Swarm Intelligence Approaches to Shelf Space Allocation Problem with Linear Profit Function

Swarm Intelligence Approaches to Shelf Space Allocation Problem with Linear Profit Function

Tuncay Ozcan (Istanbul University, Turkey) and Şakir Esnaf (Istanbul University, Turkey)
Copyright: © 2016 |Pages: 20
DOI: 10.4018/978-1-5225-0075-9.ch002
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

The efficient management of shelf space carries critical importance on both the reduction of operational costs and improvement of financial performance. In this context, which products to display among the available products (assortment decision), how much shelf space to allocate the displayed products (allocation decision) and which shelves to display of each product (location decision) can be defined as main problems of shelf space management. In this paper, allocation problem of shelf space management is examined. To this end, a model which includes linear profit function is used for the shelf space allocation decision. Then, heuristic approaches are developed based on particle swarm optimization and artificial bee colony for this model. Finally, the performance analysis of these approaches is realized with problem instances including different number of products and shelves. Experimental results show that the proposed swarm intelligence approaches are superior to Yang's heuristics for the shelf space allocation model.
Chapter Preview
Top

Introduction

As the number of brand lines continually increases, shelf space is scarce and fixed resource for a retailer. This limitation of shelf space causes some decision problems such as which products should be displayed, how much shelf space should be allocated to the displayed products. These decisions have an important role on both the optimization of financial performance of the retailer and improvement of customer service level. On the other hand, marketing research show that most customer decisions are made at the point of purchase and product selection by customers may be influenced through in-store factors such as display locations (Irion et al., 2004). The observed behavior of customers indicates tendency to purchase more visible products. In particular, this would be valid under the assumption that customers do not have the tendency of purchasing a specific product before walking in to the store. However, the research shows that unplanned purchases by customers are rather common and they constitute at least one third of the overall sales of many retailers (Buttle, 1984). This behavior pattern emphasizes that shelf space is a significant resource of retail management for increasing sales.

Retail management aims to develop a retail mix that is able to effect customer purchasing decisions and satisfy their demands (Chen & Lin, 2007). To this end, shelf space management plays an important role in satisfying customer demand and changing their purchasing preferences. A retailer’s success depends on its ability to match its changing environment by continually deciding between how much of which products to shelve where and when (Hansen et al., 2010).

Having achieved an effective shelf space management, retailers are able to attract customer interest as well as preventing stock-outs and more importantly reducing operational costs while improving their financial performances (Irion et al., 2004). Retailers are most often confronted by these basic decision problems on the shelf space management:

  • Product Assortment: Which products to choose in display amongst other potential product ranges?

  • Shelf Space Allocation: How much shelf space should be allocated for the displayed products?

  • Shelf Location: Which shelves should be allocated in the store for the displayed products?

  • Inventory: What would be the best order time and order quantity for the displayed products?

The remainder of this study is organized as follows: the next section provides a detailed review of relevant theoretical literature. In the third part, a mathematical model used for shelf space allocation developed by Yang (2001) is explained. In the fourth part, the developed approaches based on artificial bee colony and particle swarm optimization for solving the model are detailed. The fifth part shows experimental results for different problems to compare the performances of the developed approaches. In the final part, the results of experimental design are discussed.

Top

Literature Review

Many studies can be found in the literature for the last 40 years on decision problems of retail shelf space management such as space allocation, product assortment, store layout and inventory. While some of these studies were focused on single particular problem of shelf space management; others suggested integrated models for the solution of two or more decision problems. The classification of these studies in the literature based on the type of decision problem is shown in Table 1. As could be seen in Table 1, a considerable part of the studies on the shelf space management are on shelf space allocation and product assortment decisions.

Complete Chapter List

Search this Book:
Reset