1、物流 外文翻译 外文文献 英文文献 组合优化和绿色物流物流 外文翻译 外文文献 英文文献 组合优化和绿色物流附件2:外文原文(复印件) Combinatorial optimization and Green Logistics Abstract 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 combinatorial o
2、ptimization problems. The paper particularly considers the topics of reverse logistics, waste management and vehicle routing and scheduling. Keywords Green Logistics、 Reverse logistics 、 Combinatorial optimization 、Waste management 、 Hazardous materials 1 Introduction Green Logistics is concerned wi
3、th producing and distributing goods in a sustainable 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 effects on society, such as the effects
4、 of pollution on the environment. Green Logistics activities include measuring the environmental impact of different distribution strategies, reducing the energy usage in logistics activities, reducing waste and managing its treatment. In recent years there has been increasing concern about the envi
5、ronmental effects on the planet of human activity and current logistic practices may not be sustainable in the long term.Many organizations and businesses are starting to measure their carbon footprints so that the environmental impact of their activities can be monitored. Governments are considerin
6、g targets for reduced emissions and other environmental measures.There is therefore increasing interest in Green Logistics from companies and governments.Traditional logistics models for production and distribution have concentrated on minimizing costs subject to operational constraints. But conside
7、ration of the wider objectives and issues connected with Green Logistics leads to new methods of working and new models,some of which pose interesting new applications for operational research models of various types. A survey of all operational research models in this area would require a very long
8、 article and so the focus of this paper is to concentrate on some of the new or revised combinatorial optimization models that arise in Green Logistics applications. For those working in combinatorial optimization it is hoped that these new models will pose interesting new challenges that may have s
9、ignificant effects on the environment when the results are applied.The original version of this paper can be found in Sbihi and Eglese (2007). It discusses different areas that relate to the Green Logistics agenda. Section 2 concerns Reverse Logistics models that take account of the full life-cycle
10、of a product and the possibilities of various forms of recycling. Section 3 covers Waste Management that includes models for the transportation of hazardous waste, roll-on roll-off containers and the collection of household waste. Section 4 deals with Vehicle Routing models and issues relating to Gr
11、een Logistics objectives. Section 5 contains the final conclusions. 2 Reverse Logistics There are various definitions of Reverse Logistics to be found in the literature. For example,Fleischmann et al. (1997) say that reverse logistics is a process which encompasses the logistics activities all the w
12、ay from used products no longer required by the user to products again usable in a market. Dowlatshahi (2000) explains Reverse Logistics as a process in which a manufacturer systematically accepts previously shipped products or parts from the point for consumption for possible recycling, remanufactu
13、ring or disposal. Later, the European Working Group on Reverse Logistics, REVLOG, Dekker et al. (2004), give this definition: The process of planning, implementing and controlling backward flows of raw materials, in process inventory, packaging and finished goods, from a manufacturing, distribution
14、or use point, to a point of recovery or point of proper disposal.In their book, Rogers and Tibben-Lembke (1999) briefly consider the differences between Reverse Logistics and Green Logistics. In Reverse Logistics there should be some flow of products or goods back from the consumer to an earlier sta
15、ge of the supply chain.The reduction of waste that this implies certainly means that Reverse Logistics should be included within Green Logistics. For example, De Brito and Van Der Laan (2003) examine inventory management issues when product returns must be estimated. However there will be other mode
16、ls of logistics activities involving only forward flows of goods that could not be described as reverse logistics, but if they include environmental considerations, will also be included within Green Logistics. For example,Mondschein and Schilkrut (1997) describe a mixed integer linear programming m
17、odel to determine the optimal investment policies for the copper industry in Chile. A key part of the model was to control air pollution through emissions in the production process. Legislation within the European Community gives high importance to recycled products and, in some cases, it has establ
18、ished the responsibility for the end of life products to the manufacturers. For example, the Waste Electronic and Electrical Equipment (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 European
19、companies will increasingly have to think about incorporating Reverse Logistics activities in their business operations. 2.1 Location models used in Reverse Logistics There is a huge amount of research in facility location theory in general. However, in the literature we found relatively few papers
20、on this topic applicable to Reverse Logistics (RL). Krikke (1998) proposes some models for RL network design. He designs a model for a multi-product and multi-echelon situation. The model allows new facilities to be added with the corresponding cost functions when necessary. He proposes the design o
21、f a network graph and a transportation graph as basic inputs for his model. Barros et al. (1998) consider the problem of the recycling of sand (asubproduct of recycling construction waste) in the Netherlands. They propose a two-level location model for the sand problem and consider its optimization
22、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 general characteristics of product recovery networks, comparing them with traditional logistics structures. They cl
23、assified the product recovery networks in three sub-areas: re-usable item networks, remanufacturing networks, 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 lo
24、cation problems,in particular location-allocation models (see Kroon and Vrijens 1995; Ammons et al. 1999;Spengler et al. 1997; Marn and Pelegrn 1998; Jayaraman et al. 1999; Krikke et al. 1999,2001; Fleischmann et al. 2000). In most of the models, transportation and processing costs were minimized wh
25、ile the environmental costs associated with the designed network were often neglected. 2.2 Dynamic lot-sizing problem The dynamic lot sizing problem in its simplest form considers a facility, possibly a warehouse or a retailer, which faces dynamic demand for a single item over a finite horizon (see
26、Wagner and Whitin 1958). The facility places orders for the item from a supply agency, e.g.,a manufacturer or a supplier, which is assumed to have an unlimited quantity of the product.The model assumes a fixed ordering (setup) cost, a linear procurement cost for each unit purchased, and a linear hol
27、ding cost for each unit held in inventory per unit time. Given the time varying demand and cost parameters, the problem is to decide when and how much to order at the facility in each period so that all demand is satisfied at minimum cost. The dynamic lot-sizing problem has been well studied in the
28、past since it was first introduced more than four decades ago. The exact solution technique, known as the Wagner- Whitin algorithm, based on Dynamic Programming is well known in production planning and inventory control. For more information about this model, see the books by Bramel and Simchi-Levi
29、(1997), Johnson and Montgomery (1974) and Silver et al. (1996). A variety of heuristic methods have also been proposed, for example the Silver-Meal heuristic described in Silver and Meal (1973). In Teunter et al. (2006) a variant of the basic lot sizing model is considered where the serviceable stoc
30、k may also be made using a remanufacturing operation that utilizes returns and produces serviceable stock that is indistinguishable from the newly manufactured stock. Examples of remanufacturing include single-use cameras and copiers. An inventory system with remanufacturing can be described in Fig
31、. 1. The model studied makes the following assumptions: no disposal option for returns; holding cost for serviceables is greater than holding cost for returns; variable manufacturing and remanufacturing costs are not included. The objective is again to minimize the sum of the set-up costs and holdin
32、g costs. Two variants are considered. In the first it is assumed that there is a joint set-up cost for manufacturing and remanufacturing which is appropriate when the same production line is used for both processes. The second variant assumes separate set-up costs for manufacturing and remanufacturing. We review these models in the next two sections. 3 Waste management The widely acknowledged increase in solid waste production, together with the increased concern about environmental issues, have led local governments and agencies to devote resources to solid waste collection policy
