Meta-Heuristics with Local Search for Retail Shelf Allocation Optimization

Publication Type

Journal Article

Publication Date

2004

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. [PUBLICATION ABSTRACT]

Keywords

retail, shelf allocation, metaheuristics

Discipline

Operations and Supply Chain Management

Research Areas

Operations Management

Publication

Management Science

Volume

50

Issue

1

First Page

117

Last Page

131

ISSN

0025-1909

Identifier

10.1287/mnsc.1030.0165

Publisher

INFORMS

Additional URL

https://doi.org/10.1287/mnsc.1030.0165

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