FORM - 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)
A SYSTEM AND METHOD TO OPTIMIZE RESOURCE ALLOCATION
FOR DISTRIBUTION CENTERS
TATA CONSULTANCY SERVICES LTD,
an Indian Company,
of Nirmai Building, 9th floor,
Nariman Point, Mumbai 400 021,
Maharashtra, India
INVENTORS:
1. SINHA SUDHIR KUMAR
2. SALSINGIKAR SHRIPAD
3. SHAH DHEERAJ ANILKUMAR
4. BEHERA UMESH CHANDRA
The following specification particularly describes the invention and the manner in which it is to be
performed

FIELD OF DISCLOSURE
The present disclosure relates to the field of order replenishment in a supply chain.
Particularly, the present disclosure relates to the field of resource allocation to achieve optimal material / product flow in the outbound processes of distribution centers.
DEFINITIONS OF TERMS USED IN THE SPECIFICATION
The term 'cube plan' in this specification relates to a list which specifies the quantity of load to be picked for each product class / pick-type and for each batch / wave for a group of store replenishment orders belonging to a plan.
The term 'distribution center elements' in this specification relates to human entities and non-human entities involved in realization of an order replenishment operation in a distribution center. These entities include pickers, sorter / scanner subsystem, means to convey product/material, loaders, planners, supervisors and transportation vehicle drivers.
The term 'induction lines' or 'feed-in lanes' in this specification relates to the channel/ path / means including but not limited to conveyor belts or rollers that transport the load to a sorter/scanner subsystem. The induction lines are typically classified as per the product class/pick-type carried by them for instance, hazards, exports or distribution center loads (load which is supposed to be transported to another distribution center in the network).
The term 'loading operation' in this specification relates to activities involved in transportation of load from a sorter/scanner subsystem to trailers through different loading lanes and physical transfer of the material into the trailers (transportation vehicles) by loaders.

The term 'loading lane' or 'non-dock loading lane' in this specification relates to a channel/ path followed by a load to reach a designated loading dock post the sorter/ scanner subsystem or via means that transport load to a designated loading dock post the sorter/ scanner subsystem. In a typical distribution center setup, the 'non-dock loading lanes' may only be used for palletization of loads and may not have direct access to the transportation vehicles/trailers.
The term 'loaders' in this specification relates to entities which are assigned to various loading lanes for transferring loads coming onto loading lanes to designated transportation vehicles / trailers. The entities include automated machines, automatic palletizing machines, industrial robots and human beings.
The term 'pickers' in this specification relates to entities which are assigned to feed-in lanes or induction lines for picking loads from designated storage locations in the distribution center and placing them on the assigned feed-in lanes for transferring to the sorter. The entities include automated machines, automatic palletizing machines, industrial robots and human beings.
The term 'order replenishment' in this specification relates to the process of raising a demand for re-ordering products from a distribution center, which are consumed at a demand center (usually a store in a retail chain) by virtue of their consumption (sale/purchase).
The term 'pick and feed-in operation' in this specification relates to the activities involved in identifying the right product to be picked, picking that product / load from various shelves/racks of the distribution center storage and placing/feeding them on the induction lines/feed-in lanes for conveying to a sorter / scanner subsystem.

The term 'pick type' in this specification relates to the type of product/material that is being conveyed on an induction line. For instance, pick type can be classified based on the basis of any one or a combination of any of the following factors including ways of picking and packaging, storage location and product material, destination and origin.
The term 'pick ahead time' in this specification relates to a time at which an activity of picking a few pick types/product class can be initiated before starting the schedule of regular picking for a given wave/batch for enabling efficient operations.
The term 'pick induction start time' in this specification relates to a time at which pickers start feeding the picked loads to the induction lines for a given batch/wave.
The term 'picking start time' in this specification relates to a time at which pickers start collecting product loads for a given batch/wave based on the details of a replenishment order. (The loads are typically stored near an induction line which is fed to the sorter/ scanner subsystem once induction is commenced)
The term 'PULL' based technique for resource allocation in this specification relates to the technique of generating optimum resource allocation plans by synchronizing the induction or 'pick and feed-in' operations with respect to the loading operation.
The term 'PUSH' based technique for resource allocation in this specification relates to the technique of generating optimum resource allocation plans by synchronizing the loading operations with respect to induction or 'pick and feed-in' operations.

The term 'replenishment operations' in this specification relates to activities involved in fulfilling a replenishment order to a demand center through a distribution center. The activities typically include picking the products listed in the order and feeding them on induction lines to pass them through a sorter/ scanner subsystem, which further scans and sorts the products to respective loading lines from where they are loaded into transportation vehicles by loaders.
The term 'route plan' in this specification relates to a list stating sequence in which trailers are to visit various demand centers/locations/stores to deliver goods/material.
The term 'sorter' in this specification relates to an automated subsystem which may include a code scanner to automatically scan and sort the loads coming from the induction lines and sending the sorted loads to respective pre-assigned / programmed loading lanes for loading in transportation vehicles. The sorter may include an automatic roller/belt conveyor system, Supervisory Control and Data Acquisition (SCADA) system and similar Industrial Control Systems (ICS).
The term 'store-load' in this specification relates to quantity of material/product which is to be transported to a store/demand center from the distribution center.
The term 'external sub-systems' in this specification relates to systems like Inventory Management, Transportation Management, Warehouse Management and Human resource Management in a supply chain that are directly / indirectly associated with order replenishment processes to facilitate shipment of an order at the distribution center. For instance, the inputs include the store order details, the transportation vehicle details along with the route plan for carrying out the order fulfillment and the like.

The term 'user-interface' in this specification relates to an interface used for facilitating interaction between a server and client nodes. The interface includes but is not limited to a web-based, a form-based and a text-based interface.
The term 'wave plan' in this specification relates to a list which specifies a group of orders, which are to be picked and loaded into transportation vehicles, in a batch simultaneously.
BACKGROUND
A distribution center is a specialized facility designed to store large quantities of varied products/material for shipment to stores/demand centers, on demand / as per demand. Typically the distribution centers include three main sections, namely an area for receiving the products/material, an area for storage of the products/material and an area for facilitating on-demand outbound shipments.
The design and operation of the outbound shipment area of the distribution center plays a crucial role as, it is involved in order replenishment of the supply chain. FIGURE 1 of the accompanying drawings shows an exemplary layout of the outbound shipment/order sortation area of the distribution center. The outbound shipment area typically includes a conveyor mechanism which carries the load from the storage locations to the loading lanes. The conveyor mechanism is partitioned into two separate sets of lanes by a sorter subsystem. The first set of lanes are feed-in lanes or induction lines, on which product load picked by pickers from storage locations are placed and transferred to the sorter. The sorter scans and sorts the picked products/loads and sends them onto respective loading lanes (which form the second set of lanes) from where they are loaded onto the designated transportation vehicles by loaders. The efficient assignment of appropriate resources for picking and loading operations leads to efficient

operation of the distribution center and brings economic benefits to the supply chain. Any delay to the distribution center operations results in delay of the ordered product to reach the stores and leads to resultant losses along with higher cost due to inefficiencies in distribution center operations.
Typically, the picking and loading operations are performed by pickers and loaders, which are automated machines like industrial/palletisation robots and forklift trucks. Alternatively, picking and loading operations can be performed manually by employees/workers of the distribution center, who multitask by fulfilling multiple replenishment orders simultaneously.
In scenarios, where the picking and loading tasks require manual effort, they are performed with the help of some automated enablers like barcode readers, light and voice indicators to identify the product to be picked. However, these tasks are subject to the efficiency and speed of the human picker and/or the human loader, which ideally must match the speed and efficiency of the automated sortation system (sorter). However, usually the automated sortation system remains underutilized due to its dependency on manual capabilities of the employees particularly in a situation where picking and loading tasks are done manually.
Picking and loading tasks are also subject to the efficiency of the other automated devices and resources at the peripheral of the sortation system used for picking and loading operations and if the efficiency of these resources does not match the speed and efficiency of the automated sortation system (sorter), it may lead to underutilization of automated sortation system.
In order to improve utilization of the automated sortation system in a distribution center and thereby to improve overall operational efficiency of the distribution

center, there is a need of resource allocation planning system, which will plan/allocate the resources at the peripheral of the sortation system such that the capabilities of these peripheral resources of sortation system are synchronized and product flow in the system is balanced, leading to higher utilization of the automated sortation system.
The need of synchronization between picking, sorting and loading operations makes the resource allocations at picking and loading ends of the the sortation system interdependent, where resource allocation plan at one end acts as input/constraint to other.
The conventional resource allocation planning techniques assume picking operation as the bottleneck operation and therefore plan the resource allocation at the picking side first. Thus, under this technique, planning is done with the intent to induct the loads as fast as possible to the sorter. However, this approach may cause jams and un-necessary recirculation of the products on the sorter via the recirculation line when the loading lanes are jammed because the sorted products on the loading lanes are yet to be loaded on the transportation vehicles.
Moreover, conventionally, the resource allocation plans are generated manually using the rule of thumb based approach. The quality / efficiency of the resource allocation plan is highly dependent on the competency of the personnel/planner to allocate the resources considering wide variety of operational and business specific constraints/rules/priorities. The manual planning process is time consuming, gives little scope for trying out various scenarios ('What-if analysis). Most often the generated plans are sub-optimal and have further scope for improvements. Today, a majority of distribution centers' planners use a manual process to generate plans.

There is therefore felt a need for a system which:
• aids in balancing the flow in distribution center by effective allocations of assets/resources across various process / sub-processes, equipment and locations in a distribution center, thereby improving the utilization of the sorter and overall efficiency of the distribution center;
• automates the generation of resource allocation plans, thereby eliminates the manual and rule of thumb based techniques;
• performs resource/asset allocation by taking into account a variety of operational and business specific constraints/rules/priorities, using optimization techniques;
• provides a work bench to planners for ' What-if analysis; and
• overcomes shortcomings of the "conventional resource allocation planning techniques.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to provide a system which automates the generation of resource/asset allocation plan for distribution centers and eliminates use of rule of thumb based techniques.
It is another object of the present disclosure to provide a system which aids in balancing the product flow by effective allocations of assets/resources across various process/sub-processes, equipment and location in distribution centers.

It is yet another object of the present disclosure to provide a system that performs resource allocation taking into account multiple operational and business specific constraints/rules/priorities.
Further, another object of the present disclosure is to provide a system which includes a work bench to facilitate ' What-if analysis.
Yet another object of the present disclosure is to provide a system to improve the utilization of the sortation subsystem / sorter.
One more object of the present disclosure is to provide a system to overcome the shortcomings of the conventional distribution center resource allocation planning techniques.
Other objects and advantages of the present disclosure will be more apparent from the following description when read in conjunction with the accompanying figures, which are not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a computer-implemented system to optimize resource allocation for distribution centers for order replenishment within a supply chain, the system comprising:
- a centralized application server adapted to receive inputs from relevant external sub-systems of the supply chain and further adapted to perform multi-level, multi-phase optimization to generate optimum resource allocation plans using the inputs, for a designated distribution center; and
- a plurality of distribution center elements associated with the designated distribution center, the distribution center elements adapted to receive the

resource allocation plans to optimally perform the respective order replenishment operations for a corresponding distribution center.
Typically, the system further comprises a plurality of controller nodes co-operating with the application server, each of the controller nodes associated with a discrete distribution center and its corresponding distribution center elements, each of the controller nodes adapted to receive and segregate the resource allocation plans to generate instructions for distributing tasks to distribution center elements identified in the resource allocation plans.
Preferably, the application server performs the multi-level, multi-phase optimization using a "PULL" based technique of resource allocation. Alternatively, the application server performs the multi-level, multi-phase optimization using a "PUSH" based technique of resource allocation.
Further, the resource allocation plan is generated for at least a designated distribution center's loading and 'pick and feed-in' order replenishment operations, wherein:
• the resource allocation plan generated for the loading operation facilitates allocation of store-loads and outbound trucks/trailers to loading lanes and allocation of loaders to loading lanes;
• the resource allocation plan generated for the pick and feed operation facilitates allocation of 'pick type' to 'induction lines' and a picker to "pick type-induction line" pair; and
• the resource allocation plan facilitates determination of 'pick-ahead time' for different product types, time gap between 'picking start time' and 'pick induction start time'.

Still further, the application server comprises:
- a repository to discretely store the inputs received from the external subsystems, identification details for each of the distribution center elements, and resource allocation plans for each of the distribution centers;
- fetching means to fetch inputs from the external subsystems and further load the inputs to the repository, wherein the inputs are selected from the group consisting of distribution center's configuration/infrastructure details, wave plan, cube plan, route plan, and loader and picker details;
- an optimization engine to perform multi-level, multi-phase optimization using the inputs and predetermined operational and business specific multiple constraints/rules/priorities to generate resource allocation plans using techniques selected from the group consisting of linear programming, integer programming, heuristics, meta heuristics and combinations thereof at a first level and further adapted to improve the results using an iterative search procedure at a second level to further refine the first level results; and
- notification means to receive the resource allocation plans and generate wired and wireless communication based notifications for allocation of resources amongst each of the distribution center elements participating in the order replenishment operation.
In addition, the application server further comprises a user interface having interactive menu controls to facilitate communication with the application server for generation, modification and reception of the resource allocation plans for a designated distribution center as well as for editing input data coming from

external subsystems or editing configuration data of distribution center elements and exporting generated resource allocation plan.
Furthermore, the application server further comprises an analyzer unit having corresponding user interface controls to enable distribution center personnel to perform 'What-if analysis' on the generated resource allocation plans and further manipulate selection of the inputs to trigger revision of the generated resource allocation plans by the application server.
Additionally, the application server includes a report generation unit to generate reports, on demand, displaying the generated resource allocation plans in a predetermined exportable format.
In accordance with this disclosure, there is provided a method for optimizing resource allocation in a distribution center for order replenishment within a supply chain, the method comprising the following steps:
- receiving inputs from relevant external sub-systems of the supply chain over a network at an application server;
- facilitating selection and configuration of a distribution center;
- performing multilevel multiphase optimization at the application server for generating optimal resource allocation plans for the designated distribution center; and
- notifying generation of the resource allocation plans to a plurality of discrete distribution center elements associated to the designated distribution center to optimally perform the respective order replenishment operations.

Further, the step of facilitating selection and configuration of a distribution center includes the steps of facilitating editing of the received inputs from the relevant external sub-systems of the supply chain; and further facilitating input and updation of configuration/infrastructural details for the selected distribution center.
Still further, the step of performing multi-level, multi-phase optimization at an application server includes the steps of performing optimization in multiple phases/steps using the inputs and predetermined operational and business specific multiple constraints/rules/priorities employing combination of techniques including linear programming, integer programming, heuristics, meta heuristics at a first level; and improving the results using iterative search procedure at a second level to further refine the first level results to generate the optimum resource allocation plans.
In addition, the step of generating optimal resource allocation plans includes the steps of facilitating allocation of store-loads and outbound trucks/trailers to loading lanes and allocation of loaders to loading lanes for a loading operation of order replenishment; and facilitating allocation of'pick type' to 'induction lines' and a picker to "pick type-induction line" pair for a pick and feed operation of order replenishment.
Typically, the step of generating optimal resource allocation plans for a designated distribution center includes the steps of facilitating distribution center personnel to manually modify the generated resource allocation plans; subsequently facilitating reassessment of the violation of any and predetermined operational and business specific multiple constraints/rules/priorities during modification; and recalculating the value of indicators / objectives reflecting plan quality.

Furthermore, the step of generating optimal resource allocation plans for a designated distribution center includes the steps of facilitating distribution center personnel to conduct 'what-if analysis on the resource allocation plans, facilitating manipulation of the inputs and triggering corresponding revision to the generated resource allocation plans by the application server.
Additionally, the step of generating optimal resource allocation plans for a designated distribution center includes the step of facilitating distribution center personnel to generate reports displaying the generated resource allocation plans in a pre-determined exportable format.
Preferably, the step of notifying generation of the resource allocation plans to a plurality of discrete distribution center elements includes the steps of receiving the resource allocation plans at a controller node associated with a corresponding distribution center, segregating the resource allocation plan for each of the identified distribution center elements and generating instructions to communicate the tasks to be performed to the identified distribution center elements, for optimal order replenishment.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
The system and method to optimize resource allocation for distribution centers will now be described with reference to the non-limiting, accompanying drawings, in which:
FIGURE 1 illustrates an exemplary layout of a distribution center;
FIGURE 2 illustrates a schematic of the system for optimizing resource allocation in a distribution center in accordance with this disclosure;

FIGURE 3 illustrates a schematic of the optimization engine in accordance with this disclosure;
FIGURE 4 illustrates an exemplary snapshot of the user interface showing the distribution center's configuration input showing feed-in lane parameters provided to the system in accordance with this disclosure;
FIGURE 5 illustrates an exemplary snapshot of the user interface showing the resource allocation plan generated by the report generation unit for store-load allocation in accordance with this disclosure;
FIGURE 6 illustrates an exemplary snapshot of the user interface showing the resource allocation plan generated by the report generation unit for truck/trailer allocation to loading lanes in accordance with this disclosure;
FIGURE 7 illustrates an exemplary snapshot of the user interface showing the resource allocation plan generated by the report generation unit for loader allocation in accordance with this disclosure;
FIGURE 8 illustrates an exemplary snapshot of the user interface showing the resource allocation plan generated by the report generation unit for picking allocation in accordance with this disclosure; and
FIGURE 9 is a flowchart showing the steps for optimizing resource allocation in a distribution center in accordance with this disclosure.
DETAILED DESCRIPTION OF THE ACCOMPANYING DRAWINGS
The system and method for optimizing resource allocation in a distribution center will now be described with reference to the accompanying drawings which do not

limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
FIGURE 1 of the accompanying drawings shows an exemplary layout of an order sortation area / outbound shipment area of a distribution center. In accordance with this disclosure, the system classifies the products based on ways of picking and packaging, storage location and product material as 'pick type'. The proposed system assigns dedicated pickers to pick these classified products and place them on designated induction lines for that 'pick type'. The induction lines forward the cartons/cases of the product to a sorter, which is equipped with a scanner. The cartons/cases from the induction lines pass under the scanner that reads the pre-placed label on cartons/cases, sorts and sends them to the loading lanes assigned by the proposed system for loading into the trailers. The system assigns specific loaders to each of the loading lanes to transfer the cartons/cases inside transportation vehicles, which are parked at the loading lane allotted by the proposed system.
Referring to the accompanying drawings, FIGURE 2 illustrates a block diagram of the system 100 for optimizing resource allocation for distribution centers. The system 100 comprises a central application server 102 which remotely co-operates with a plurality of external sub-systems 114, to 114n typically part of the order replenishment system of a supply chain, collectively represented by reference numeral 114 and controller nodes 118] to 118n, collectively represented by reference numeral 118. Each of the controller node 118 is discretely associated with a distribution center and performs the important task of receiving the optimized resource allocation plans from the central application server 102, segregating and distributing tasks based on the resource allocation plans to

associated distribution center elements 120a (I...n) to 120n (1...n), collectively represented by 120.
The central application server 102 receives inputs from relevant external subsystems 114 of the supply chain and further performs multi-level, multi-phase optimization for resource allocation. The application server 102 generates optimum resource allocation plans using the received inputs for at least a designated distribution center.
The application server 102 is the central server which generates optimum resource allocation plans for a plurality of pre-registered distribution centers based on the order details received from the external sub-systems 114. The application server 102 further displays and generates notifications of the optimized resource distribution plans to authorized distribution center personnel on the controller nodes 118.
The application server 102 includes a repository 108 which stores all the received input data for the distribution centers, as well as input data entered by the distribution center personnel, along with the resource allocation plans generated by the application server 102. The input data entered by the distribution center personnel includes the configuration/infrastructural details for a selected distribution center. In accordance with this disclosure, the ability to configure a distribution center's infrastructure enables the proposed system 100 to scale corresponding to business operations of any organization.
The application server 102 also comprises fetching means 104 to fetch inputs from the external subsystems 114 and further load the inputs to the repository 108. The fetching means 104 co-operates with external subsystems 114 including but not

limited to inventory management subsystem, transportation management subsystem, warehouse management subsystem and human resources subsystem to extract relevant input data including but not limited to wave and cube plan (batch wise store replenishment order details), route plan, and loader and picker details. The fetching means 104 also receives inputs pertaining to each distribution center's configuration details specifying the layout of the distribution centers including but not limited to the type of products, number of lanes (induction and loading) and storage layout. The fetching means 104 stores the fetched inputs for each of the predetermined distribution centers in the repository 108.
The optimization engine 106 of the application server 102 receives the fetched inputs from the repository 108 and uses them to generate resource allocation plans for a predetermined / designated distribution center. The overall objective of the optimization engine 106 is to ensure optimal allocation of assets/resources across various processes, equipment and locations of a distribution center, thereby increasing the operational efficiency of the distribution center.
In accordance with this disclosure, the optimization engine 106 utilizes the 'PULL' based technique of resource allocation where the 'pick and feed' operations of the distribution center are synchronized based on allocation derived for the loading operations. Alternatively, the optimization engine 106 can be configured to utilize "PUSH" based technique of resource allocation where the loading operations of the distribution center are synchronized based on allocation derived for the 'pick and feed' operations.
The optimization engine 106 performs the resource allocation using multi-level, multi-phase optimization techniques taking into consideration multiple operational and business specific constraints/rules/priorities applicable to loading as well as

'pick and feed' operations of the distribution centers. The optimization engine 106 employs a combination of techniques like linear programming, integer programming, heuristics, and meta heuristics at a first level and improves the results using an iterative search procedure at a second level to further refine the first level results to generate the optimum resource allocation plans.
The optimization engine 106 includes the following modules for generating optimum resource allocation plans for a distribution center as seen in FIGURE 3. The modules are inter-dependent wherein the output of a predecessor module is being used as one of the inputs to a successor module. For illustrative purposes, the operation of the optimization engine 106 described hereinafter is based on the 'PULL' based technique of resource allocation.
• Loading Lane Assignment Module 302: The objective function of this module 302 of the optimization engine 106 is to minimize the material/ product load handling time for swing loads. Swing loads are the loads which require parallel loading of different store loads in the same transportation vehicle within a wave plan.
The loading lane assignment module 302 receives the route plan and the distribution center configuration details as the input along with the wave plan. Exemplary lists of the operational and business specific constraints/rules/priorities which are considered by this module to generate optimum loading lane assignment plan include:
o The transportation vehicles can be only assigned to loading lanes;
o A load can be allocated to only one loading lane;
o At most one load and one transportation vehicle can be allocated to a loading lane in a batch/wave; and

o A transportation vehicle may need to be allocated to the same loading lane for all the batches.
The loading lane assignment module 302 of the optimization engine 106 uses the aforementioned inputs and operational and business specific constraints/rules/priorities to generate a first level solution using a combination of techniques like linear programming, integer programming, heuristics, meta heuristics at a first level and improves the results using iterative search procedure at a second level to further refine the first level results. In particular, it uses 'branch and cut' method to get the first level solution and further performs a local search improvement technique at the second level. An exemplary local search heuristic generates a new solution by exchanging allocations between loading lanes, inserting existing allocations at a new lane position and shifting the allocations by one lane in the direction of a first emptied lane.
• Loader Allocation Module 304: The objective function of this module 304 of the optimization engine 106 is to minimize the loading time for all the loads to transportation vehicles for a batch/wave and evenly distribute the load amongst loaders.
The loader allocation module 304 receives the inputs including but not limited to store-load/order details and the loader availability and productivity details. Typically, loader productivity depends on loaders' assignment to multiple lanes as well as multiple allocations of loaders to a lane. Exemplary list of, the operational and business specific constraints/rules/priorities which are considered by this module to generate loader allocation details include:

o A loader may not be assigned to non-dock lanes and loading lanes
simultaneously; o The loader can only be assigned to a defined maximum number of
adjacent lanes only; o Total number of allocated loaders should be less than that of available
loaders; and o Minimum one loader should be allocated to a lane.
The loader allocation module 304 uses the aforementioned inputs and operational and business specific constraints/rules/priorities to generate a 'loader allocation plan' and loading time using a combination of techniques like linear programming, integer programming, heuristics, meta heuristics at a first level; and further performs a local search improvement technique at a second level. In particular, it uses 'branch and cut' method to get the first level solution.
The outputs of lane assignment 302 and loader allocation modules 304 together represent the loading optimization module 300 which generates the 'loading' resource allocation plan. Further, the 'loading' resource allocation plan is given to the third module 306 as input such that the 'pick and feed-in' operation of the distribution center's order sortation process can be synchronized based on the 'loading' resource allocation plan.
• Pick Induction and Picker Allocation Module 306: The objective function of the pick induction and picker allocation module 306 of the optimization engine 106 is to minimize the 'pick induction time' (time to complete picking and conveying picks to sorter for all picks for a given batch) and 'pick difficulty

index'. It also aims at determining the 'pick-ahead time' (time gap between picking start time and pick induction start time), if any.
The pick induction and picker allocation module 306 receives the inputs including but not limited to the carton-wise break-up of the store-load for pick-types, wave wise availability of pickers and the output of the lane assignment module and loader allocation module. Exemplary list of, the operational and business specific constraints/rules/priorities which are considered by this module are:
o The number of pickers allocated to 'pick-type induction line" pair must
be less than the pre-defined upper limit for that pair; o Total allocated pickers must be less than maximum number of pickers
available; o Induction of picks to induction lines must always start at the same time;
and o The carrying capacity of the induction line may be constrained and typically defined using belt speed.
The pick induction and picker allocation module 306 of the optimization engine 106 uses the aforementioned inputs and operational and business specific constraints/rules/priorities to generate a first level solution using a combination of techniques like linear programming, integer programming, heuristics, meta heuristics at a first level; and improves the results using iterative search procedure at a second level to further refine the first level results. In particular, it relaxes certain constraints to generate a first level solution using the 'branch and cut' based approach and further re-enforces the constraints at a second level along with the information of first level to generate the picker allocation details,

allocation of 'pick-types' to induction lines, Pick Induction Time, Pick Ahead time and wave time.
An exemplary measure of the difficulty index in the context of pick allocation to induction line can be based on the effort/difficulty of a picker to pick and induct the product from storage to the induction point. For example, picking a product stored at ground floor (Level 0) and inducting it at 1st floor (Level 1) will require more effort (due to additional material handling effort) as compared to inducting the product at ground floor induction line in a multi storied distribution center. Thus, a difficulty-index is used to fasten the induction by minimizing the effort and increasing the load on less difficult induction lines.
The resource allocation plans generated by these modules are then stored in the repository 108 for the corresponding distribution center.
Still further, the application server 102 also includes a multi-user user interface 110 which acts as the communication channel between the controller nodes 118 and the application server 102, The user interface 110 enables authorized distribution center personnel to access the input data used for optimization, edit the input data as well as configuration for various distribution center elements, generate the resource allocation plan on demand, by invoking optimization engine, view the resource allocation plans generated by the optimization engine 106 and generate reports.
FIGURE 4 shows an exemplary snapshot of the user interface 110 showing the distribution center's configuration input for feed-in line parameters which are provided to the application server 102. The feed-in line 'distribution center configuration parameter' record fetched by the fetching means 104 is represented by reference numeral 30. In addition, the user interface 110 also enables the

distribution center personnel to provide manual inputs, represented by reference numeral 32, for a particular distribution center. Similar user-interface is presented to the distribution personnel on the controller nodes 118 for the other inputs stored in the repository 108. In addition, the user interface 110 also enables the distribution center personnel to modify the plan generated by optimization engine, where distribution center personnel can change the allocation plan as per changed scenarios at ground level
Furthermore, the central application server 102 enables the distribution center personnel to perform analysis on the generated resource allocation plans using an analyzer unit 112 through the user interface 110. The analyzer unit 112 employs the 4what-if analysis' technique and enables a distribution center personnel to change input parameters to reflect changes as per changed scenarios at ground level (in the distribution center) like changes in the configuration of the distribution center elements, turn on/off the constraints applied to each of the optimization engine's 106 modules and change the objective function of the modules to analyze the impact on the resource allocation plans.
Additionally, the central application server 102 includes a report generation unit 117 which enables distribution center personnel to generate reports displaying the resource allocation plans generated for a particular distribution center. The report generation unit 117 co-operates with the repository 108 to extract the information required for generating a report requested by a distribution center personnel. The report generation unit 117 displays the information in the reports in a predetermined exportable format on the user interface 110.
FIGURE 5, FIGURE 6, FIGURE 7 and FIGURE 8 show an exemplary snapshot of the user interface 110 showing the reports that are presented to the

distribution center personnel, showing the resource allocation plan generated for store load, allocation, truck/trailer allocation, loader allocation and picking allocation respectively by the report generation unit 117. These resource allocation reports are displayed to the distribution center personnel on their controller nodes 118. The user interface 110 provides the necessary menu based controls to the distribution center personnel to export these reports in predetermined file formats. FIGURE 5 shows store load allocation report wherein a list of loading lanes are assigned with corresponding identity of the store and cube data for a particular wave id/plan. FIGURE 6 shows a truck/trailer allocation report generated by the report generation unit 117, the report shows truck identification numbers assigned to different loading lanes for a particular wave plan. FIGURE 7 shows a list of the loading lanes and the corresponding identifier of the loader allocated to that lane for a particular wave plan. Similarly, the resource allocation report interface as seen in FIGURE 8 displays the number of units to be pick, the identifier for the assigned picker, and pick ahead time for an assigned induction lane and 'pick-type' combination.
The central application server 102 includes notification means 116 which generates notifications for a corresponding controller node 118, to alert the node of the new resource generation plan. The controller nodes 118 can be any wired or mobile computing devices having a processor for processing information and instructions, a storage for storing the resource allocation plans, an output device such as a display device (e.g., a monitor) for displaying resource allocation information and presenting the user interface 110 to the distribution center personnel, an user input device for communicating information and command selections to the processor. The controller nodes may also include a communication link between the

controller nodes and a network, using either a wired or a wireless communication interface.
The controller nodes 118 on receiving the resource application plan, analyzes and segregates the plan based on the allocation of tasks determined by the application server 102.. The controller node 118 communicates with each of the distribution center elements 120 identified in the resource allocation plan to allot the respective elements their tasks, for realizing the order fulfillment operation. For instance, the controller node 118 sends instructions to the pickers, wherein each of the identified pickers are instructed to pick a particular number of store loads based on the 'pick type' from the storage location and feed them on one or more specific induction lines. The pickers are also instructed of the 'pick ahead time', if any. Likewise, the loaders identified in the resource allocation plan are instructed to collect loads from one or more specific loading lanes and further load them into the identified transportation vehicles. The controller node 118 also sends instructions to transportation vehicle drivers so that their vehicles are parked along the respective loading bays to reduce order fulfillment delays. Moreover, instructions are sent to the sorter informing the sorter the number of store loads belonging to an order that must be shipped in a particular transportation vehicle and its corresponding loading lane. The sorter based on the instructions, scans the products and accordingly sorts them over the respective loading lanes.
In addition, the controller node 118 provides distribution center personnel access to the functionalities provided by the user interface 110 to enable them to communicate with the application server 102 for viewing / editing the resource allocation plan related data, conducting analysis and generating reports.

Thus, the controller node 118 ensures effective execution of the resource allocation plans generated by the application server 102. Since, the controller node 118 is typically a distribution center resident device, which can both wirelessly or through wired means communicate tasks to the identified distribution center elements 120 at once, in real-time this reduces the time required for order replenishment.
In accordance with this disclosure, a method for optimizing resource allocation for distribution centers for order replenishment within a supply chain is provided which comprises the following steps as seen in FIGURE 9: receiving inputs from relevant external sub-systems of the supply chain over a network at an application server 1000; facilitating selection and configuration of a distribution center 1002; performing multilevel multiphase optimization at the application server for generating optimal resource allocation plans for the designated distribution center 1004; and notifying generation of the resource allocation plans to a plurality of discrete distribution center elements associated to a distribution center to optimally perform the respective order replenishment operations 1006.
Further, the step of facilitating selection and configuration of a distribution center includes the steps of facilitating editing of the received inputs from the relevant external sub-systems of the supply chain; and further facilitating input and updation of configuration/infrastructural details for the selected distribution center.
Still further, the step of performing multi-level, multi-phase optimization at an application server includes the steps of performing optimization in multiple phases/steps using the inputs and predetermined operational and business specific multiple constraints/rules/priorities employing combination of techniques including linear programming, integer programming, heuristics, meta heuristics at a first level; and improving the results using iterative search procedure at a second

level to further refine the first level results to generate the optimum resource allocation plans.
In addition, the step of generating optimal resource allocation plans includes the steps of facilitating allocation of store-loads and outbound trucks/trailers to loading lanes and allocation of loaders to loading lanes for a loading operation of order replenishment; and facilitating allocation of 'pick type' to 'induction lines' and a picker to "pick type-induction line" pair for a pick and feed operation of order replenishment.
Furthermore, the step of generating optimal resource allocation plans for a designated distribution center includes the steps of facilitating distribution center personnel to conduct 'what-if analysis on the resource allocation plans, facilitating manipulation of the inputs and triggering corresponding revision to the generated resource allocation plans by the application server.
Typically, the step of generating optimal resource allocation plans for a designated distribution center includes the steps of facilitating distribution center personnel to manually modify the generated resource allocation plans; subsequently facilitating reassessment of the violation of any and predetermined operational and business specific multiple constraints/rules/priorities during modification; and recalculating the value of indicators / objectives reflecting plan quality.
Additionally, the step of generating optimal resource allocation plans for a designated distribution center includes the step of facilitating distribution center personnel to generate reports displaying the generated resource allocation plans in a pre-determined exportable format.

Preferably, the step of notifying generation of the resource allocation plans to a plurality of discrete distribution center elements includes the steps of receiving the resource allocation plans at a controller node associated with a corresponding distribution center, segregating the resource allocation plan for each of the identified distribution center elements and generating instructions to communicate the tasks to be performed to the identified distribution center elements, for optimal order replenishment.
TECHNICAL ADVANTAGES
The technical advantages of the present disclosure include realization of a system and method for optimizing resource allocation in a distribution center for order replenishment.
The proposed system automatically generates allocation plan in least possible time using mathematical models and heuristics based techniques to save manual effort and eliminate manual and rule of thumb based distribution center resource/asset allocation techniques. This can also be leveraged to centrally plan the resource allocations for all distribution centers in a network in a standardized and efficient way.
The proposed system utilizes 'PULL' based concept of resource allocation where loading operation is identified as the crucial operation and induction or 'pick and feed' operation is synchronized with respect to the loading operation. Alternatively, the proposed system can be configured to utilize "PUSH" based concept of resource allocation.
Further, the proposed system facilitates effective allocation of assets/ resources of the distribution center considering wide variety of operational and business

specific constraints/rules/priorities and performing multilevel multiphase optimization.
Furthermore, the proposed system facilitates the distribution center personnel to
manually modify the generated resource allocation plans. The system subsequent
to the modification facilitates reassessment of the violation of any and
predetermined operational and business specific multiple
constraints/rules/priorities; and enables recalculation of the value of indicators / objectives reflecting plan quality.
Still further, the proposed system enables distribution center personnel to perform 'what-if analysis' and gives them the provision to change input parameters, turn on/off the constraints applied to each of the optimization engine's modules and change the objective function of the modules via interactive controls, to analyze the impact on the resource allocation plans.
Thus, the proposed system optimizes resource allocation to maximize distribution center infrastructural efficiencies and minimize cost of operations.
Throughout this specification the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.
The use of the expression "at least" or "at least one" suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results.

Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the invention as it existed anywhere before the priority date of this application.
While considerable emphasis has been placed herein on the particular features of this invention, it will be appreciated that various modifications can be made, and that many changes can be made in the preferred embodiment without departing from the principles of the invention. These and other modifications in the nature of the invention or the preferred embodiments will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.

WE CLAIM
1. A computer-implemented system to optimize resource allocation for
distribution centers for order replenishment within a supply chain, said
system comprising:
- a centralized application server adapted to receive inputs from relevant external sub-systems of the supply chain and further adapted to perform multi-level, multi-phase optimization to generate optimum resource allocation plans using said inputs, for a designated distribution center; and
- a plurality of distribution center elements associated with said designated distribution center, said distribution center elements adapted to receive said resource allocation plans to optimally perform the respective order replenishment operations for a corresponding distribution center.

2. The system as claimed in claim 1, wherein said system further comprises a plurality of controller nodes co-operating with said application server, each of said controller nodes associated with a discrete distribution center and its corresponding distribution center elements, each of said controller nodes adapted to receive and segregate said resource allocation plans to generate instructions for distributing tasks to distribution center elements identified in said resource allocation plans.
3. The system as claimed in claim 1, wherein said application server performs said multi-level, multi-phase optimization using a "PULL" based technique of resource allocation.

4. The system as claimed in claim 1, wherein said application server performs said multi-level, multi-phase optimization using a "PUSH" based technique of resource allocation.
5. The system as claimed in claim 1, wherein said resource allocation plans are generated for at least a designated distribution center's loading and 'pick and feed-in' order replenishment operations, wherein:

• said resource allocation plan generated for said loading operation facilitates allocation of store-loads and outbound trucks/trailers to loading lanes and allocation of loaders to loading lanes;
• said resource allocation plan generated for said pick and feed operation facilitates allocation of 'pick type' to 'induction lines' and a picker to "pick type-induction line" pair; and
• said resource allocation plan facilitates determination of 'pick-ahead time' for different product types, time gap between 'picking start time' and 'pick induction start time'.
6. The system as claimed in claim 1, wherein said application server
comprises:
- a repository to discretely store said inputs received from said subsystems, identification details for each of said distribution center elements, and resource allocation plans for each of the distribution centers;
- fetching means to fetch inputs from said subsystems and further load said inputs to said repository, wherein said inputs are selected from the group consisting of distribution center's configuration/infrastructure details, wave plan, cube plan, route plan, and loader and picker details;

- an optimization engine to perform multi-level, multi-phase optimization using said inputs and predetermined multiple operational and business specific constraints/rules/priorities to generate resource allocation plans using techniques selected from the group consisting of linear programming, integer programming, heuristics, meta heuristics and combinations thereof at a first level and further adapted to improve the results using an iterative search procedure at a second level to further refine the first level results; and
- notification means to receive said resource allocation plans and generate wired and wireless communication based notifications for allocation of resources amongst each of the distribution center elements participating in the order replenishment operation.

7. The system as claimed in claim 1, wherein said application server further comprises a user interface having interactive menu controls to facilitate communication with said application server for generation, modification, reception and export of said generated resource allocation plans for a designated distribution center, said user interface further facilitates editing of said inputs received from the external subsystems and editing of configuration data associated with said designated distribution center and its elements.
8. The system as claimed in claim 1, wherein said application server further comprises an analyzer unit having corresponding user interface controls to enable distribution center personnel to perform 'What-if analysis' on said generated resource allocation plans and further manipulate selection of said

inputs to trigger revision of said generated resource allocation plans by said application server.
9. The system as claimed in claim 1, wherein said application server includes a report generation unit to generate reports displaying said generated resource allocation plans in a pre-determined exportable format.
10. A method for optimizing resource allocation in a distribution center for order replenishment within a supply chain, said method comprising the following steps:

- receiving inputs from relevant external sub-systems of the supply chain over a network at an application server;
- facilitating selection and configuration of a distribution center;
- performing multilevel multiphase optimization at said application server for generating optimal resource allocation plans for the designated distribution center; and
- notifying generation of said resource allocation plans to a plurality of discrete distribution center elements associated to the designated distribution center to optimally perform the respective order replenishment operations.
11. The method as claimed in claim 10, wherein the step of facilitating selection
and configuration of a distribution center includes the steps of facilitating
editing of said received inputs from relevant external sub-systems of the
supply chain for said selected distribution center; and facilitating input and
modification of configuration/infrastructural details for said selected
distribution center.

12.The method as claimed in claim 10, wherein the step of performing multilevel, multi-phase optimization at an application server includes the steps of performing optimization in multiple phases/steps using the inputs and predetermined multiple operational and business specific constraints/rules/priorities employing combination of techniques including linear programming, integer programming, heuristics, meta heuristics at a first level; and improving the results using iterative search procedure at a second level to further refine the first level results to generate the optimum resource allocation plans.
13.The method as claimed in claim 10, wherein the step of generating optimal resource allocation plans includes the steps of facilitating allocation of store-loads and outbound trucks/trailers to loading lanes and allocation of loaders to loading lanes for a loading operation of order replenishment; and facilitating allocation of 'pick type' to 'induction lines' and a picker to "pick type-induction line" pair for a pick and feed operation of order replenishment.
14.The method as claimed in claim 10, wherein the step of generating optimal resource allocation plans for a designated distribution center includes the steps of facilitating distribution center personnel to conduct 'what-if analysis on said resource allocation plans, facilitating manipulation of said inputs and triggering corresponding revision to said generated resource allocation plans by said application server.
15. The method as claimed in claim 10, wherein the step of generating optimal resource allocation plans for a designated distribution center includes the step of facilitating distribution center personnel to generate reports

displaying said generated resource allocation plans in a pre-determined exportable format.
16.The method as claimed in claim 10, wherein the step of generating optimal resource allocation plans for a designated distribution center includes the steps of facilitating distribution center personnel to manually modify the generated resource allocation plans; subsequently facilitating reassessment of the violation of any and predetermined operational and business specific multiple constraints/rules/priorities during modification; and recalculating the value of indicators / objectives reflecting plan quality.
17.The method as claimed in claim 10, wherein the step of notifying generation of said resource allocation plans to a plurality of discrete distribution center elements includes the steps of receiving said resource allocation plans at a controller node associated with a corresponding distribution center, segregating said resource allocation plan for each of the identified distribution center elements and generating instructions to communicate the tasks to be performed to the identified distribution center elements, for optimal order replenishment.