BACKGROUND
[0001] Online demand arises from orders placed by users, while fulfillment is typically
performed through one or more facilities. The orders are dispatched to destinations or are picked
up from the one or more facilities. Picking involves retrieval of items from their storage locations
(such as aisles in a facility) to satisfy orders.
BRIEF DESCRIPTION OF DRAWINGS
[0002] To assist those of skill in the art in making and using a location-based identification
system and associated methods, reference is made to the accompanying figures. The
accompanying figures, which are incorporated in and constitute a part of this specification,
illustrate one or more embodiments of the present disclosure and, together with the description,
help to explain the present disclosure. Illustrative embodiments are shown by way of example in
the accompanying drawings and should not be considered as limiting. In the figures:
[0003] FIG. 1 illustrates an exemplary computer network for a fulfillment design and
optimization engine, in accordance with an exemplary embodiment;
[0004] FIG. 2A-2B illustrates user interfaces in the form of dashboards for entering data into the
fulfillment design and optimization engine that can be used to imitate a picking process, in
accordance with an exemplary embodiment;
[0005] FIG. 3 illustrates a user interface in the form of a dashboard for creating a scenario, in
accordance with an exemplary embodiment;
[0006] FIG. 4A-4B illustrates user interfaces in the form of dashboards for running queries on
scenarios, in accordance with an exemplary embodiment;
[0007] FIG. 5 illustrates a user interface in the form of a dashboard for comparing results, in
accordance with an exemplary embodiment;
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[0008] FIG. 6 is a is a block diagram of an exemplary computing device suitable for use in an
exemplary embodiment; and
[0009] FIG. 7 illustrates results of improvements made by the fulfillment design and
optimization engine.
DETAILED DESCRIPTION
[0010] Described in detail herein are methods and systems for fulfilment design and
optimization. The system includes a fulfilment design and optimization engine that includes a
simulator module. The simulator module includes a set of controls designed for imitating a
picking process and comparing and contrasting the current picking algorithm with various
enhanced models and algorithms. These enhanced models and algorithms are used to observe the
outcomes of different assumptions and actions and enables the comparison of the enhanced
models and algorithms against business key performance indicators (KPIs).
[0011] Picking time is the time the picker takes to complete a trip on which items are collected.
A trip may include, for example: a travel time a picker spends travelling from a service station to
a first pick location, between the pick locations, and from a last pick location to the service
station; a search time required for an identification of an item; a retrieval time needed for moving
items from a corresponding article location onto a tote; a setup time consumed by set-up tasks at
a beginning and an end of each tour, including collection of trolleys/carts, setting up of devices,
printers, and discussions with managers. The picker is guided through the system in such a way
that he automatically reaches a next pick-up point and long idle times are avoided. The picker is
guided by a pick list of order lines, the quantity of items, and the respective storage location.
The order lines are sorted into a sequence according to which order the picker is meant to collect
the items. The fulfilment design and optimization engine reduces travel time by creating pick
lists that include one or more orders with maximum pick density, a sequence in which items of a
given picking order are to be picked, and an identification of the corresponding (shortest) tour for
the order picker which connects the respective item locations among each other and with the
service stations. Overlapping items will significantly reduce search and retrieval time as well.
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[0012] Inefficiency of order picking may result in unsatisfactory customer service (e.g.,
inaccurate items, longer delivery times, etc.) and high costs (e.g., labor, equipment, etc.). In
general, of all warehouse operations, order picking is the most cost intensive operations and
contributes to between 50%-65% of total warehousing costs. Therefore, efficient order picking
policies can lead to great reductions in store management costs. The fulfillment design and
optimization engine evaluates fulfillment costs. The engine further improves the planning
process for long-range decisions such as zoning policies, capacities of backrooms, tote sizes, and
bagging decisions. The engine further improves strategic decisions on service pricing and
underlying fulfilments costs. Improving the order fulfillment process is a key to ensure that
more items are processed per hour per picker, thereby improving the capacity of online orders
fulfilled in a day and driving operational costs down. The engine maximizes items picked per
hour and lowers pick walks, thus ensuring better on time store dispatches.
[0013] FIG. 1 illustrates an exemplary network 100 for a fulfillment design and optimization
system 100. The system 100 includes a fulfillment design and optimization engine 102. The
fulfillment design and optimization engine 102 includes a simulator module 104 and
communicates with one or more user devices 106 for entering pick costs and creating and
running scenarios, as described below. In some embodiments, the fulfillment design and
optimization engine 102 is connected to a global integrated fulfillment system (GIF) 108 that
provides the fulfillment design and optimization engine 102 with orders, as described below.
[0014] FIG. 2A illustrates a user interface in the form of a dashboard 200A for entering pick
costs into the fulfillment design and optimization engine 102. The pick costs is used in the
process of imitating a picking process in a facility, according to an exemplary embodiment. The
dashboard 200A is displayed on the user device 106 and is communicatively coupled to the
fulfillment design and optimization engine 102. The pick costs is associated with pick related
costs that include, but are not limited to, walking costs 202, retrieval cost 204, consolidation cost
206, and consolidation space cost 208.
[0015] The walking costs 202 include one or more of a speed entered in meters per second,
sequence separation entered in meters, and a total retrieval penalty entered in minutes. The
retrieval costs 204 include one or more of a retrieval time in minutes, a nil1 pick time entered in
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minutes, a seek time entered in seconds, and a scan cost entered in seconds. The consolidation
costs 206 include one or more of a temperature band entered in minutes, a 2-way split order
entered in minutes, a 4-way split order entered in minutes, a seek time entered in seconds, a 3-
way split order entered in minutes, and a HSC split order entered in minutes. The consolidation
space cost 208 include one or more of a cost per tote in consolidation and a number of totes in
consolidation.
[0016] FIG. 2B illustrates another user interface in the form of a dashboard 200B for entering
pick costs into the fulfillment design and optimization engine 102 for imitating the picking
process in a facility, according to an exemplary embodiment. The dashboard 200B is displayed
on the user device 106 and is communicatively coupled to the fulfillment design and
optimization engine 102. In dashboard 200B, the user selects a cost profile 210 from a drop
down menu. The user sets a cost value 212 for each parameter for the selected cost profile by
clicking on the value and then entering a new value. In one embodiment, each cost profile 210
includes default cost values for each parameter.
[0017] FIG. 3 illustrates a user interface in the form of a dashboard 300 for creating a scenario,
according to an exemplary embodiment. The dashboard 300 is displayed on the user device 106
and is communicatively coupled to the fulfillment design and optimization engine 102. To
create a scenario, the user enters parameters regarding various aspects on which the user wants to
run the scenario simulation on a facility or a set of facilities (e.g., stores). A scenario is a
combination of at least a zoning policy, a routing policy, and a base algorithm, as described
below. In some embodiments, additional information such as batch sizes and/or sub and nil1 pick
items are entered.
[0018] To create a new scenario, the user selects to add a new scenario from the main dashboard
page shown in FIG. 4. The user enters a descriptive scenario name. In an exemplary
embodiment, the user selects a zoning policy, a base algorithm, volumetrics, a routing policy,
and a maximum due time. The user further enters a batch size based on a commodity for which
the user wants to run the simulation. The user selects the routing policy as sequencing or XYZ.
In some embodiments, the user selects a drop zone, such as a backroom or a service station,
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where the picker drops off the totes. In additional embodiments, the user may select bagging as
true or false and/or enter a split size for split algorithms to split orders within commodities.
[0019] The base algorithm involves intelligent order batching. Multiple orders known before
the fulfilment process starts are slotted deliveries throughout the day, and thus these orders can
be batched intelligently as pick lists are generated for the picker. Order batching involves
distributing orders across several batches and then sortinglrouting the picker efficiently across all
locations to pick these orders. The base algorithm reduces the overall time to pick all orders on
the floor within due times of the truck departures.
[0020] The base algorithm includes smart order batching, split order batching, and volumetrics
order batching, as described below. The engine 102 uses the selected algorithm during the
scenario simulation.
The Base Algorithm : Smart Order Batchinq
[0021] Problem definition: Several orders are received for in-store fulfillment on a daily basis
and slotted to be delivered throughout the day. Each order consists of a number of order lines.
Several orders are known before batching decisions are made so that the requirements of
different orders can be aggregated. Under such assumptions, a picker works with a batch of
various orders simultaneously sorting items into the right orders. Thus, no consolidation is
needed at the end of the route. Moreover, since several orders are picked simultaneously,
efficient routes can be devised and the travel times can be reduced and pick rates improved.
[0022] Order batching deals with the problem of distributing orders (of a due time and a
commodity) among batches (each batch with a total load below the load-capacity of a picker)
and determining a batch-picking route for each batch in order to minimize the total pick time to
pick all items of all orders. Order batching involves batching of orders and route sequencing to
be used by each picker to retrieve the items. Smart Order Batching resolves how a customer
orders for a due time and within a commodity be grouped (batched) into order batches to be
picked, and for each order batch, a sequence the storage locations of the items are visited (along
with the route to-from the Service Stations) so that the intra-item distance is minimized.
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[0023] Routing and Sequencing Strategy: Given a set of items to be collected and the respective
locations where they are stored, a sequence is determined according to which these locations
should be visited such that the length of the corresponding picker trip is minimized. Optimal
routes are often complicated and difficult to memorize and the pickers seem to prefer routes
based on simple routing strategies. The application of routing strategies, e.g. the s-shape, the
return, the midpoint and the largest gap strategy, can be considered as a heuristic approach.
[0024] In s-shape heuristic, the order picker entirely traverses every picking aisle containing at
least one item to be picked. When following the return heuristic, the order picker enters and
leaves every picking aisle to be visited from the front cross aisle. For the midpoint heuristic, the
warehouse is divided equally into two areas, the front section and the rear section. All items
located in the front section are accessed from the front cross aisle, while articles in the rear
section are reached from the rear cross aisle. For the largest gap heuristic, in each aisle to be
visited the two sections are determined by the largest distance between two adjacent requested
items or between a requested items and the adjacent cross aisle.
[0025] Order Batching: Given the items storage locations, the routing strategy to be used, and
the capacity of the toteltrolley, how can the set of orders be grouped into batches such that the
sum of the total lengths of all trips required to collect the requested items is minimized?
Definition of a batch is grouping of orders within a certain due time and commodity in such a
way that all the items are collected on trips to the store floor. Batches are constrained by the
capacity of toteltrolley and the fact that items from different orders cannot be carried in the same
tote. Batching is impacted by the sequence according to which the locations of the items are
visited by the pickers to find out the inter item distances and the to-from distance from the
Service Station. Given a batch of orders, a trip is the travel of a picker to pick items of the orders
of the batch starting from the Service Station to the first pick location, between the pick locations
and from the last pick location to the Service Station.
[0026] Definition of a Sequence: Sequences (1,2,3,4,5.. .) corresponds to A4-2, A4- I , A5-7,
A4-3 where A4, A5 are the Aisles and -2,-1,-7 are the Sections. Sequences are natural walking
paths and follow the S routing strategy. If there are picks for locations A4-1-11, A4-2-15,
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A4-2-18, A4-3-1, A5-7-4 then the sequence of the picks is A4-2-15, A4-2-18, A4-1-1 I, A5-7-4,
A4-3-1.
[0027] Proposed batching strategy: The engine 102 interfaces with the global integrated
fulfillment system (GIF) 108. Orders are released in two waves (for example, orders aggregated
until 4 am will drop at 4 am and orders aggregated from 4 am to 10 am will drop at 10 am). Each
order will have a truck load and a due time. Within due time orders are divided by various
commodity types and subsequently interfaced to engine 102. GIF 108 also passes the fixed
number of orders per batch (pick list). This is computed based on rule of thumb for each
commodity type by GIF 108.
[0028] Assumptions & Considerations: P is the number orders per batch as provided by GIF 108
and fixed per commodity. All items of a customer order must be a part of the same batch. All
items of P orders are picked in one trip. However, due to lack of volume information there may
be batches where the picker has to walk multiple times to the service stations or may have empty
totes at the end of the trip. Forward Walk (Service Station to I st location) & Last Walk (Last
item to Service Station) is non-significant and is 0. Inter Item walk is the most significant
component of the walk. Seek time is constant for every 1st item but is 0 for overlapping items.
Retrieval time is non-significant and is 0. Intra Item Walk is calculated by subtracting sequence
numbers; e.g. A and B are at 1,7; then the distance between them is 6. The assumption is that
each subsequent sequence corresponds to 1 unit. Customer blocking time is not considered.
[0029] Objective Function: Engine 102 aims to minimize the total time required for the pick
walk, while maximizing the total number of items picked. In order to facilitate this, engine 102
create N batches of P orders each (per commodity type) taking into consideration the due time of
the order while batching them. The batching of the orders are optimized as follows. The engine
102 begins with one order as a single batch. Then for each other customer order, the engine 102
uses proximity to determine whether it is favorable to pick other orders separately or to add them
into one of the already existing batches while not violating the trolleylcart capacity. The output
of this algorithm will be batch orders with maximum overlapping of items thereby increasing the
density of pick-up points until the capacity of the toteltrolley is exhausted. The engine 102 uses
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metaheuristic methods to batch orders or work on subset of orders and use greedylother methods
to optimize the objective function within orders.
[0030] Total Trip Time for a batch of P orders: Trip 1 : inter item walk for the batch 1 + seek
time for batch 1. Trip 2: inter item walk for the batch 2 + seek time for batch 2. Trip 3: inter
item walk for the batch 3 + seek time for batch 3, and so on.
[0031] Sample: Filtered by due time - load - zone (commodity) - batch within zone minimizing
inter-item distancelmax pick density. Batch 1 : Complete Orders 1, 2, 3 with minimum inter-item
distance and maximum pick density. Pick List 1 : Sequence in which orders 1,2,3 to be picked.
Batch 2: Complete Orders 4, 5, 6 with minimum inter-item distance and maximum pick density.
Pick List 2: Sequence in which orders 4,5,6 to be picked
[0032] Due time integrity: Orders of loads with the same due time are serviced together to
ensure picking is complete on time. This also provides highly dense picking zones.
The Base Algorithm : Split Order Batchinq
[0033] Problem Definition: Splitting of orders is allowed, therefore, the items required by an
order may be collected on different trips. This is because items of an order may be scattered
across the floor and the inter item pick walk gets impacted if the same picker must walk across
the entire floor to pick up items. Other reasons for partial orders include (I) to batch overlapping
orders that will not be otherwise batched if completion of each one of them is not possible due to
toteltrolley capacity, and (2) partial order assignment to batches with space to spare.
[0034] The batching problem here is modified by allowing portions of an order (placed closer to
other batches) be made a part of different trips. Orders can be split and added to different
existing batches, although this will increase the number of totes for the overall order. Hence, the
number portions should be limited to 2 or 3 (should be a variable). General merchandize and
ambient are the busiest and the biggest zones and requires splitting of orders within their aisles.
For splitting orders, the engine 102 can dynamically create further subzones within these
commodities to ensure that batches are formed within these sub-zones.
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(00351 Proposed batching strategy: The engine 102 interfaces with the GIF 108. Orders are
released in two waves (for example, orders aggregated till 4 am are dropped at 4 am and orders
aggregated from 4 am to 10 am are dropped at 10 am). Each order will have a truck load and a
due time. Within the due time, orders are divided by various commodity types and subsequently
interfaced to the engine 102.
[0036] Assumptions & Considerations: Batch size is less than or equal to toteltrolley capacity.
Variable number of orders per batch. Items of an order can be a part of the different batches.
Forward walk (Service Station to lSt location) & last walk (last item to service station) is
significant and is a constant. Inter Item walk is a significant component of the walk. Seek time is
constant for every lSt item but is 0 for overlapping items. Retrieval time is non-significant and is
0. Tntra item walk is calculated by subtracting sequence numbers; e.g. A and B are at 1,7, then
the distance between them is 6. The assumption is that each subsequent sequence corresponds to
I unit. Blocking time is not considered
[0037] Objective Function: Minimize Total Trip Time. Create N batches of variable orders each
(based on commodity type) for a due time and commodity type such that the total trip time is
globally across all such trips. Orders can be split across multiple batches. The algorithm begin
with one order as a single batch then for each other customer order use proximity to determine
whether it is favorable to pick other orders separately or to add them into one of the already
existing batches not violating the trolley capacity. The output of this algorithm is batch orders
with maximum overlapping of items thereby increasing the density of pick-up points until the
capacity of the toteltrolley is exhausted.
[0038] Total Trip Time for a batch of P orders: Trip I: Forward Walk + Inter Item Walk for the
batch i + Seek time for batch 1 + Last Walk. Trip 2: Forward Walk + Inter Item Walk for the
batch 2 + Seek time for batch 2 + Last Walk. Trip 3: Forward Walk + Inter Item Walk for the
batch 3 + Seek time for batch 3 + Last Walk.
[0039] Sample: Zones created everyday based on order profiles. Filtered by due time - zone
(commodity) - batch within zone minimizing inter-item distancelmax pick density. Batch 1 :
Partial Orders 1, 2 with minimum inter-item distance and walk to-from the service station. Batch
2: Partial Orders 1, 2 with minimum inter-item distance and walk to-from the service station
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[0040] Super overlaps: Highly overlapping large orders (spread across a large number of aisles)
that are picked by two or more pickers in separate trips rather than having one picker take
complete orders. Batch 1: Since Order 1,2, are spread out, creating sub zones within the
commodity creates high density areas. Partial Order Batching of 1,2 in Sub Zone 1. Batch 2:
Partial Order Batching of 1, 2 in Sub Zone 2.
[0041] TrolleyICart utilization: Taking more items in the trolley essentially translates to avoiding
more trips to pick those items. Any remaining totes of trolleys now can bring back not-so
"overlapping" orders that have been left behind and needed a new trip. Batch 1: Since Order 1,2,
are spread out, creating sub zones within the commodity creates high density areas. Partial Order
Batching of 1,2 in Sub Zone I . Batch 2: Partial Order Batching of I, 2,4 in Sub Zone 2.
[0042] Pick density: Partial orders providing good pick density and must be picked along with
other orders. Batch 1: Since Order 1,2, are spread out, creating sub zones within the commodity
creates high density areas. Partial Order Batching of 1,2 in Sub Zone 1. Batch 2: Partial Order
Batching of 1,2 in Sub Zone.
The Base Algorithm : Volumetric Order Batching
[0043] Problem definition: This algorithm minimizes the total trip walk, as both inter-item
walks and walks to-from the service station are significant. The earlier approach of fixed P
orders per batch may result in batches where the picker may have to walk multiple times to the
service stations or may have empty totes at the end of the trip. Information on the volume
information of the items along with the capacity requirements of the toteltrolley enables the
creation of batches with a variable number of orders per batch such that the total trip walk is
minimized.
[0044] Volumetric order batching can be implemented in two ways, Split and No Split. In No
Split, the algorithm moves complete orders into different pick walks to see what combinations
minimize the pick walk. In Split version, the algorithm creates full totes of orders and move totes
around into different pick walks to see what combinations minimize pick walks (this is similar to
Split Order Batching). The algorithm should include load capacity constraints to generate split
orders or the maximum split limits
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[0045] Proposed batching strategy: The engine 102 interfaces with the GIF 108. Orders are
released in two waves (orders aggregated till 4 am are dropped at 4 am and orders aggregated
from 4 am to 10 am are dropped at 10 am). Each order will have a truck load and a due time.
Within the due time orders are divided by various commodity types and subsequently interfaced
to the engine 102.
[0046] Assumptions & Considerations: A batch size is less than or equal to toteltrolley capacity.
Variable number of orders per batch. All items of a customer order must be a part of the same
batch. All items of P orders are picked in one trip. Forward walk (service Station to 1st location)
& last walk (Last item to service station) is significant and is a constant. Inter item walk is a
significant component of the walk. Seek time is constant for every lSt item but is 0 for
overlapping items. Retrieval time is non-significant and is 0. Intra item walk is calculated by
subtracting sequence numbers; e.g. A and B are at 1,7; then the distance between them is 6. The
assumption is that each subsequent sequence corresponds to 1 unit. Customer blocking time is
not considered
[0047] Objective Function: Minimize total trip time. Create N batches of variable orders each
(based on commodity type) for a due time and commodity type such that the total trip time is
globally across all such trips. The algorithm begins with one order as a single batch then for each
other order use proximity to determine whether it is favorable to pick other orders separately or
to add them into one of the already existing batches not violating the trolley capacity. The output
of this algorithm is batch orders with maximum overlapping of items, thereby increasing the
density of pick-up points until the capacity of the toteltrolley is exhausted.
[0048] Total Trip Time for a batch of P orders: Trip I: Forward Walk + Inter Item Walk for the
batch 1 + Seek time for batch 1 + Last Walk. Trip 2: Forward Walk + Inter Item Walk for the
batch 2 + Seek time for batch 2 + Last Walk. Trip 3: Forward Walk + Inter Item Walk for the
batch 3 + Seek time for batch 3 + Last Walk.
[0049] Sample: Filtered by due time - zone (commodity) - Batch within zone minimizing
inter-item distancelmax pick density. Batch 1: Complete Orders 1, 2 with minimum inter-item
distance and walk to-from the service station. Adding order 3 would violate the capacity
constraint and result in additional walk to-from the service station. Batch 2: Complete Orders 3,
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4, 5, 6 with minimum inter-item distance and walk to-from the service station as they fill the 8
totes and require only one trip to the service station
[0050] No Split: Overloaded overlapping batches: Overlapping batches of orders may require
multiple walks back and forth to the service stations due to capacity constraints to deposit and
pick up totes, increasing the overall pick walk. Batch I: Even though Order 1, 2 overlap but due
to larger size of items of Order 2 (that in turn increases walks to-from the service stations),
Complete Orders 1 , 3 are batched together. Batch 2: Complete Orders 2, 4, 5 fill the 8 totes and
require only one trip to the service station.
[0051] No Split: Underloaded overlapping batches: Overlapping batches of orders may have
items that don't requires all 8 totes, this is where additional orders can be clubbed to the trip that
may not be so overlapping but reduces the overall pick walk. Batch 1: All orders will fit into 8
totes. Batched Orders 1,2,3,4 due to smaller item sizes ensuring that any additional pick walks
would increase the overall pick walk. Batch 2: NA.
[0052] Split volumetric: For Split volumetric, orders will be split into full totes across multiple
pick lists to ensure minimum pick walk. This is similar to Split Order Batching with Volumetric
constraints.
[0053] Use of Volumetric is a yes or a no answer. However, details on volume are displayed as
a pop up once Volumetric is selected.
[0054] The engine 102 also uses a zoning policy during the simulation. The zoning policy
includes re-zoning and XYZ co-ordinates, as described below.
Zoning Policv : Re-zoning
[0055] Problem definition: The engine 102 redefines the commodity based zoning based on
temperature bands (ambient, chill and frozen), rather than the existing merchandize based zones.
General merchandize and ambient are the busiest and the biggest zones and require splitting of
orders within them.
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[0056] For splitting orders, the engine 102 creates dynamic sub-zones within these temperature
bands every day to ensure that batches are formed within these sub-zones. Sub-zones are based
on aisle separations and inter aisle distances, rather than merchandize based zones. The subzones
are picking based to ensure faster high density picking. Re-zoning of bigger commodities
such as ambient enables for more picking friendly zones. Re-zoning makes inter-item walks as
insignificant as possible without breaking the order into multiple portions.
Zoning Policy : XYZ Co-ordinates
[0057] Problem definition: XYZ coordinates are fundamental to determine accurate order
batching and optimum travel path. Store mapping will consists of XY grid coordinates for
locations and intersections. The Z coordinate correspond to a height at locations and
intersections. Locations are origins and destination of walks, like rack, shelving and block
stacks, and pick-up and deposit stations or areas like receiving and shipping or staging areas.
Intersections are nodes on a travel network that define the possible paths through the store, such
as a wall or a dead end, ends of aisles, tunnels, entries, exits, corners, and other changes in
direction.
[0058] XYZ Co-ordinates offer precise distance and locations. XYZ co-ordinates will provide
better estimates of distances and pick walks.
[0059] The user selects a routing policy to find efficiencies in final routing of the pick walk. The
user selects the routing policy as sequencing or XYZ Co-ordinates.
[0060] FIG. 4A illustrates a user interface in the form of a dashboard 400A for building and
running queries one on one or more stores based on scenarios, according to an exemplary
embodiment. Each query uses the pick costs entered using dashboard 200 and the one or more
scenarios created using dashboard 300. In the example embodiment, two scenarios are selected
in table 402 listing a plurality of scenarios. A scenario includes a zoning policy, a routing policy,
a base algorithm, and other parameters selected in FIG. 3.
[0061] A user is able to build a query 404 for the dashboard. The user is able to search, edit,
delete, and add a scenario. To build a query, a user select a market, a store, a commodity, a
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product (possibly all), a beginning date and an end date, and a due time. The user selects one or
more scenarios and runs the query. The engine 102 runs the scenario (including the scenario
input and the pick costs described above) on the store information entered when building the
query.
[0062] FIG. 4B illustrates another user interface in the form of a dashboard 400B for building
and running queries on one or more stores based on the work costs entered using dashboard 200
and the scenarios created using dashboard 300. A user is able to build a query 404 for the
dashboard and select scenarios from a table 402 listing a plurality of scenarios.
[0063] A user is able to build a query 404 for the dashboard. The user is able to search, edit,
delete, and add a scenario. To build a query, a user select a market, a store, a commodity, a
product (All), a from date, a duration (number of days for which the simulator is to run starting
with the from date, a load start, a load end, and a cost profile. The user select up to scenarios and
execute them.
[0064] The dashboards 400A and 400B are displayed on the user device 106 and are
communicatively coupled to the fulfillment design and optimization engine 102.
[0065] FIG. 5 illustrates a user interface in the form of a dashboard 500 for comparing query
results for executed scenario simulations, according to an exemplary embodiment. The
dashboard 500 is displayed on the user device 106 and is communicatively coupled to the
fulfillment design and optimization engine 102. The dashboard 500 runs several scenario
simulations for the same store or set of stores and other parameters entered during the query
build shown in FIG. 4A and 4B. Each scenario simulation provides one or more of pick rate,
distance per item, total distance, walk time, retrieval time, seek time, consolidation time, total
time, items per tote, and total totes for the selected store or set of selected stores.
[0066] Based on the scenario simulations, a best scenario 502 is determined. In an exemplary
embodiment, the best scenario is determined based on the algorithm that provides that best pick
rates. However, in alternative embodiment, the best scenario may be based on the best algorithm
given on cost, distance, time, etc.
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[0067] In addition, graphs 504 compare the selected scenarios, for example, a comparison of the
pick rate, total time, total distance and number of picks lists of the selected scenarios. A table
506 lists the plurality of scenarios and for each scenario summarizes the details of the algorithm
including one or more of a rank of an algorithm, a name of the algorithm, a number of orders
processed, a number of items, a pick rate, a distance per item, a total distance, a total pick time, a
walk time, a retrieval time, a seek time, a consolidation time, a number of items per tote, and a
total totes produced. The table 506 enables a comparison of the different values for each
algorithm. In some embodiments, a pick list button is included enabling the user to download
the pick list for each scenario selected. '
[0068] FIG. 6 is a block diagram of an example computing device 600 that can be used to
perform one or more steps provided by exemplary embodiments. In an exemplary embodiment,
computing device 600 is a server (e.g. server 120 as shown in FIG. 1) and/or a user device (e.g.
user device 110 shown in FIG. 1). Computing device 600 includes one or more non-transitory
computer-readable media for storing one or more computer-executable instructions or software
for implementing exemplary embodiments such as the prioritization module described herein.
The non-transitory computer-readable media can include, but are not limited to, one or more
types of hardware memory, non-transitory tangible media (for example, one or more magnetic
storage disks, one or more optical disks, one or more USB flash drives), and the like. For
example, a memory 606 included in computing device 600 can store computer-readable and
computer-executable instructions or software for implementing exemplary embodiments such as
the prioritization module described herein. Computing device 600 also includes a processor 602
and an associated core 604, and optionally, one or more additional processor(s) 602' and
associated core(s) 604' (for example, in the case of computer systems having multiple
processors/cores), for executing computer-readable and computer-executable instructions or
software stored in memory 606 and other programs for controlling system hardware. Processor
602 and processor(s) 602' can each be a single core processor or multiple core (604 and 604')
processor.
[0069] Computing device 600 may include a browser application 615 and a browser cache 617.
As described above, browser application 615 can enable a customer to select files and/or file
links, and receive a file.
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Attorney Docket No.: 127955-03640 (4090IN011)
[0070] Virtualization can be employed in computing device 600 so that infrastructure and
resources in the computing device can be shared dynamically. A virtual machine 614 can be
provided to handle a process running on multiple processors so that the process appears to be
using only one computing resource rather than multiple computing resources. Multiple virtual
machines can also be used with one processor.
[0071] Memory 606 can include a computer system memory or random access memory, such as
DRAM, SRAM, ED0 RAM, and the like. Memory 606 can include other types of memory as
well, or combinations thereof. In some embodiments, a customer can interact with computing
device 600 through a visual display device 618, such as a touch screen display or computer
monitor, which can display one or more customer interfaces 619 that can be provided in
accordance with exemplary embodiments. Visual display device 618 may also display other
aspects, elements and/or information or data associated with exemplary embodiments.
Computing device 600 may include other 110 devices for receiving input from a customer, for
example, a keyboard or any suitable multi-point touch interface 608, a pointing device 61 0 (e.g.,
a pen, stylus, mouse, or trackpad). The keyboard 608 and pointing device 61 0 may be coupled to
visual display device 61 8. Computing device 600 may include other suitable conventional 110
peripherals.
100721 Computing device 600 can also include one or more storage devices 624, such as a harddrive,
CD-ROM, or other computer readable media, for storing data and computer-readable
instructions and/or software. Exemplary storage device 624 can also store one or more storage
devices for storing any suitable information required to implement exemplary embodiments,
such as ordered services 150.
100731 Computing device 600 can include a network interface 612 configured to interface via
one or more network devices 622 with one or more networks, for example, Local Area Network
(LAN), Wide Area Network (WAN) or the Internet through a variety of connections including,
but not limited to, standard telephone lines, LAN or WAN links (for example, 802.11, TI, T3,
56kb, X.25), broadband connections (for example, ISDN, Frame Relay, ATM), wireless
connections, controller area network (CAN), or some combination of any or all of the above.
The network interface 612 can include a built-in network adapter, network interface card,
Attorney Docket No.: 127955-03640 (4090IN011)
PCMCIA network card, card bus network adapter, wireless network adapter, USB network
adapter, modem or any other device suitable for interfacing computing device 600 to any type of
network capable of communication and performing the operations described herein. Moreover,
computing device 600 can be any computer system, such as a workstation, desktop computer,
server, laptop, handheld computer, tablet computer (e.g., the i~ad@tab let computer), mobile
computing or communication device (e.g., the i~hone@co mmunication device), or other form of
computing or telecommunications device that is capable of communication and that has
sufficient processor power and memory capacity to perform the operations described herein.
[0074] Computing device 600 can run any operating system 616, such as any of the versions of
the MicrosoftO Windows@ operating systems, the different releases of the Unix and Linux
operating systems, any version of the MacOSO for Macintosh computers, any embedded
operating system, any real-time operating system, any open source operating system, any
proprietary operating system, any operating systems for mobile computing devices, or any other
operating system capable of running on the computing device and performing the operations
described herein. In exemplary embodiments, the operating system 616 can be run in native
mode or emulated mode. In an exemplary embodiment, the operating system 616 can be run on
one or more cloud machine instances.
[0075] In a non-limiting exemplary operation of an embodiment of the system, pick costs are
received by an embodiment of the fulfillment design and optimization engine executing on a
computing device via one or more user interfaces, and parameters for building a scenario are
received by the fulfillment design and optimization engine via the one or more user interfaces. A
global integrated fulfillment system configured to provide the fulfillment design and
optimization engine with orders. The parameters can include at least a zoning policy, a routing
policy, and a base algorithm. The scenario can be created by the fulfillment design and
optimization engine based on the received parameters. An identifier of at least one facility can
be received by the fulfillment design and optimization engine to associate the at least one facility
with the scenario. A scenario simulation on the at least one facility is executed by the fulfillment
design and optimization engine based on the scenario and the pick costs, wherein the scenario
simulation generates pick information for the at least one facility. The pick information includes
one or more of a pick rate, a distance per item, a total distance, a walk time, a retrieval time, a
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Attorney Docket No.: 127955-03640 (4090INOll)
seek time, a consolidation time, a total time, items per tote, and total totes for the at least one
store.
[0076] In a non-limiting exemplary operation of an embodiment of the system, one or more
additional scenarios can be created by the fulfillment design and optimization engine based on
additional parameters entered by the user, and additional scenario simulations on the at least one
facility can be executed by the fulfillment design and optimization engine based on the one or
more additional scenarios. A best scenario can be identified by the fulfillment design and
optimization engine based on the pick information. A comparison between the pick information
generated by the scenario simulations can be displayed on a display via the one or more user
interfaces.
[0077] FIG. 7 illustrates results of improvements made by the fulfillment design and
optimization engine.
[0078] The description herein is presented to enable any person skilled in the art to create and
use a computer system configuration and related method and systems for improving access to
electronic data. Various modifications to the example embodiments will be readily apparent to
those skilled in the art, and the generic principles defined herein may be applied to other
embodiments and applications without departing from the spirit and scope of the invention.
Moreover, in the following description, numerous details are set forth for the purpose of
explanation. However, one of ordinary skill in the art will realize that the invention may be
practiced without the use of these specific details. In other instances, well-known structures and
processes are shown in block diagram form in order not to obscure the description of the
invention with unnecessary detail. Thus, the present disclosure is not intended to be limited to
the embodiments shown, but is to be accorded the widest scope consistent with the principles and
features disclosed herein.
[0079] In describing exemplary embodiments, specific terminology is used for the sake of
clarity. For purposes of description, each specific term is intended to at least include all
technical and functional equivalents that operate in a similar manner to accomplish a similar
purpose. Additionally, in some instances where a particular exemplary embodiment includes a
plurality of system elements, device components or method steps, those elements, components or
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Attorney Docket No.: 127955-03640 (4090IN011)
steps can be replaced with a single element, component or step. Likewise, a single element,
component or step can be replaced with a plurality of elements, components or steps that serve
the same purpose. Moreover, while exemplary embodiments have been shown and described
with references to particular embodiments thereof, those of ordinary skill in the art will
understand that various substitutions and alterations in form and detail can be made therein
without departing from the scope of the invention. Further still, other aspects, functions and
advantages are also within the scope of the invention.
[0080] Exemplary flowcharts are provided herein for illustrative purposes and are non-limiting
examples of methods. One of ordinary skill in the art will recognize that exemplary methods can
include more or fewer steps than those illustrated in the exemplary flowcharts, and that the steps
in the exemplary flowcharts can be performed in a different order than the order shown in the
illustrative flowcharts.

We Claim:
1. A optimization system comprising:
a data storage device; and
a fulfillment design and optimization engine communicatively coupled to the data storage
device, the fulfillment design and optimization engine configured to execute an optimization
module that when executed:
receives a plurality of pick costs;
receives parameters for building a scenario, the parameters including at least a
zoning policy, a routing policy, and a base algorithm;
create the scenario based on the parameters;
receive an identifier of at least one facility to associate with the scenario; and
execute a scenario simulation on the at least one facility based on the scenario and
the pick costs, wherein the scenario simulation generates pick information for the at least
one facility.
2. The system of Claim 1, wherein the pick information includes one or more of a pick rate,
a distance per item, a total distance, a walk time, a retrieval time, a seek time, a consolidation
time, a total time, items per tote, and total totes for the at least one facility.
3. The system of Claim 1, the optimization module, when executed, further configured to:
create one or more additional scenarios based on additional parameters entered by the
user; and
execute additional scenario simulations on the at least one facility based on the one or
more additional scenarios.
4. The system of Claim 3, the optimization module, when executed, further configured to
identify a best scenario based on the pick information.
5. The system of Claim 3, the optimization module, when executed, further configured to
display a comparison between the pick information generated by the scenario simulations.
Attorney Docket No.: 127955-03640 (4090INO 1 1)
6. The system of Claim 1, wherein the pick costs includes one or more of a walking cost, a
retrieval cost, a consolidation cost, and a consolidation space cost.
7. The system of Claim 1, further comprising a global integrated fulfillment system
configured to provide the fulfillment design and optimization engine with orders.
8. A method comprising:
receiving, by a fulfillment design and optimization engine, a plurality of pick costs;
receiving, by the fulfillment design and optimization engine, parameters for building a
scenario, the parameters including at least a zoning policy, a routing policy, and a base
algorithm;
creating, by the fulfillment design and optimization engine, the scenario based on the
parameters;
receiving, by the fulfillment design and optimization engine, an identifier of at least one
facility to associate with the scenario; and
executing, by the fulfillment design and optimization engine, a scenario simulation on the
at least one facility based on the scenario and the pick costs, wherein the scenario simulation
generates pick information for the at least one facility.
9. The method of Claim 8, wherein the pick information includes one or more of a pick rate,
a distance per item, a total distance, a walk time, a retrieval time, a seek time, a consolidation
time, a total time, items per tote, and total totes for the at least one store.
10. The method of Claim 8, further comprising:
creating, by the fulfillment design and optimization engine, one or more additional
scenarios based on additional parameters entered by the user; and
executing, by the fulfillment design and optimization engine, additional scenario
simulations on the at least one facility based on the one or more additional scenarios.
11. The method of Claim 10, further comprising identifying, by the fulfillment design and
optimization engine, a best scenario based on the pick information.
Attorney Docket No.: 127955-03640 (4090INOll)
12. The method of Claim 10, wherein a comparison between the pick information generated
by the scenario simulations can be displayed.
13. The method of Claim 8, wherein the pick costs includes one or more of a walking cost, a
retrieval cost, a consolidation cost, and a consolidation space cost.
14. The method of Claim 8, further comprising a global integrated fulfillment system
configured to provide the fulfillment design and optimization engine with orders.