Complex Adaptive Logistics System Optimization Using Agent-Based Modelling and Simulation

Complex Adaptive Logistics System Optimization Using Agent-Based Modelling and Simulation

Copyright: © 2014 |Pages: 14
DOI: 10.4018/978-1-4666-4908-8.ch013
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Abstract

This chapter examines how to control the extreme events happening when a complex adaptive logistics system is implemented in used product remanufacturing, particularly in the used products transhipment stage. The chapter starts with an introduction about the necessity of introducing the complex adaptive logistics system. Then, the related studies dealing with similar issues 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., the agent-based modelling and simulation) can be found in the proposed methodology section. Right after this, an illustrative simulation 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 conclusions drawn in the last section close this chapter.
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Background

Reverse Logistics System

As one of the supply networks, RL system focused on taking back products from customers and recovering added value by reusing, recycling and/or remanufacturing (Sasikumar & Kannan, 2008a, 2008b, 2009). In (Mortiz Fleischmann, Hans Ronald Krikke, Rommert Dekker, & Simme Douwe P. Flapper, 2000), the authors pointed out that RL system comprises a series of activities including collection, inspection/separation, re-processing, disposal and re-distribution. In a similar vein, Guide and Wassenhove (2009) classified the activities of RL into three broad categories: frond-end issues of product collection and acquisition, engine issues of remanufacturing operations, and back-end issues of channel development and remarketing.

To illustrate how does the RL works, numerous case studies have been carried out. For example, Jayaraman, Guide, and Srivastava (1999) analyzed the logistics network of an electronic equipment remanufacturing company. In (S. Dowlatshahi, 2005), the author proposed a strategic framework that including cost, quality, customer service, environmental concerns and political concerns for the design and implementation of remanufacturing operations in RL. For more information in this regard, please refer to (Korchi & Millet, in press).

Furthermore, RL has had a significant economic impact on industry as well as society (Krumwiede & Sheu, 2002). In (Rogers & Tibben-Lembke, 2001), the authors pointed out that RL costs were estimated to account for approximately 9.9% of the U.S. economy. Despite the costs, companies appear very willing to accept RL due to it can be seen as an opportunity for enhanced customer support and the ultimate issue for profitability (Krumwiede & Sheu, 2002), such as high-tech companies have reduced inventories along with improving field engineer productivity by as much as 40% through appropriate handling of RL (Minahan, 1998). Moreover, RL also provides a green image to the firms by increasing the demand of conscious customer for their products (Demirel & Gökçen, 2008).

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