Teaching-learning based optimization (TLBO) with variable neighborhood search to retail shelf-space allocation

Abstract

Shelf space is a scarce and expensive resource in the retail industry because a large number of products compete for limited display space. Thus, shelf-space allocation is frequently implemented in shops to increase product sales and profits. In the past few decades, numerous models and solution methods have been developed to deal with the shelf-space allocation problem (SSAP). In this paper, a novel population-oriented metaheuristic algorithm, teaching-learning-based optimization (TLBO) is applied to solve the problem and compared with existing solution methods with respect to their solution performance. Further, a hybrid algorithm that combines TLBO with variable neighborhood search (VNS) is proposed to enhance the performance of the basic TLBO. The research results show that the proposed TLBO-VNS algorithm is superior to other algorithms in terms of solution performance, in addition to using fewer control parameters. Therefore, the proposed TLBO-VNS algorithm has considerable potential in solving SSAP.