Raf Atama Yönetimi için Tamsayılı Programlama Destekli Meta-Sezgisel Algoritmalar

Year 2022, Issue: 41, 100 - 117, 30.11.2022

Beray Bayazıt , Gülnihal Uçarkuş , Burcu Çağlar Gençosman , Mehmet A. Beğen

https://doi.org/10.31590/ejosat.1121006

Abstract

Perakendeciliğin en karmaşık yönlerinden biri olan perakende raf alanı yönetimi, ürünlerin hangi miktarlarda ne zaman ve nerede teşhir edileceğini belirlemek ve değişen piyasa şartlarını dikkate alarak belirlenen sergilemeyi dinamik olarak güncellemek olarak tanımlanabilir. Önemli bir problem olmasına rağmen ürünlerin dikdörtgensel yerleşimini maksimum kâr amacıyla gerçekleştiren çalışmalar sınırlıdır. Bu çalışmada raf alanı tahsisi ve sergileme problemi için ürünlerin dikdörtgensel sergilenme adetlerinin belirlenmesi ürün kalınlıkları da dikkate alınarak gerçekleştirilmiş ve kârın maksimizasyonu amaçlanmıştır. İki boyutlu raf alanı tahsisi problemi çözümü için tamsayılı programlama ile genetik algoritma (TP-GA) ve tamsayılı programlama ile ateş böceği algoritması (TP-ABA) meta-sezgiselleri birlikte kullanılarak iki matsezgisel algoritma geliştirilmiştir. Bir kitabevinden alınan gerçek veriler kullanılarak oluşturulan veri seti ile matsezgisellerin performansları karşılaştırılmıştır. TP-GA ve TP-ABA matsezgiselleri ile sırasıyla ortalama %4,47 ve %4,57 optimale yakın çözümler elde edilmiştir. Geliştirilen matsezgiseller ile 900’e kadar ürünlü problemler çözülebilmiştir. İki boyutlu raf atama probleminde başarılı olan bu matsezgisel yöntemler, kitabevinde kitapların yerleşimi, perakendecilikte ürün ailelerinin yerleşimi veya İnternet sitelerinde reklamların gösterimi gibi benzer özellik taşıyan problemlerin çözümünde de kullanılabilir.

Keywords

iki boyutlu raf alanı tahsisi problemi, tamsayılı programlama, genetik algoritma, ateş böceği algoritması, matsezgisel algoritmalar

Supporting Institution

TÜBİTAK

Project Number

217M920

Thanks

Bu çalışma, TÜBİTAK tarafından 3001 Programı kapsamında 217M920 numaralı proje ile desteklenmiştir.

Meta-Heuristic Algorithms based on Integer Programming for Shelf Space Allocation Problems

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.

Keywords

two-dimensional shelf space allocation problem, integer programming, genetic algorithm, Firefly algorithm, Matheuristic algorithms

Project Number

217M920

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