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Heuristics for retail shelf space allocation problem with linear profit function

Heuristics for retail shelf space allocation problem with linear profit function Purpose – Shelf space is often retailer's critical resource. Growing number of products has posed a challenge to the retailers for efficient allocation of available shelf space to them. The paper aims to consider a retail shelf space allocation problem with linear profit function and aims to develop efficient heuristics to solve this problem. Design/methodology/approach – The paper develops three heuristics to solve a shelf space allocation problem. It compares three heuristics with existing heuristic using empirical study. Findings – In an empirical study of 320 randomly generated instances of problems with size (products, shelves) varying from (25, 5) to (200, 50), it was found that all three new heuristics are competitive with existing heuristic. The best amongst three heuristics found solution with average objective value of 99.59 percent of upper bound in a reasonable central processing unit time. Research limitations/implications – The linearity assumption of the profit function is based on earlier findings that marginal returns to space first increase and then decrease in an S‐shaped curve. Hence, linearity assumption for profit function is justified by the fact that retails would want to operate on linear (or approximately linear) and more strongly increasing part of the curve. Practical implications – The proposed heuristics are applied to a case of existing retail store which gave more profit than the current allocation scheme. Originality/value – The paper proposes new initial constructor and neighbourhood move strategy to develop efficient heuristic. Heuristics proposed in this paper are competitive with existing heuristics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Retail & Distribution Management Emerald Publishing

Heuristics for retail shelf space allocation problem with linear profit function

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References (21)

Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0959-0552
DOI
10.1108/09590551111109094
Publisher site
See Article on Publisher Site

Abstract

Purpose – Shelf space is often retailer's critical resource. Growing number of products has posed a challenge to the retailers for efficient allocation of available shelf space to them. The paper aims to consider a retail shelf space allocation problem with linear profit function and aims to develop efficient heuristics to solve this problem. Design/methodology/approach – The paper develops three heuristics to solve a shelf space allocation problem. It compares three heuristics with existing heuristic using empirical study. Findings – In an empirical study of 320 randomly generated instances of problems with size (products, shelves) varying from (25, 5) to (200, 50), it was found that all three new heuristics are competitive with existing heuristic. The best amongst three heuristics found solution with average objective value of 99.59 percent of upper bound in a reasonable central processing unit time. Research limitations/implications – The linearity assumption of the profit function is based on earlier findings that marginal returns to space first increase and then decrease in an S‐shaped curve. Hence, linearity assumption for profit function is justified by the fact that retails would want to operate on linear (or approximately linear) and more strongly increasing part of the curve. Practical implications – The proposed heuristics are applied to a case of existing retail store which gave more profit than the current allocation scheme. Originality/value – The paper proposes new initial constructor and neighbourhood move strategy to develop efficient heuristic. Heuristics proposed in this paper are competitive with existing heuristics.

Journal

International Journal of Retail & Distribution Management – Emerald Publishing

Published: Feb 15, 2011

Keywords: Retailing; Shelf space; Profit

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