System and Method for Space and Resource Optimization

Technical Field

The present disclosure relates to resource optimization, more precisely in solving specific space and resource optimization problem.

Background

Space and resource optimization is a long standing problem present across various fields such as inventory management, retail management etc. Currently all the known space and resource optimization techniques look at space as single parameter of allocation and solve only the specific shelf space optimization problem. However, there exists several unsolved problems such as unable to recognize space-time as a generic continuum- current problems only treat micro space , macro space as two different problems , and do not contain the time dimension in optimization process -leading to only one frame in time of the micro space planogram. unable to treat inventory , supply chain , labor , presentation , assortment ,portfolio , size and pack , budgetary , promotional and price variations, constraints in planning as a single point of resolution leading to fragmentation of the planning problems into different optimizations problems such as inventory , price , supply chain , assortment, promotional, portfolio , macro and micro space optimizations with manual integration among them. Also, unable to recognize the spatial data in a way to reuse existing application infrastructure and have more context specific as well as context independent business constraints, rules for better macro or micro space , assortment planning. Business process of merchandize planning , space planning is mostly manual for large no of retailers and store planning and merchandize planning processes / departments are not fully integrated to give total life cycle support -kind of resources (time/money/people)spent are phenomenal and yet accuracies/productivities are mostly missing . Integration is mostly manual between various stages of planning and 3rd party tools if it exists. Reuse of existing application in store planning and merchandize planning from store layout and product placement are not accurately connected; lot of intangible rules doesn't get captured or are manually adjusted for in the space planning processes. Correlation based rules remain untapped as data for them is not available easily; inflexibility of workflow management , business process scalability at lower level of execution; inability of existing application to integrate store clustering as well as store planning as phases of planning with integrated view. Inability of existing products to treat assortment plan, enterprise plan as consistent business entities and business documents- and haxe. a consistent strategy for their treatment, integration, view or management. Absence of integrated resource- asset management tool features to integrate the store plans, assortment plans and space plans and common information repository for various departments and implementation which will be a solution without replicating the information sources across department (having consistent ,one source of entities , business documents , business processes across planning cycles for all roles - store planning , space planning , assortment planning ,enterprise merchandize planning) or having a data integrity issues with information or have non consistent business process. The amount of information to be processed during the merchandising cycle requires an efficient, performing, automated and easily context integral solution which current products are not. compulsion on space elasticity numbers to be able to decide on facings in optimizations.

Accordingly, there is a need for a technique that enables a very generic principles, approach , framework , process , techniques and methodology to solve different kind of problems involving multi dimensional, hierarchical multi objective optimization with distinct commonality abstracted and similarities generalized and in process identifying common relaxations, assumptions and approximations.

Summary of the invention

Aspects of the disclosure relates to solving specific space and resource optimization problem. The present disclosure describes methods that would ensure solving the problems involving space, time, item entity dimensional hierarchies and optimization of allocation, selection problems under constraints and objectives.

According to the one aspect of the present disclosure, a method for space and resource optimization comprises receiving a plurality of inputs, wherein the inputs can be but not restricted to a reference data or a user provided constraints. The next step of the method comprises, transforming the plurality of inputs to an algorithmic graph or a structural graph based on a domain specific area using a processor. The plurality of inputs is transformed based on an internal application or an algorithmic requirement. After transforming the plurality of inputs, they are unfolded on to a specific axis. Further, the plurality of data unfolded on the specific axis is encoded on to a s-cell structure axis. The step of transforming the plurality of inputs also comprises, a plurality of decision variables based on a problem statement or an internal structure. The plurality of constraints can be transformed using a plurality of constraints such as plurality of relaxations, plurality of approximations or a plurality of assumptions. The embodiment of the present disclosure also comprises, generating a dynamic model from a plurality of objectives, wherein the plurality of objectives can be a single objective, a multiple objective or both. The embodiment of the present disclosure can also generate an implicit constraint file or an explicit constraint file. The embodiment further comprises creating heuristics for implicit or explicit parallelization. Furthermore, the embodiment further comprises a step of performing an optimization run. The final step of the present embodiment comprises analyzing an optimal result from the optimization run. Analyzing an optimal result also comprises, generating a set of decisions, at least one output dimensions or an optimal result from the plurality of decision variables.

Drawings

These and other features, aspects, and advantages of the present invention will be better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Fig. 1 is a flow chart illustrating a method 100 for space and resource optimization in accordance with an embodiment of the present disclosure; and

Fig. 2 is a block diagram illustrating a system 200 for space and resource optimization, in accordance with an embodiment of the present disclosure.
Detailed Description

As appreciated by a person having ordinary skill in the art, the important terms and definitions of the present disclosure includes:

1. Cellular computing: cellular computing , as the very word suggest is to mimic the workings of the cell into the world of computing to solve variety of problems or create systems which are self-driven while organic in nature, This import of biological constructs fits very well into existing paradigms and into the world of computing.

2. s-cell -super-cell or stem-cell: constructs under cellular computing , in essence is the unit of computing or computation under cellular computing , it reflects the computing equivalent of biological cell, by saying it as s-cell it is assumed to function like a super cell or stem-cell i.e. root of all other cells along with being itself a cell, it encapsulates the core functionality of domain into it in same way the stem-cell encapsulates the core functionality of other cells of all other organs and develop into organs of different type. It contains attributes and transformed dimensional data which facilitates computings- computing cell can belong different types and can have intelligence associated with it in form of algorithmic creators , translators or transmitters -communication facilities just like real cell.

3. s-grid -super grid: the world of computing is composed of creating , interpreting and transforming computing virtually into canvas or pictures made up of s-cells , such large multi dimensional matrix which encapsulates the domain structural taxonomy made form s-cell into it along with unfolding of hierarchical and dimensional data is called as super grid or simply s-grid , s grid is somewhat like a intelligent organic digitization of multi¬dimensional multi hierarchical virtuality or a motion picture of computing world

4. s-graph - super graph or super assembly: same as s-grid but here the computational universe is made from hierarchal assembly or a super graph , it has similar properties' of s-grid and has its own intelligence -just like a organ functional differently from the cells which made those organs and body functions using variety of these organs but body itself has different multitude of functions which uses these organs; we can day that body has survival or growth function because cell constituting that body has survival and growth needs , we can also infer that body has a free will and other set of desires which are somewhat missing in simple cell-cell is body in itself at lower scale

5. seed computing: this novel computing paradigm involves patterns which simulate or mimic the seed plantation over a field , in essence you will drop certain seeds over the plantation to cover the plantation you will drop seeds at tactical locations to avoid overlaps, in computing world plantation is replaced by the digital canvas- of solution- universe and seed as locations and algorithmic which will start growing (solving .) the. digital canvas for a solutions , you can drop many seeds in the solution irrrrverse-to divide and conquer the problem -solution universe as fast as possible , each seed will-start growing in one or many directions to traverse the possible solutions space and arrive at one or many possible candidate solutions, idea is to use parallel, grid , cloud computing along with divide and conquer to solve the problem much faster; each cell will grow in its nativity so seed dropping algorithmic also has to be considerate of solution universe and its size , resources available and to make sure no two seeds overlap in their reach -if they intersect or overlap it could terminate the germination of both the seed and may involve reduced utilization of the seed -just like in real world where if we place seeds to two big trees side by side either both will perish or one will consume other , idea is to avoided redundant computing due to possible overlap or keep the redundant computing only to boundary areas (as much possible) and have well balanced , well divided (equally divided if possible) solution universe for distributed or dropped seeds ; to have maximum effect and smallest possible execution time to cover the area

6. symmetric computing: computing patterns which are present or needed or can be used to create or harness structural or processing symmetry of the context and its entities to derive significant benefits in enriching the base computing capacity of the scaffolding or/and system throughput; computing patterns which involves exploiting the structural or behavioral symmetries of the problem or the solution universe to fasten solution or computing time are called as using symmetric computing

7. Enterprise computing: computing patterns, techniques, tools which work as whole at the level of enterprise and enable computing to happen at various-levels by effectively provides enterprise functional hierarchies and smooth or seamless integration- are collectively called as belonging to enterprise computing ; alternatively enterprise computing can be conceived as patterns or computation which brings effectiveness in resource utilization and demand there by facilitating efficiency and maximize the return on IT can be called as enterprise computing ; thirdly enterprise computing can simply be term for and around usage of enterprise software tools and creating enterprise wide computational platform

8. Chain reaction computing: computing patterns which mimic the effect of chain reaction quickly spread and consume the space are called as chain reaction computing patterns , they essentially act over a seed and grow rapidly in all direction to create a computational graphs of problem and solution universe ; these patterns and this paradigm is primarily used to fasten the computing time of large computational requirements by breaking down - the problem-solution universe and executing the sub problems or exploring the sub solution universe for quicker completion

9. avatar computing: computing phenomenon , computing need , techniques where optimal selection , placement and distribution of entities (from variety of groups) and its variations inside a container with application of particular contextual rules or constraints is called avatar computing ; in essence entity from various avatars , one is selected to manifests in container in some form

10. Logical or virtual product: we define these terms so as to have ease of approximations in our computing algorithms as well as ease of merchandising to retailers when they use our product, logical or virtual product is essentially made up of one or many products and behaves as a single atomic unit of merchandising

11. Avatars: we define avatar as a specific -special representation of the produet on the shelf, we recognize and identify various ways retailer deicide to arrange the products on the shelf, sometimes as a group of products , sometimes rotated , stacked on top of one another or side by side , we captures these various manifestations of the products as avatars in retail world and we use various computing techniques to create optimal fitment of these products with their avatars on shelf and fixture

12. avatar for facings: facings number of physical products placed side by side which expose the face of each product on shelf to consumer for selection , variations in how many products of each types should be kept side by side in what numbers and in what store is a complex domain intensive choice which uses functional structures such as decision trees , we defines one form of avatar as representing products with optimal number of facings out of plethora of choices on the shelf

13. avatar of product rotation which placement on the shelf: retailer may choose to keep certain products with their facings as rotated product in particular way on the shelf -this can be for space saving or space utilization or for proper representation -books on the shelf, we define various 6 configurations in which product can be placed on the shelf and we call these as rotational avatars of the product

14. avatar of product stacking: manifestation of the product which involves stacking product of same or different types on top of one another based on strategies of presentation , assortment and inventory or space are called as "stacking avatar" in our proposed terminology to retail merchandising

15. V-avatar: avatar of logical or virtual products: avatar can involve one product or groups of products such what we call as logical or virtual products behaving as single atomic unit, such manifestations will be termed as avatar of logical or virtual products

16. super avatar:-s-avatar: all the above distinctive variations of the product possible in its universe will be called as super avatar

17. God-Avatar, G-avatar: generic abstraction of concept of avatar - as applied to any entity in its generic universe: all concepts which are applicable to retail as avatars are also applicable to generic allocation of any entity type in space , or to be more generic to any entity which is a containee in its container for selection , placement and distribution ; our generic approach use this as a generic placement mechanisms.

18. Zero or one-click : Indicates the level of automation , intelligence and usability aspects of the solution so as merchandisers can easily configure rules -constraints , templates to create merchandise plans at the go of one or no click and without any manual entry or rework.

19. Integrated: assortment, Macro and Micro and other tie ups such as inventory , pricing , budget.

20. Hierarchical : merchandising hierarchy correlated to space hierarchy to tentatively and recursively find levels of optimization

21. Simultaneous : Space-Both Macro and Micro , Budget , assortment ,Inventory , Promotions, Price, Size and pack and Supply Chain Optimization in One go

22. Round Trip : continuous feedback with versioning of entities and auto intelligence incorporated.

23. Automated : Automatic creation of Micro Plan-o-grams , floor space plans, assortment plan ,Budget , inventory plan using intelligence from rules and- templates with customization per season-cluster-store

24. Optimality : use of intelligence and optimization to create optimal merchandise plans such as plan-o-gram (POG), floor plan, assortment plan, Budget and inventory plans.

The present disclosure proposes a method 100 represented in Fig.l for space and resource optimization in accordance with an embodiment of the present disclosure. The step 110 describes the process of receiving the plurality of inputs. The plurality of inputs can be but not restricted to at least one dimension, a reference data, at least one hierarchy, or a plurality of user provided constraints. There can exist at least one hierarchy present inside each of the at least one dimension defined as an input.

At step 120, the plurality of inputs is transformed into at least one or more of an algorithmic graph or a structural graph. In the present embodiment of the present disclosure, the plurality of inputs is transformed based on an algorithmic requirement or based on an internal application. The at least one hierarchy present inside at least one dimension is unfolded on a specified axis. Further, the plurality of data is encoded on the specific axis in a s-cell structure axis. The plurality of data on the specific axis in the s-cell structure axis is solved. A plurality of decision variables can also be used while transforming the plurality of inputs. The plurality of decision variables can be a problem statement or an internal structure. Also, a plurality of constraints can be used for the step of transformation of the plurality of inputs. The plurality of constraints ean be but not limited to a plurality of relaxations, or a plurality of approximations or -a plurality of assumptions. The step of transformation can also be performed using the plurality of-user inputs, wherein the plurality of user inputs can be but not restricted to a set of domain specific rules or a set of domain independent rules. Transformation is performed at the higher level constraint type into lower levels ones by subsequent application for each higher level or extended constraint type into its relevant lower level constraints and then further covert them into our base or atomic constraints.

In another embodiment of the present disclosure, structural simplifications are performed after the transformation of the plurality of inputs. The structural simplifications can be enforced by an implicit constraint.

At step 130, heuristics are created for parallelization. The heuristics can be for both implicit and explicit parallelization.

At step 140, the optimization run is performed. In essence, when optimization runs, optimal picture that is rendered is (nonlinear or linear multi-dimensional -multi objective) is point on part of the internal canvas representing optimality, i.e internal Picture or structure =transformations + implicit-explicit domain specific and domain independent structural constraints + explicit domain picture constraints + implicit domain picture constraints and the algorithm is mimicked by creating a structure inside optimization.

At step 150, the optimal result generated from the optimization run is analyzed. In the step of analyzing the optimal results from the optimization run, a set of decisions or at least one output dimension or an optimal result from the plurality of decision variables are generated in combination or individually. Furthermore, a post transformation or reverse transformation is performed after analyzing the optimal results. Transformation is specific to problem type and algorithm and is created as a picture of the source or target universe of feasible solutions, picture is made up of cells-like pixels, super cell to organisms or may evolves like stem cells into any definite organ where s-cell encodes information with respect to dimensional data, hierarchical data, objective function data as a combined decision variable, transform the input to fit into that structure.

As appreciated by a person having ordinary skill in the art, the concept of s-cell can be defined by defining s-cell as a encoding of information of multiple dimensions and hierarchies into it, we can create different structures (assemblies or graphs of s-cells encapsulating properties of domain objective function along with domain entities )statically or dynamically to facilitating mimicking different algorithms or generalize the different problem statements, structures of s- cell are enforced by means of generic or specific constraint of association of multi-dimensional entity-hierarchies and its unfolding on internal axis where structure is established , it reflects either the generic or aggregates or higher resolutions of problems statements, for e.g we can define s-cell as a whole or part of decision variable which has cellx component, y component, z component where cell x component = Transform(dimensionl.hierachyl-Node,.. diml.hierarchyn-
Node) cell y component = Transform(dimension2.hierachyl-Node,.. dim2.hierarchyn-Node) cell z component = Transform(dimension3.hierachyl-Node,.. dim2.hierarchyn-Node) objective function = maximize function of Profit or Rank or Multiple Objective as a (sum(all s-cells, s-cells in frame ,decision variable..) is specific s-cell part of the internal picture or point of optimality becomes a yes / no decisions variable either set implicitly (implicit constraint) or set by optimization run at the output based on optimality accomplished.

The method described in the present disclosure can be better explained when the steps in the described method can be used to find out optimal set of products to be carried in an assortment for a typical retail store, at micro level, more particularly an approach for best possible location for the selected assortment, to assess the impact of varying product margins on the potential assortment, to decide upon the number of facings required to be carried by the retailers in case elasticity (variation in sales due to variations in facings, and unit change in profit/revenue as we change the space allocated to it per store-cluster-season) is required to be considered. The present disclosure also derives the useful space elasticity substitutes in computing the optimality at Macro (department / category) levels. The present disclosure also assists in arriving at the proper specializations of Macro/Micro plans by applying / creating business intelligence scenarios/what ifs for various parameters to derive further demographic sensitive eontextuafeed - planogram or floor plans at cluster or individual store or fixture or zone levels.

In one embodiment of the present disclosure, the micro-planning can be performed using one or more of: Using inventory, presentation, assortment, pricing, budget and supply chain replenishment rules and case pack size to determine facings, reducing the optimization to a knapsack like problem; Using facings min and max limits along with product instances (avatars) to handle elasticity Using consumer decision trees to limit the number of products that meet the same demand.

In another embodiment of the present disclosure, the macro planning level optimization can be performed by using concept of 720 degrees of sensitivity analysis from micro stage to compute space, location, cross elasticity's to be used at macro level: 720 degrees business super sense- lligence is an advance sensitivity and spherical sensitivity gradient analysis, is analysis or what if around all key decision variables , parametric variations , inputs variations possible in problem universe to provide, likely changes in the inputs to arrive at specified o/p values .i.e. to create a guidance system in a form of advance decision sphere for all possible - relevant data inputs and changes in levers as well as variations in merchandise strategies for creation of most optimal POGs, assortment set as tuned to context, demographics, season and cluster; use of per cluster- per store-per season avatars for departments, categories and subgroups -variations in terms of allocation of space and equivalent variation in expected sales and expected average margins aggregated for subgroup, categories and departments to drive space allocation to department/ categories and subgroups for specific group of stores and store per season.

Primary business process novelty for retail merchandise and space planning is can be zero or One-Click, Integrated, Hierarchical, simultaneous, round trip with automation and optimality.

In an embodiment of the present disclosure, a sample minimalistic model for micro -one of the base models that could be created with relaxations and assumptions for faster performance and feasibility can have the following assumptions:

1. model without stacking , rotations of the products inside the shelf

2. facings variations being implemented as "AVATAR" ,

3. single objective function in rank and profit

4. simple linear objective and no non linear objective

5. we also assume no elasticity to be used -linear model

6. we also assume that product is being stacked behind its facing by the same product

7. don't elaborate on Height and depth constraints as equations in this document

8. Shelf width as equal.

9. shelf and section/fixture as rectangular entity only

10. Ignoring any implicit loss of space due to fixturing components.

In the present disclosure constraint type are defined as templates (including the composite ones) which are used by the merchandisers to create constraint instances which are dynamically converted into model equations as a part of dynamically generated model and then send to optimization with relevant input data to create an optimality state.

The present disclosure also proposes a simple and effective technique for identifying various constraint types needed, required in Micro space planning optimization, this method essentially creates a permutation and combination around key entities and decisions involved in the optimization of space. Some of the various parameters which act as an input to constraint type creator and identifier are as follows. This technique essentially de-normalizes, flattens or expands the constraint types.-expanded constraint type instead of normalized or abstracted constraint is easier to manipulate by the end user -merchandiser of the retailer.

The selection-relation constraint type family may comprise one or more of the following list of:

1. Item(s) Must | Must Not be Selected (From Groups)

2. Iteml (s), Item 2(s) Must together | Must Not be Selected together (From Groups)
3. Selection-Relation Constraint Types Family

4. Item(s) Must | Must Not Be selected based On from Item Group; where condition =<,<=,=,>,>=

5. Item can only be selected with X number or less facings

6. Item Can only have 1 or many possible rotational configurations
The positional-relational type constraints family may comprise one or more of: 1. Item(s) Must | Must Not be on shelves (#)(if selected)

1. One or many from Iteml(s), one or many from Item 2(s) Must | Must Not be together on shelves/Levels/Z-indexes

2. Item(s) Must | Must Not be on shelves and/or location(s)/Levels/Z-indexed

3. Item must be | Must Not be in particular vicinity -neighborhood(logical and physical neighbors) of shelf or location/Levels/Z-indexed groups or other item

4. Item Must | Must Not be at exact physical coordinates
Both of the above embodiments can be combined, mixed or grouped to create variety of constraint types possible, only few have been enumerated of many possible types.

The embodiment disclosed above depicts various constraint types for micro space planning that could be created involving item, item and shelf (both in singular or plural ) for eg. one constraint type could be "Must selection of the item" or could be "Must placement of the item to shelf' among many . All different combination of constraint types can be mined, we then shortlist and prioritized the important constraint types and researched, modeled those in micro or generic space planning. Possible relevant permutations and combination creating a unique de-normalized constraint types are many. Both embodiments disclosed above can be generalized and applied to generic space -time by substituting entity from a dimensional hierarchy in place of item.

In an embodiment of the present disclosure, Micro or Macro, Business Constraints Management-constraint editor is the ability provided to merchandisers to create a dynamic business constraint/rule instance to act as a input to specific run of POG optimization ; this is where the intelligence gets converted into dataset to be inculcated into optimization process and generate cluster-store-fixture-product specific more relevant and personalized optimized planograms ; this acts as a decision input to primary optimization and is very user friendly module to be used by end user such as buyer-merchandiser.

In another embodiment of the present disclosure, the Micro or Macro, Visual POG/floor plan Constraint Pattern Recognition, Mining, Extraction has facility to create or mine constraints, business rules automatically from the existing data such as POG, floor plan or POS sales.

In another embodiment of the present disclosure Micro or Macro CDT creation Module provides facility to merchandisers to create a guiding principles for consumer decision trees and automatically creation of constraints-rules based on priorities auto and manual Business Linkage:

facility to reconcile, link the business documents and entities such as assortment plan, set, POGs , fixtures , stores , clusters with meaningful linkages with various documents and their versions.

A sample pseudo code for micro space planning avatar in addition to all content on avatar can be performed as disclosed below:

1. Allow retail merchandisers to create valid "Avatars" of the products -per season , demographics or cluster

2. Allow retail merchandisers to specify different sales measures or rank per avatar of the product

3. Treat avatars as single internal dummy product being mapped to external product having the facings x width or as per that avatar of the product

4. Allow merchandisers to use these avatars of products in Input data, what ifs and for Optimization or as input argument to constraint types.

5. Generate a avatar specific constraints with one additional special constraint to only choose one(& not more than one) of the possible avatars of the products -i.e allow optimizer to prefer the one which maximizes the objectives with constraints -this makes sure that our basic premise of allowing only one product instance with most appropriate facings to be on the shelf

6. create optimal versions of the planogram, with avatars seamlessly of products (transforming real product to internal dummy product and transform it back as needed )

In another embodiment of the present disclosure, the problem of retail shelf space optimization: integrated-simultaneous retail assortment, macro and micro space and inventory planning can be solved by representing general information on x , y, z axis in conjunction with real or binary output indicator and objective function value represents the basic structure ;size depends upon its logical associations and logical constructs which make the s-grid , along with constraint maps are created as additional data restricting cell association to its space. The input can be Store Clusters/Stores and attributes Products/Categories/groups, attributes Fixtures/Sections, attributes Stores/Floor Plans- Association hierarchies and attributes, between store clusters/stores, floor plans, fixtures, products Constraint-rules Association fixture-presentation-assortment inventory rules hierarchy demand data. When these inputs are transformed as described in the method 100, outputs generated would be but not restricted to, 2d-3d-Location-space-hierarchy, product, time (optional) map Assortment-Time (optional) map maximizing the profit. Revenue and increasing the return on space.

In another embodiment of the present disclosure, generic domain independent space optimization can be achieved by accepting one or more inputs of generic space structure across which entity has to be allocated ,selected, assorted generic entity structure which needs to be allocated , generic attribute of entity and space, generic constraints associating entity/or its attribute with space, generic constraints associating entity with entity or its attributes, generic constraint associating space with space or its attributes, generic constraint associating entity groups with entity groups and representing general information on x , y , z axis in conjunction with real or binary output indicator and objective function value represents the basic structure ;size depends upon its logical associations and logical constructs which make the s-grid , along with constraint maps are created as additional data restricting cell association to its space. The generated output would be but not restricted to, 2d-3d-Location-allocatble space hierarchy, allocable entity, time (optional) map selection assortment-Time (optional) list.

In yet another embodiment of the present disclosure, generic domain independent multi¬dimensional, multi hierarchical optimization can be solved by accepting Entity dimension = entity dimension + entity hierarchy and time dimension = time dimension + time hierarchy and association hierarchy between space hierarchy - entity hierarchy are additional inputs along with hierarchies themselves, objective function also represents a hierarchical function. After accepting the inputs, the general information is represented on x, y, z axis in conjunction with real or binary output indicator and objective function value represents the basic structure; size depends upon its logical associations and logical constructs which make the s-grid, along with constraint maps are created as additional data restricting cell association to its space.

In another embodiment of the present disclosure, by performing the steps disclosed in the method 100, the following problems could be solved.

1. Techniques for cargo -container space optimization: placement of items inside the container and container inside a cargo ship for efficient placement, retrieval considering logistics cost and distribution network costs as well as product , container size , associativity , temperature or pressure considerations could be solved by, accepting inputs such as cargo container ship , allocable space attributes and ship attributes cargo container product-source and destination attributes -route-address data and attributes, cargo container source and destination storage capability, attributes and data, things- Products/Categories/groups and attributes, container-Fixtures(logical)/Sections(logical) data and attributes, cargo ship level/ship-Floor Plans- Association hierarchies and attributes, between cargo ship space clusters ,floor-ship plans , fixtures, things-products, port data -route data, route data , source supply data / things-products and destination demand data / thing-products, shipping yard and ship labor , shipping yard constraints, product movement cost data, product storage cost data, product fragility cost risk data, product shipping-supply data -product demand data at ports, constraint-rules Association for fixture-product containment -level-section, container-container association, container size restriction, contain type restrictions - inventory, route, destinations rules hierarchy.

2. Warehouse space and inventory optimization : allocation of items , carton and boxes into 3d space for efficient placement, retrieval and logistics of distribution with maximizing the space utilization and minimizing the movement, logistics costs by accepting inputs, warehouse Clusters/warehouse dimension and attributes, Products/Categories/groups and attributes, Fixtures/Sections and attributes, warehouse level/Floor Plans- Association hierarchies and attributes, between warehouse clusters-store clusters associations /stores ,floor plans , fixtures, products, store -warehouse-entity demand data, labor constraints, product movement constraints and costs Constraint-rules Association fixture- presentation-assortment - inventory rules hierarchy demand data -cluster and warehouse.

3. Office space optimization : allocation of space to various projects and its team members or departments across various office space structures keeping in mind logistics , security , proximity , associativity and isolation aspects of allocation by accepting inputs related to projects , segment teams data, departments data, employee master and roles data Seating floor plan data, space, project, departmental constraints and employee constraints

4. Budget-portfolio optimization: allocation of limited funds to various portfolios which are distributed across departments or tracks (cost-risks-returns maximization on portfolio) by accepting inputs related to budget data, portfolios and categorization data (available data) portfolio risk and returns data-functions (return -time function, risk function) budget, portfolio-category constraints.

5. Project funding -selection : selection , allocation of funds to various types of projects inside and across large multinational organization, (risks , costs and return maximization on projects) by accepting inputs related to Project cost-retum-risk data, projeet type -and attribute data projects data -hierarchy, funding data, fund groups.data , .fund availability data, fund allocation hierarchy data fund constraints, projects constraints, project category constraints.

In another embodiment of the present disclosure, retail merchandisers can define avatars of the product or use normal product themselves for performing the following steps disclosed. The retail merchandisers are allowed to create valid "Avatars" of the products -per season, demographics or cluster. Further, the retail merchandisers are allowed to specify different sales measures or rank per avatar of the product. Also, the avatars are treated as single internal dummy product being mapped to external product having the facings x width or as per that avatar of the product. The merchandisers are allowed to use these avatars of products in input data, what ifs and for Optimization or as input argument to constraint types. Furthermore, generating an avatar specific constraints with one additional special constraint to only choose one (& not more than one) of the possible avatars of the products -i.e. allow optimizer to prefer the one which maximizes the objectives with constraints -this makes sure that our basic premise of allowing only one product instance with most appropriate facings to be on the shelf. The last steps involve creating optimal version of the planogram with avatars seamlessly of products (transforming real product to internal dummy product and transform it back as needed).

In one or more embodiments of the present disclosure, it is preferred to apply certain constraints and relaxations. Few of the constraints and relaxations include but not limited to Same products goes behind in the facing

1. Same product goes beside as a one facing

2. Same product goes above as one facing

3. Same height product goes on same level

4. Same width product goes on same level

5. Same depth product goes on same level

6. Same height x width product goes on same level

7. Same height and depth product goes on same level

8. Same depth and width product goes on same level

9. All products with all its facing is one logical product

10. Each product can only be selected once

11. Empty space is null product of different variable length x width and depth

12. One product can span many space cell or one product can be allocated only to one space cell

13. All products can be rectangulaized to nearest shape in 2d or 3d

14. Products cannot be arranged on top of one another

15. Products which has to be arranged on top has to have lesser width than bottom product

16. Products can be rotated only in some of allowable ways for the best fit out of all possible arrangements resulting into new length x width and depth.
In another embodiment of the present disclosure, the space cell approximation strategies include

1. One cell can be one location or one location is made of many cells

2. One location is occupied by one logical product

3. No of locations horizontally cannot be greater than the shelf width/min size of logical or physical products

4. No of levels cannot be greater than the shelf height divided by min height of the logical or physical product

5. All cells are orthogonal in nature and all cell walls are parallel to container –shelf

6. All locations cells at one level have equal dimensions

7. All locations cells at one level have equal height

8. All locations cells at one level have equal width

9. All locations cells at one level have equal depth

10. All locations cells at one level have equal height x width dimensions

11. All locations cells at one level have equal height x depth dimensions

12. All locations cells at one level have equal depth x width dimensions

13. All cells are square or cube

14. All cells are rectangles of fixed sizes in dimensions

15. All cells at one level are same shape either cube or rectangle and optionally can have same one dimension

16. All cells at one level are of same dimensions

17. All space can be digitized or gridded into cells

18. All locations can be mapped onto cells

19. All locations can be classified as merchandisable or non-merchandisable cell units

20. No location can be occupied by more than one product unless , however one location can be made up of more than one cell and hence one product can be mapped to more than one cell

21. Logical locations increment irrespective of nulls or empty space which are null locations

22. All locations are tagged unique top to left and all the way down or with similar strategy.

Above one or many relaxations, approximation and assumptions create a variant of the base models dynamically inside the present disclosure so as to either make optimization possible and easier or to provide extra feature from model capability or accomplish faster running models.

The present disclosure for the base model creates only minimalistic approach and with following assumptions to arrive at the most simple model.

Fig. 2 is a block diagram illustrating a system 200 for space and resource optimization, in accordance with an embodiment of the present disclosure. The system comprises a user input 210, data receiving module 220, data transforming module 230, data processing module 240 and a data analyzing module 250.

The data receiving module 220 is configured to receive a plurality of inputs. The plurality of inputs can be but not restricted to at least one dimension, a reference data, at least one hierarchy, or a plurality of user provided constraints. There can exist at least one hierarchy present inside each of the at least one dimension defined as an input.

The system 200 further comprises a data transforming module 230 communicably coupled to the data receiving module 220 wherein the data transforming module 230"is further configured to transform the plurality of inputs to one or more of an algorithmic graph or a structural graph. the present embodiment of the present disclosure, the plurality of inputs is transformed* based On an algorithmic requirement or based on an internal application. The at least one hierarchy present inside at least one dimension is unfolded on a specified axis. Further, the plurality of data is. encoded on the specific axis in a s-cell structure axis. The plurality of data on the specific axis in the s-cell structure axis is solved. A plurality of decision variables can also be used while transforming the plurality of inputs. The plurality of decision variables can be a problem statement or an internal structure. Also, a plurality of constraints can be used for the step of transformation of the plurality of inputs. The plurality of constraints can be but not limited to a plurality of relaxations, or a plurality of approximations or a plurality of assumptions. The step of transformation can also be performed using the plurality of user inputs, wherein the plurality of user inputs can be but not restricted to a set of domain specific rules or a set of domain independent rules. Transformation is performed at the higher level constraint type into lower -levels ones by subsequent application for each higher level or extended constraint type into its relevant lower level constraints and then further covert them into our base or atomic constraints.

The system 200 further comprises a data processing module 240. The data processing module 240 is configured to receive input from the data transforming module 230 and perform an optimization run. In essence, when optimization runs, optimal picture that is rendered is (nonlinear or linear multi-dimensional -multi objective) is point on part of the internal canvas representing optimality, i.e internal Picture or structure =transformations + implicit-explicit domain specific and domain independent structural constraints + explicit domain picture constraints + implicit domain picture constraints and the algorithm is mimicked by creating a structure inside optimization. The data processing module 240 is also configured to create heuristics for parallelization. The heuristics can be for both implicit and explicit parallelization.

The last component of the system 200 includes a data analyzing module 250 configured to analyze an optimal result received from the data processing module 240. The optimal result generated from the optimization run is analyzed. In the step of analyzing the optimal results from the optimization run, a set of decisions or at least one output dimension or an optimal result from the plurality of decision variables are generated in combination or individually. Furthermore, a post transformation or reverse transformation is performed after analyzing the optimal results. Transformation is specific to problem type and algorithm and is created as a picture of the source or target universe of feasible solutions, picture is made up of cells-like pixels, super cell to organisms or may evolves like stem cells into any definite organ where s-cell encodes information with respect to dimensional data, hierarchical data, objective function data as a combined decision variable, transform the input to fit into that structure.

In view of the many possible embodiments to which the principles of our invention may be applied, we claim as our invention all such embodiments as may come within the scope and spirit of the following claims and equivalents thereto.

Claims

What is claimed is:

1. A method comprising:

receiving a plurality of inputs transforming the plurality of inputs into at least one or more of: an algorithmic graph; and a structural graph, based on a domain specific area using a processor; creating heuristics for parallelization; performing an optimization run; and analyzing an optimal result from the optimization run.

2. The method of claim 1 wherein the plurality of inputs is at least one or more of:

at least one dimension;

a reference data

at least one hierarchy; and

a plurality of user provided constraints.

3. The method of claim 1 wherein the step of transforming the plurality of inputs further comprises, transforming the plurality of inputs based on internal application.

4. The method of claim 1 wherein the step of transforming the plurality of inputs further comprises, transforming the plurality of inputs based on an algorithmic requirement.

5. The method of claim 2 wherein the at least one hierarchy is present inside each of the at least one dimension.

6. The method of claim 1 wherein the step of transforming the plurality of inputs further comprises, unfolding the at least one hierarchy inside the least one dimension on a specific axis.

7. The method of claim 6 further comprises encoding a plurality of data on the specific axis in a s-cell structure axis.

8. The method of claim 1 wherein the step of transforming the plurality of inputs further comprises, defining a plurality of decision variables appropriately based on:

a problem statement; and an internal structure.

9. The method of claim 1 wherein the step of transforming the plurality of .inputs further comprises, using a plurality of constraints, wherein the plurality of constraints is one- or more of:

a plurality of relaxations; a plurality of approximations; and a plurality of assumptions.

10. The method of claim 1 further comprises performing structural simplifications.

11. The method of claim 1 wherein the step of transforming the plurality of inputs further comprises, creating an implicit constraint to enforce the structural limitations.

12. The method of claim 1 wherein the step of transforming the plurality of inputs further comprises, accepting a plurality of user inputs.

13. The method of claim 12 wherein the plurality of user inputs is one or more of:

a set of domain specific rules; and a set of domain independent rules.

14. The method of claim 1 where in the step of creating heuristics for parallelization further comprises, creating heuristics for:

implicit parallelization; and explicit parallelization.

15. The method of claim 1 further comprises generating a dynamic model from a plurality of objectives, wherein the plurality of objectives is one or more of:

a single objective; and a multiple objective.

16. The method of claim 7 further comprises solving the plurality of data on the specific axis in the s-cell structure axis.

17. The method of claim 1 further comprises one or more of:

generating at least one implicit constraint file; and generating at least one explicit constraint file.

18. The method of claim 1 wherein analyzing the optimal results from the optimization run further comprises, generating one or more of:

a set of decisions;

at least one output dimensions; and

an optimal result from the plurality of decision variables.

19. The method of claim 18 further comprises performing one or more of:
a post transformation; and a reverse transformation.

20. A system comprising:

a data receiving module configured to receive a plurality of inputs;

a data transforming module communicably coupled to the data receiving module, the data transforming module further configured to transform the plurality of inputs to at least one or more of: an algorithmic graph; and a structural graph.

a data processing module configured to receive input from the data transforming module and perform an optimization run; and a data analyzing module configured to analyze an optimal result received from the data processing module.

21. The system of claim 20 wherein the plurality of inputs is one or mare of:

at least one dimension;

a reference data;

at least one hierarchy; and

a plurality of user provided constraints.

22. The system of claim 20 wherein the data transforming module is further configured to transform the plurality of inputs based on one or more of:

an internal application; and an algorithmic requirement.

23. The system of claim 21 wherein the at least one hierarchy is present in each of the at least one dimension.

24. The system of claim 20 wherein the data transforming module is further configured to unfold the at least one hierarchy inside the least one dimension on a specific axis and encode a plurality of data on the specific axis in a s-cell structure axis.

25. The system of claim 20 wherein the data transforming module is configured to define a plurality of decision variables based on one or more of:

a problem statement; and an internal structure.

26. The system of claim 20 wherein the plurality of inputs are transformed using a plurality of constraints, wherein the plurality of constraints is one or more of:

a plurality of relaxations; a plurality of approximations; and a plurality of assumptions.

27. The system of claim 20 wherein the data transforming module is configured to perform one or more of:

creating heuristics for implicit parallelization and creating heuristics for explicit parallelization;

performing structural simplifications; and

creating an implicit constraint to enforce a structural limitations.

28. The system of claim 20 wherein the data transforming module is configured to receive a plurality of user inputs, wherein the plurality of user inputs is one or more of:

a set of domain specific rules; and a set of domain independent rules.

29. The system of claim 20 wherein the data processing module is configured to generate a dynamic model from a plurality of objectives, wherein the plurality of objectives is one or more of:

a single objective; and

a multiple objective.

30. The system of claim 24 wherein the data transforming module is configured to solve the plurality of data on the specific axis in the s-cell structure axis.

31. The system of claim 20 wherein the data transforming module is configured to perform one or more of:

generating at least one implicit constraint file; and generating at least one explicit constraint file.

32. The system of claim 20 wherein the data analyzing module is further configured to analyze the optimal results from the optimization run, analyzing the optimal-results further comprises, generating one or more of:
a set of decisions;

at least one output dimensions; and

an optimal result from the plurality of decision variables.

33. The system of claim 32 wherein the data analyzing module is further configured to perform one or more of:

a post transformation; and a reverse transformation.

34. The system of claim 20 wherein the data processing module can be configured to reduce the optimization to a knapsack algorithm by accepting one or more of:

assortment;

pricing;

budget;

supply chain replenishment rules; and

case pack size to determine facings.

35. The system of claim 26 wherein the plurality of assumptions can be one or more of:

rotations of the products inside the shelf;

single objective function in rank and profit;

simple linear objective;

shelf width as equal; and

shelf and section/fixture as rectangular entity.

36. The system of claim 26 wherein the plurality of constraints can be one or more of:

a plurality of items selected from the group; at least two of the plurality of items together; and a plurality of items selected with lesser facings.