Raf Atama Yönetimi için Tamsayılı Programlama Destekli Meta-Sezgisel Algoritmalar

Abstract

Retail shelf space management, which is one of the most complex aspects of retailing, can be defined as determining when, where and in what quantities products will be displayed and dynamically updating the display considering changing market conditions. Although it is an important problem, research papers that study rectangular arrangement of products to optimize profit are limited. In this paper, we determine rectangular facing units of products to maximize profit for shelf space allocation and the display problem. To solve our two-dimensional shelf space allocation problem, we develop two matheuristic algorithms by using integer programming and genetic algorithm (TP-GA) and integer programming and firefly algorithm (TP-ABA) meta-heuristics together. The performances of the mathheuristics were compared with a real-world dataset from a bookstore. TP-GA and TP-ABA methods were able to generate near-optimal solutions with an average of 4.47% and 4.57% GAPs, respectively. We can also solve instances up to 900 products. These matheuristic algorithms, which are successful in the two-dimensional shelf assignment problem, can also be used to solve similar problems such as allocation of books in a bookstore, allocation of product families in a grocery store, or display of advertisements on websites.