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Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization

Author

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  • Andrew Lim

    (Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

  • Brian Rodrigues

    (School of Business, Singapore Management University, 469 Bukit Timah Road, Singapore 259756)

  • Xingwen Zhang

    (Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

Abstract

Efficient shelf-space allocation can provide retailers with a competitive edge. While there has been little study on this subject, there is great interest in improving product allocation in the retail industry. This paper examines a practicable linear allocation model for optimizing shelf-space allocation. It extends the model to address other requirements such as product groupings and nonlinear profit functions. Besides providing a network flow solution, we put forward a strategy that combines a strong local search with a metaheuristic approach to space allocation. This strategy is flexible and efficient, as it can address both linear and nonlinear problems of realistic size while achieving near-optimal solutions through easily implemented algorithms in reasonable timescales. It offers retailers opportunities for more efficient and profitable shelf management, as well as higher-quality planograms.

Suggested Citation

  • Andrew Lim & Brian Rodrigues & Xingwen Zhang, 2004. "Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization," Management Science, INFORMS, vol. 50(1), pages 117-131, January.
  • Handle: RePEc:inm:ormnsc:v:50:y:2004:i:1:p:117-131
    DOI: 10.1287/mnsc.1030.0165
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    References listed on IDEAS

    as
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