reaching a maximum size shipment for a batch. In some
embodiments, the exchange operator of FIG. 6 may be used to find
the best shipment outside of the batch to exchange with the worst
shipment in the batch to reduce the overall picking distance of the
batch. For instance, if removing the worst shipment would reduce
the overall picking distance of a batch by 10 meters and adding the
best shipment (which is not in the batch) would increase the
overall picking distance 5 meters, exchanging these two shipments
would reduce the overall picking distance by 5 meters.
[0096] The score of a shipment (which may contain many items) is
the average picking distance costed for picking all items in that
shipment. If the score is low, it means that it costs shorter
picking distance to pick all the items, otherwise, it would cost
longer picking distance.
[0097] Maps 600 and 650 in FIG. 6 show items in various locations
of FC 200. Circles represent items currently in a batch. Triangles
and squares represent items that are candidates to replace items in
the batch or be added to the batch. A high score for a candidate
item may indicate it is close in distance to the items in a
batch.
[0098] In map 600, the score of the square item is b.sub.s and the
score of the triangle item is g.sub.s. Since the triangle item is
closer in distance to items in the batch than the square item, the
score that item is higher (g.sub.s>b.sub.s), and thus gradient
descent algorithm 307 may select the triangle item to be placed
into the batch instead of the square item.
[0099] In system 650 exemplifying an exchange operator as disclosed
above with respect to FIG. 3, item B in the batch is the worst item
in the batch because of its proximity to the other items in the
batch and its score is B.sub.w. Furthermore, item A is the best
item among the triangle candidates to replace items in the batch
(circles) because it is closer to the items in the batch than the
other candidate items and its score is A.sub.b. In some
embodiments, if A.sub.b>B.sub.w, gradient descent algorithm 307
may remove item B from the batch and replace it with item A because
item A has a higher score and is closer than item B to the items in
the batch.
[0100] FIG. 7 is a block diagram of an exemplary process for batch
optimization. Process 700 may be performed by processor of, for
example, SAT system 101, which executes instructions encoded on a
computer-readable medium storage device. It is to be understood,
however, that one or more steps of process 700 may be implemented
by other components of system 100 (shown or not shown).
[0101] At step 710, system 100 may receive a plurality of orders,
each order comprising one or more items for picking. In some
embodiments, items of each order may have been placed by users at
devices mobile device 102A or computer 102B of FIG. 1A through a
website hosted on external front end system 103 of FIG. 1A.
[0102] Additionally, SAT system 101 may consolidate the one or more
items in orders into a list of items. In some embodiments, the list
of items may be stored in a database or in memory as discussed
above with respect to data from DB 301 of FIG. 3. At step 720, SAT
system 101 may calculate one or more high density areas in a
fulfillment center by calculating distances between one or more
location pairs of items from the list of items using a search
algorithm, and calculating nearest neighboring items for all the
items as discussed above with respect to density algorithm 304.
Density algorithm 304 calculate KNN (KNN--identify dense area 306
of FIG. 3) for all unbatched items in order to determine high
density areas of the fulfillment center floor. In some embodiments,
density algorithm 304 of FIG. 3 chooses K items and segments those
into a high density area. In some embodiments, the value K may be
chosen as input size/maximum batch size and maximum batch size may
be set by the number of slots in the rebin wall.
[0103] At step 730, as discussed above with respect to step 303 of
FIG. 3 and generally discussed with respect to FIG. 5, SAT system
101 may store all shortest distances of all the items in
memory.
[0104] At step 740, as discussed above with respect to steps 305
and 306, SAT system 101 may create one or more batches based on the
calculated one or more high density areas.
[0105] At step 750, as discussed above with respect to step 307,
SAT system 101 may add items from the one or more high density
areas into a single batch using a gradient descent algorithm, the
adding based on an item increasing an average distance least among
pickable items.
[0106] At step 760, SAT system 101 may provide a list of items for
adding in the single batch and a location on a user device for
display. SAT system 101 of FIG. 1A may send a message to the
worker's PDA (e.g., mobile device/PDA 119B of FIG. 1A) providing a
list of items for gathering in the single batch and a location of
those items. Display 400 (corresponding to tablet 119A, mobile
device/PDA 1196, computer 119C of FIG. 1A) includes a user
interface presented to workers. As a result, SAT system 101 may
improve the process of shipping through batch optimization by
finishing orders faster because items reflected on the worker's PDA
(e.g., mobile device/PDA 119B of FIG. 1A) are closer and may be
picked more quickly, so the worker may move to the next batch
faster.
[0107] While the present disclosure has been shown and described
with reference to particular embodiments thereof, it may be
understood that the present disclosure may be practiced, without
modification, in other environments. The foregoing description has
been presented for purposes of illustration. It is not exhaustive
and is not limited to the precise forms or embodiments disclosed.
Modifications and adaptations may be apparent to those skilled in
the art from consideration of the specification and practice of the
disclosed embodiments. Additionally, although aspects of the
disclosed embodiments are described as being stored in memory, one
skilled in the art may appreciate that these aspects may also be
stored on other types of computer readable media, such as secondary
storage devices, for example, hard disks or CD ROM, or other forms
of RAM or ROM, USB media, DVD, Blu-ray, or other optical drive
media.
[0108] Computer programs based on the written description and
disclosed methods are within the skill of an experienced developer.
Various programs or program modules may be created using any of the
techniques known to one skilled in the art or may be designed in
connection with existing software. For example, program sections or
program modules may be designed in or by means of .Net Framework,
.Net Compact Framework (and related languages, such as Visual
Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX
combinations, XML, or HTML with included Java applets.
[0109] Moreover, while illustrative embodiments have been described
herein, the scope of any and all embodiments having equivalent
elements, modifications, omissions, combinations (e.g., of aspects
across various embodiments), adaptations and/or alterations would
be appreciated by those skilled in the art based on the present
disclosure. The limitations in the claims are to be interpreted
broadly based on the language employed in the claims and not
limited to examples described in the present specification or
during the prosecution of the application. The examples are to be
construed as non-exclusive. Furthermore, the steps of the disclosed
methods may be modified in any manner, including by reordering
steps and/or inserting or deleting steps. It is intended,
therefore, that the specification and examples be considered as
illustrative only, with a true scope and spirit being indicated by
the following claims and their full scope of equivalents.
* * * * *