A DYNAMIC PROGRAMMING HEURISTIC FOR RETAIL SHELF SPACE ALLOCATION PROBLEM

2011 ◽  
Vol 28 (02) ◽  
pp. 183-199 ◽  
Author(s):  
HASMUKH K. GAJJAR ◽  
GAJENDRA K. ADIL
Keyword(s):  
Dynamic Programming ◽  
Optimal Solution ◽  
Retail Store ◽  
Allocation Problem ◽  
Shelf Space ◽  
Reasonable Time ◽  
Cpu Time ◽  
Local Search Heuristic ◽  
Space Allocation ◽  
Shelf Space Allocation

Shelf space allocation to products greatly impacts the profitability in a retail store. In this paper, we consider a retail shelf-space allocation problem where retailer wishes to allocate the available spaces of different shelves to a large number of products considering direct space elasticity in the product's demand. There is a great interest to develop efficient heuristics due to NP-hard nature of this problem. We propose a dynamic programming heuristic (DPH) to obtain near optimal solution in a reasonable time to solve this problem. The empirical results found that DPH obtained near optimal solutions for randomly generated instances of problems with size (products, shelves) varying from (100, 30) to (200, 50) within a few seconds of CPU time. The performance of DPH is benchmarked against an existing local search heuristic (LSH). It was found that DPH takes substantially less CPU time and attains a solution close to that obtained by LSH. Thus, DPH has great potential to solve the problem of realistic size within reasonable time. The proposed DPH is also applied to a case of an existing retail store.

scholarly journals A RETAIL CATEGORY MANAGEMENT MODEL INTEGRATING SHELF SPACE AND INVENTORY LEVELS

2009 ◽  
Vol 26 (04) ◽  
pp. 457-478 ◽  
Author(s):  
B. RAMASESHAN ◽  
N. R. ACHUTHAN ◽  
R. COLLINSON
Keyword(s):  
Optimal Solution ◽  
Management Model ◽  
Category Management ◽  
Shelf Space ◽  
Product Assortment ◽  
Space Allocation ◽  
Review Period ◽  
Proposed Model ◽  
Industry Practice ◽  
Shelf Space Allocation

A retail category management model that considers the interplay of optimal product assortment decisions, space allocation and inventory quantities is presented in this paper. Specifically, the proposed model maximizes the total net profit in terms of decision variables expressing product assortment, shelf space allocation and common review period. The model takes into consideration several constraints such as the available shelf space, backroom inventory space, retailer's financial resources, and estimates of rate of demand for products based on shelf space allocation and competing products. The review period can take any values greater than zero. Results of the proposed model were compared with the results of the current industry practice for randomly generated product assortments of size six, ten and fourteen. The model also outperformed the literature benchmark. The paper demonstrates that the optimal common review period is flexible enough to accommodate the administrative restrictions of delivery schedules for products, without significantly deviating from the optimal solution.

Swarm Intelligence Approaches to Shelf Space Allocation Problem with Linear Profit Function

2016 ◽  
pp. 22-41
Author(s):  
Tuncay Ozcan ◽  
Şakir Esnaf
Keyword(s):  
Swarm Intelligence ◽  
Allocation Problem ◽  
Profit Function ◽  
Allocation Decision ◽  
Shelf Space ◽  
Space Management ◽  
Bee Colony ◽  
Space Allocation ◽  
Problem Instances ◽  
Shelf Space Allocation

The efficient management of shelf space carries critical importance on both the reduction of operational costs and improvement of financial performance. In this context, which products to display among the available products (assortment decision), how much shelf space to allocate the displayed products (allocation decision) and which shelves to display of each product (location decision) can be defined as main problems of shelf space management. In this paper, allocation problem of shelf space management is examined. To this end, a model which includes linear profit function is used for the shelf space allocation decision. Then, heuristic approaches are developed based on particle swarm optimization and artificial bee colony for this model. Finally, the performance analysis of these approaches is realized with problem instances including different number of products and shelves. Experimental results show that the proposed swarm intelligence approaches are superior to Yang's heuristics for the shelf space allocation model.

A Multiobjective Evolutionary Algorithm for the Linear Shelf Space Allocation Problem

2008 ◽  
pp. 1001-1010
Author(s):  
Anna I. Esparcia-Alcázar ◽  
Ana I. Martínez-García ◽  
Jose M. Albarracín-Guillem ◽  
Marta E. Palmer-Gato ◽  
Juan J. Merelo ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Allocation Problem ◽  
Shelf Space ◽  
Multiobjective Evolutionary Algorithm ◽  
Space Allocation ◽  
Shelf Space Allocation

scholarly journals A STUDY OF THE EFFECT OF THE PARAMETERS FOR OPTIMIZING PROFIT USING SIMULATED ANNEALING TO SOLVE SHELF SPACE ALLOCATION PROBLEM

2020 ◽  
Vol 3 (2) ◽  
pp. 255-268
Author(s):  
Romit S. Beed ◽  
◽  
Ankita Sarkar ◽  
Raya Sinha ◽  
Deboshruti Dasgupta
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Programming Model ◽  
Limited Resource ◽  
Allocation Problem ◽  
Shelf Space ◽  
Combinatorial Optimization Problems ◽  
Space Allocation ◽  
Conflicting Objectives ◽  
Shelf Space Allocation

Shelf space allocation has always remained a crucial issue for any retail store, as space is a limited resource. This work proposes a model that uses a hyper-heuristic approach to allocate products on shelves to maximize the retailer's profit. This work has concentrated on providing a solution specifically for a consumer packaged goods store. There exist multiple conflicting objectives and constraints which influence the profit. The consequence is a non-linear programming model having a complex objective function, which is solved by using multiple neighborhood approaches using simulated annealing as simulated annealing is a useful tool for solving complex combinatorial optimization problems. Detailed analysis of the proposed technique of using annealing and reheating has revealed the effectiveness in profit maximization in the shelf space allocation problem. Various simulated annealing parameters have been studied in this paper, which provides optimum values for maximizing profit.

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