Assortment optimization with log-linear demand: Application at a Turkish grocery store. (September 2019)

• Record Type: Journal Article
• Authors: Hekimoğlu, Mustafa; Sevim, Ismail; Aksezer, Çağlar; Durmuş, İpek
• Is Part Of: Journal of retailing and consumer services. Volume 50(2019)
• Page Start: 199
• Page End: 214
• Publication Date: 2019-09
• Subjects: Retail trade -- Periodicals; Service industries -- Periodicals; Customer services -- Periodicals; Commerce de détail -- Périodiques; Service à la clientèle -- Périodiques; Customer services; Retail trade; Periodicals; 658.87
• DOI: 10.1016/j.jretconser.2019.04.007
• Languages: English
• ISSNs: 0969-6989

Abstract

Abstract: In retail sector, product variety increases faster than shelf spaces of retail stores where goods are presented to consumers. Hence, assortment planning is an important task for sustained financial success of a retailer in a competitive business environment. In this study, we consider the assortment planning problem of a retailer in Turkey. Using empirical point-of-sale data, a demand model is developed and utilized in the optimization model. Due to nonlinear nature of the model and integrality constraint, we find that it is difficult to obtain a solution even for moderately large product sets. We propose a greedy heuristic approach that generates better results than the mixed integer nonlinear programming in a reasonably shorter period of time for medium and large problem sizes. We also proved that our method has a worst-case time complexity of O ( n 2 ) while other two well-known heuristics' complexities are O ( n 3 ) and O ( n 4 ) . Also numerical experiments reveal that our method has a better performance than the worst-case as it generates better results in a much shorter run-times compared to other methods.