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1、4OR (2007) 5:99–116 DOI 10.1007/s10288-007-0047-3INVITED SURVEYCombinatorial optimization and Green LogisticsAbdelkader Sbihi · Richard W. EgleseReceived: 23 February 2007 / Revised: 20 April 2007 / Published online
2、: 1 June 2007 © Springer-Verlag 2007Abstract The purpose of this paper is to introduce the area of Green Logistics and to describe some of the problems that arise in this subject which can be formulated as combinato
3、rial optimization problems. The paper particularly considers the topics of reverse logistics, waste management and vehicle routing and scheduling.Keywords Green Logistics · Reverse Logistics · Combinatorial opt
4、imization · Waste management · Hazardous materialsMSC classification (2000) 90C10 · 90C35 · 90C59 · 90C901 IntroductionGreen Logistics is concerned with producing and distributing goods in a sust
5、ainable way, taking account of environmental and social factors. Thus the objectives are not only concerned with the economic impact of logistics policies on the organization carrying them out, but also with the wider ef
6、fects on society, such as the effects of pollution on the environment. Green Logistics activities include measuring the environmental impact of different distribution strategies, reducing the energy usage in logistics ac
7、tivities, reducing waste and managing its treatment. In recent years there has been increasing concern about the environmental effects on the planet of human activity and current logistic practices may not be sustainable
8、 in the long term.A. Sbihi · R. W. Eglese (B ) Department of Management Science, Lancaster University Management School, Lancaster, LA1 4YX, UK e-mail: a.sbihi@lancaster.ac.ukR. W. Eglese e-mail: r.eglese@lancaster.
9、ac.uk123Combinatorial optimization and Green Logistics 101gramming model to determine the optimal investment policies for the copper industry in Chile. A key part of the model was to control air pollution through emissio
10、ns in the production process. Legislation within the European Community gives high importance to recycled products and, in some cases, it has established the responsibility for the end of life products to the manufacture
11、rs. For example, the Waste Electronic and Electrical Equip- ment (WEEE) Directive (2002/96/EC)1 deals with this. Such legislation is one of the drivers in establishing the importance of reverse logistics operations. Most
12、 European companies will increasingly have to think about incorporating Reverse Logistics activ- ities in their business operations.2.1 Location models used in Reverse LogisticsThere is a huge amount of research in facil
13、ity location theory in general. However, in the literature we found relatively few papers on this topic applicable to Reverse Logis- tics (RL). Krikke (1998) proposes some models for RL network design. He designs a model
14、 for a multi-product and multi-echelon situation. The model allows new facili- ties to be added with the corresponding cost functions when necessary. He proposes the design of a network graph and a transportation graph a
15、s basic inputs for his model. Barros et al. (1998) consider the problem of the recycling of sand (a subproduct of recycling construction waste) in the Netherlands. They propose a two-level location model for the sand pro
16、blem and consider its optimization using heuristic procedures. Fleischmann et al. (2000) reviewed nine published case studies on logistics network design for product recovery in different industries, and identified some
17、general char- acteristics of product recovery networks, comparing them with traditional logistics structures. They classified the product recovery networks in three sub-areas: re-usable item networks, remanufacturing net
18、works, and recycling networks. Other references deal with this topic (e.g., Krikke 1998; Sarkis 2001; Fleischmann 2001). Most of the models developed in this field are similar to the traditional loca- tion problems, in p
19、articular location-allocation models (see Kroon and Vrijens 1995; Ammons et al. 1999; Spengler et al. 1997; Marìn and Pelegrìn 1998; Jayaraman et al. 1999; Krikke et al. 1999, 2001; ?). In most of the models, t
20、ransportation and process- ing costs were minimized while the environmental costs associated with the designed network were often neglected.2.2 Dynamic lot sizing problemThe dynamic lot sizing problem in its simplest for
21、m considers a facility, possibly a warehouse or a retailer, which faces dynamic demand for a single item over a finite horizon (see Wagner and Whitin 1958). The facility places orders for the item from a supply agency, e
22、.g., a manufacturer or a supplier, which is assumed to have an unlim- ited quantity of the product. The model assumes a fixed ordering (setup) cost, a linear1 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:
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