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1、附件2：外文原文（复印件）Combinatorial optimization and Green LogisticsAbstract 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 optimization problems. The paper particularly considers t
2、he topics of reverse logistics, waste management and vehicle routing and scheduling.Keywords Green Logistics、 Reverse logistics 、 Combinatorial optimization 、Waste management 、 Hazardous materials1 IntroductionGreen Logistics is concerned with producing and distributing goods in a sustainable way,ta
3、king 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 of pollution on the environment. Green Logistics activitie
4、s 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 environmental effects on the planet of human activity and curre
5、nt 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 considering targets for reduced emissions and other environmental mea
6、sures.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 consideration of the wider objectives and issues connected with Gr
7、een 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 article and so the focus of this paper is to concentrate o
8、n 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 significant effects on the environment when the results are
9、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 of a product and the possibilities of various forms of recy
10、cling. 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 Green Logistics objectives. Section 5 contains the final conc
11、lusions.2 Reverse LogisticsThere 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 way from used products no longer required by the user to prod
12、ucts 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, remanufacturing or disposal”. Later, the European Working Group on Re
13、verse 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 or use point, to a point of recovery or point of proper
14、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 stage of the supply chain.The reduction of waste that this
15、 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 models of logistics activities involving only forward flows
16、 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 model to determine the optimal investment policies for t
17、he 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 established the responsibility for the end of life products t
18、o 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 companies will increasingly have to think about incorpo
19、rating Reverse Logistics activities in their business operations.2.1 Location models used in Reverse LogisticsThere is a huge amount of research in facility location theory in general. However, in the literature we found relatively few papers on this topic applicable to Reverse Logistics (RL). Krikk
20、e (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 of a network graph and a transportation graph as basic inp
21、uts 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 using heuristic procedures. Fleischmann et al. (2000) rev
22、iewed 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 classified the product recovery networks in three sub-areas
23、: 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 location problems,in particular location-allocation models (
24、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 while the environmental costs associated with the designed n
25、etwork were often neglected.2.2 Dynamic lot-sizing problemThe 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 Wagner and Whitin 1958). The facility places orders for the
26、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 holding cost for each unit held in inventory per unit time. Giv
27、en 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 past since it was first introduced more than four decades ago
28、. 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 (1997), Johnson and Montgomery (1974) and Silver et al. (1996
29、). 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 stock may also be made using a remanufacturing operation that util
30、izes 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 . 1. The model studied makes the following assumptions: no dis
31、posal 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 holding costs. Two variants are considered. In the first it is assume
32、d 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 man
33、agementThe 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 planning. Waste management is a key process to protect the environment and c
34、onserve resources. In recent years, policies of governments towards waste management have focused on waste avoidance, reuse and recycling. As a result there has been significant progress in these management areas, particularly for the more developed nations. The environmental aspects of waste manage
35、ment means that activities concerning the transport of waste materials are clearly part of the Green Logistics agenda.4 Vehicle routing and schedulingThe Vehicle Routing and Scheduling Problem (VRSP) concerns the determination of routes and schedules for a fleet of vehicles to satisfy the demands of
36、 a set of customers. The basic Capacitated Vehicle Routing Problem (CVRP) can be described in the following way.We are given a set of homogeneous vehicles each of capacity Q, located at a central depot and a set of customers with known locations and demands to be satisfied by deliveries from the cen
37、tral depot. Each vehicle route must start and end at the central depot and the total customer demand satisfied by deliveries on each route must not exceed the vehicle capacity, Q. The objective is to determine a set of routes for the vehicles that will minimize the total cost. The total cost is usua
38、lly proportional to the total distance traveled if the number of vehicles is fixed and may also include an additional term proportional to the number of vehicles used if the number of routes may vary.The CVRP and many of its variants have been well studied in the literature since its introduction by
39、 Dantzig and Ramser (1959). Its exact solution is difficult to determine for large-scale problems as it is a member of the class of NP-hard problems. Specialised algorithms are able to consistently find optimal solutions for cases with up to about 50 customers; larger problems have been solved to op
40、timality in some cases, but often at the expense of considerable computing time.In practice, other variations and additional constraints that must be taken into consideration usually make the vehicle routing problem even more difficult to solve to optimality.So many solution procedures are based on
41、heuristic algorithms that are designed to provide good feasible solutions within an acceptable computing time, but without a guarantee of optimality.There are several books and survey articles that summarize different approaches and provide references to the large number of journal articles that hav
42、e been written on this topic (e.g., Golden and Assad 1988; Toth and Vigo 2001). There are many other research works about the classical CVRP. Some exact methods have been tailored for this problem (e.g., Laporte and Nobert 1987; Agarwal et al. 1989; Lysgaard et al. 2004; Fukasawa et al.2006). Others
43、 have proposed approximate methods and heuristics due to the complexity of the problem and the need to solve it in a reasonable computing time (see Gendreau et al.2002; Laporte and Semet 2002; Cordeau and Laporte 2004; Cordeau et al. 2005). Most of these approaches are based on local search techniqu
44、es.Most papers assume that the costs and times of traveling between the depot and the customers and between customers are known and fixed. They are either given or calculated using a shortest path algorithm on the graph or network representing the locations. In practice,the times and shortest paths
45、may vary, particularly by time of day.5 ConclusionsThis paper has described the field covered by Green Logistics and described some of the new problems that arise when the objectives considered are not simply economic, but involve wider environmental and social considerations too. There are many dif
46、ferent types of operational research models that have key roles to play in dealing with Green Logistics issues, but in this paper we have concentrated on describing areas where combinatorial optimization is central to the design of acceptable solutions. It is expected that as environmental factors a
47、ssume increasing importance, the effective use of combinatorial optimization theories and techniques will be needed to meet the challenges of new problems.There is a research consortium in the UK working on many different aspects of Green Logistics models and more information can be found on the web
48、site of the Green Logistics project. The Green Logistics project includes several work modules that relate to topics covered in this review such as reverse logistics and the effect of vehicle routing and scheduling policies on the Green Logistics agenda.附件1：外文资料翻译译文组合优化和绿色物流摘要：本文的目的是介绍绿色物流领域及描述通过组合优
49、化制定中出现的一些问题。本文重点介绍了逆向物流、 废物管理和物流配送车辆调度等问题。关键字: 绿色物流、逆向物流 、组合优化 、废物管理、危险物品1 引言绿色物流主要关注的是可持续的生产方式和货物的销售，重点考虑到环境和社会的因素。因此，绿色物流的目标并不只是关注物流政策的执行对经济的影响，还关注对社会具有的更加广泛的影响，如对环境污染的影响。绿色物流活动包括测量不同分销策略对环境的影响，减少物流活动中的能源使用量，减少废物，管理和处理物流对环境的影响。近年来关于人们在地球的活动和物流实践对环境造成的影响越来越受到关注。很多组织和企业开始测量他们碳的排放量，以便可以监视他们的活动对环境的影响。政府现正考虑减少排放和其它环保措施。因此不管是公司还是政府对绿色物流越来越感兴趣。传统物流模式的生产和分配都集中在约束业务成本，将其降至最低。但是考虑到更加长远的目标和与绿色物流有关的问题，就必须有新的工作方法和模式，其中也包含了一些有趣的，最新应用的研究模型。阐述这一领域内所有的研究模型将需要很长篇幅的文章，所以本文的重点是集中于一些在绿色物流的应用中出项的新的或者是修订的组合优化模型。对于这些组合优化的工作，希望对于那些组合优化的新
