Batch Order Picking Optimization Using Ant System

Batch Order Picking Optimization Using Ant System

Copyright: © 2014 |Pages: 19
DOI: 10.4018/978-1-4666-4908-8.ch012
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Abstract

This chapter concentrates on the batch order picking for remanufactured product distribution. The chapter starts with an introduction about the issue of secondary sales channels that arise in the remanufactured product redistribution phase and the delivery-oriented service strategy in remarketing. Then, the related studies in the literature are discussed in the background section. Next, the focal problem of this chapter is stated in the problem statement section. A detailed description about the approach (i.e., ant system and MAX-MIN ant system) can be found in the proposed methodology section. Right after this, an illustrative numerical example is discussed in the experimental study section. The potential research directions regarding the main problem considered in this chapter are highlighted in the future trends section. Finally, the conclusion drawn in the last section closes this chapter.
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Introduction

The final stage of a remanufacturing process is redistribution or remarketing, i.e., replacing or selling remanufactured products again to customers. Some researchers (e.g., (Dowlatshahi, 2000; Guide & Jayaraman, 2000; Mitra, 2007)) pointed out that the remanufactured products are often offered at a reduced price but still profitable for the companies, even though the remanufactured products are sold only to a number of smaller niche markets (Guide & Jayaraman, 2000). Successful examples include Xerox, Kodak, AT&T, IBM (Guide, Jayaraman, & Linton, 2003; Guide, Jayaraman, Srivastava, & Benton, 2000).

However, according to a recent research (Atasu, Guide, & Wassenhove, 2010), the authors concluded that remanufactured products may cannibalize the new products’ sale. To solve this challenge, one area of growing interest is that of secondary sales channels (e.g., second-hand market, aftermarket, and emerging market), which are separate (or parallel) from the primary channels (Atasu et al., 2010). For example, in (McKenna, Reith, Cail, Kessler, & Fichtner, 2013), the authors pointed out that, by 2008, the automotive aftermarket had a volume of nearly 20 billion Euro in Germany. By using agent technologies and a learning search algorithm, in (Jiang, Xu, & Sheng, 2010), the authors studied the pricing strategy problems via indirect retailer channels and direct Internet channels. Also, in (Kwak, Kim, & Thurston, 2012), the authors presented two case studies (i.e., laptop computers and mobile phones) to emphasize the value of both new and remanufactured product sales in second-hand market. Similarly, the opportunities in the secondary markets for hybrid remanufacturing/manufacturing systems was analyzed in (Gallo, Gerra, & Guizzi, 2009).

Whether or not the handling of cannibalization should be done with that secondary sales channel is one of the issues to be addressed by the quality or logistics services level that are offered, such as rapidity, reliability in the deliveries, stock availability and flexibility. Service, especially in the area of distribution, has been seen as a competitive strategy (Daugherty, Stank, & Ellinger, 1998). In this chapter, we focused on the warehouse service during the redistribution process, which is a key component of improving the whole logistics service level. In particular, we selected the batch order picking (BOP) issue as our starting point due to it is one of the most essential functions in warehouse management (F. Chen, Wang, Qi, & Xie, 2013; R. De Koster & Poort, 1998). We formulated the BOP as the classic travelling salesman problem (TSP) that considers not only the total travel distance but also the total travel time. To evaluate our model, we proposed two algorithms (i.e., ant system (AS) and MAX-MIN ant system (MMAS)) to solve the problem. The comparison results showed that the proposed method improve the warehouse operations’ efficiency.

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