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

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Volume 3 (2), December 2020, Pages 255-268

Romit S. Beed, Ankita Sarkar, Raya Sinha, Deboshruti Dasgupta

St. Xavier’s College (Autonomous), Kolkata, India, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

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.

Keywords:

Shelf Space Allocation, Optimization Problem, Simulated Annealing, Hyper-Heuristic.

DOI: https://doi.org/10.32010/26166127.2020.3.2.255.268

Reference 

Aarts, E., Korst, J., & Michiels, W. (2005). Simulated annealing. In Search Methodologies (pp. 187-210). Springer, Boston, MA.

Bingüler, H. E., Bulkan, S., & Ağaoğlu, M. (2016). A heuristic approach for a shelf space allocation problem. Journal of Management and Information Science, 4(1), 38-44.

Corstjens, M., & Doyle, P. (1981). A model for optimizing retail space allocations. Management Science, 27(7), 822-833.

Cox, K. K. (1970). The effect of shelf space upon sales of branded products. Journal of Marketing Research, 7(1), 55-58.

Curhan, R. C. (1972). The relationship between shelf space and unit sales in supermarkets. Journal of Marketing Research, 9(4), 406-412.

Deb, K. (1998, January). Genetic algorithm in search and optimization: The technique and applications. In Proceedings of International Workshop on Soft Computing and Intelligent Systems (pp. 58-87). Machine Intelligence Unit, Indian Statistical Institute Calcutta, India.

Dreze, X., Hoch, S. J., & Purk, M. E. (1994). Shelf management and space elasticity. Journal of Retailing, 70(4), 301-326.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.

Haupt, R. L., & Ellen Haupt, S. (2004). Practical genetic algorithms. John Wiley & Soons.

Hwang, H., Choi, B., & Lee, M. J. (2005). A model for shelf space allocation and inventory control considering location and inventory level effects on demand. International Journal of Production Economics, 97(2), 185-195.

Jubril, A. M. (2012). A nonlinear weights selection in weighted sum for convex multiobjective optimization. Facta Universitatis, 27(3), 357-372.

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.

Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992-1007.

Kotzan, J. A., & Evanson, R. V. (1969). Responsiveness of drug store sales to shelf space allocations. Journal of Marketing Research, 6(4), 465-469.

Laarhoven, P. J. M., Aarts, E. H. L., Lenstra, J. K. (1992). Job shop scheduling by simulated annealing. Operations Research, 40(1), 113-125.

Murray, C. C., Talukdar, D., & Gosavi, A. (2010). Joint optimization of product price, display orientation and shelf-space allocation in retail category management. Journal of Retailing, 86(2), 125-136.

Ombuki, B., Ross, B. J., & Hanshar, F. (2006). Multi-objective genetic algorithms for vehicle routing problem with time windows. Applied Intelligence, 24(1), 17-30.

Russell, R. A., & Urban, T. L. (2010). The location and allocation of products and product families on retail shelves. Annals of Operations Research, 179(1), 131-147.

Wang, Z., Geng, X., & Shao, Z. (2009). An effective simulated annealing algorithm for solving the traveling salesman problem. Journal of Computational and Theoretical Nanoscience, 6(7), 1680-1686.

Yang, M. H., & Chen, W. C. (1999). A study on shelf space allocation and management. International Journal of Production Economics, 60, 309-317.

Zadeh, L. (1963). Optimality and non-scalar-valued performance criteria. IEEE Transactions on Automatic Control, 8(1), 59-60.