SHELF SPACE OPTIMIZATION USINGMETAHEURISTIC ALGORITHMS

Efficient allocation of shelves in retail is essential to gain and maintain competitiveness. Shelf Space Allocation Problem (SSAP) is an extension of the knapsack problem; the objective is determination of the products and their locations on shelves to maximize expected profit. Various factors such...

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Bibliographic Details
Published in:International journal of information, business and management business and management, 2013-05, Vol.5 (2), p.210
Main Authors: Bilsel, Murat, Ayhan, M Batuhan, Bulkan, Serol
Format: Article
Language:English
Subjects:
Algorithms
Allocations
Competitive advantage
Genetic algorithms
Heuristic
Inventory
Knapsack problem
Mutation
Neighborhoods
Optimization
Population
Profit margins
Profit maximization
Profitability
Retail stores
Retailing industry
Studies
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Summary:Efficient allocation of shelves in retail is essential to gain and maintain competitiveness. Shelf Space Allocation Problem (SSAP) is an extension of the knapsack problem; the objective is determination of the products and their locations on shelves to maximize expected profit. Various factors such as location within the store, product adjacencies and number of facings allocated for that particular product affect profitability of a product. Due to the size and complexity of the problem, metahuristic methods are preferred. This manuscript provides two metaheuristic solution algorithms to the model proposed by Ayhan et al. (2007), which is an extension of the model introduced by Yang (2001). This study has shown that for problems of various sizes both Tabu Search (TS) and Genetic Algorithm (GA) can provide better solutions than the greedy algorithm proposed by Ayhan et al. [PUBLICATION ABSTRACT]
ISSN:2076-9202
2218-046X