Heuristics for retail shelf space allocation problem with linear profit function

Abstract

PurposeShelf 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/approachThe paper develops three heuristics to solve a shelf space allocation problem. It compares three heuristics with existing heuristic using empirical study.FindingsIn 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/implicationsThe 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 implicationsThe proposed heuristics are applied to a case of existing retail store which gave more profit than the current allocation scheme.Originality/valueThe paper proposes new initial constructor and neighbourhood move strategy to develop efficient heuristic. Heuristics proposed in this paper are competitive with existing heuristics.