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 optimization 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 with 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 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 environmental 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 considering 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 consideration 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 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 significant 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 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 Green 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 way 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, remanufacturing 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 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 stage 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 models 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 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 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 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 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 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 of 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 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 classified 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 location problems,in particular 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; Fleischmann et al. 2000). In most of the models, transportation and processing costs were minimized while 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 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 holding 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 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 (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 stock 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 . 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 holding 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 planning. Waste management is a key process to protect the environment and conserve 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 management means that activities concerning the transport of waste materials are clearly part of the Green Logistics agenda. 4 Vehicle routing and scheduling The Vehicle Routing and Scheduling Problem (VRSP) concerns the determination of routes and schedules for a fleet of vehicles to satisfy the demands of 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 central 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 usually 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 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 optimality 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 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 have 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 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 techniques. 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 may vary, particularly by time of day. 5 Conclusions This 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 different 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 assume 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 website 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 他们碳的排放量，以便可以监视他们的越来越受到关注。很多组织和企业开始测量 活动对环境的影响。政府现正考虑减少排放和其它环保。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模型将带来有趣的新挑战，同时该工作也可能对环境带来重大影响。这份文件的原始版本可以在史宾斯和列更斯(2007年)找到。它讨论了不同领域的有关绿色物流的议程。第二部分阐述了对逆向物流的担忧以及考虑到产品的整个生命周期和对各种可能性的模型的回收利用。第三部分是废物管理，包括了危险废物运输的模型、 滚装滚卸容器和家居废物收集。第四部分涉及车辆路径模型和绿色物流目标相关的问题。第五部分为最后的结论。 2 逆向物流 在文献中可以找到各种逆向物流的定义。例如，弗莱希曼等人(1997年) 称逆向物流是‖涵盖了在物流活动中从使用的产品‖，用户不再需要的产品及在市场中 再利用产品的过程。道拉图沙斯(2000年)逆向物流解释为‖制造商有系统地从消费者手中回收以前所提供的产品或部件来再循环、 再制造或处置的一个进程‖。后来，欧洲工作小组在逆向物流的研究中，列维朗，德克尔等人 (2004年)给出这一定义:在规划的过程中，实施和控制原材料的落后流动，在制品库存中，包装和成品从制造，分销到使用，恢复或适当处置的过程。在他们的书中，罗杰斯和连布克 (1999年) 简要地考虑逆向物流与绿色物流的区别。逆向物流应该从消费者手中获得一些商品或产品回到商品流供应链的早期阶段从而减少了废物，这肯定意味着逆向物流应列入绿色物流。例如，德布里托和范德兰(2003年) 说必须估计产品返回检查库存管理问题。但是会有的只涉及向前流动的其它类型那么就不可以被称为逆向物流，但如果它们包括环境方面的考虑，也将被列入绿色物流货物的物流活动。例如，蒙德沙因和席勒库特(1997年) 通过描述混合的整数线性规划模型来确定在智利铜工业的最优投资策略。通过在生产过程中排放的空气污染来控制模型的一个关键部分。欧洲共同体内部立法高度重视产品的循环再造，而且，在某些情况下，已 相关责任。例如，废电子和电气设备 (WEEE) 指确立了制造商在产品使用结束后的 令。这种立法是建立逆向物流业务的重要性的一种驱动。大多数的欧洲公司会越来越多的考虑将逆向物流活动纳入他们的业务操作之中。 2.1 区位模型在逆向物流中的应用 对于一般设施选址的理论研究已有很多。然而，我们发现在文献中关于逆向物流(RL)这一主题的论文却相对较少。库勒德库(1998)提出RL网络设计模型。他设计了一个为多产品和多级的情况下的模型。在必要时该模型允许添加附有相应的费用函数的新设施。他建议将网络图的设计与交通图一同作为他的模型输入的依据。巴罗斯等人(1998)考虑在荷兰(回收建筑废料的子产品)砂的回收问题。他们提出了两个级别的砂的选址模型问题，并考虑采用启发式过程对其进行优化。弗莱希曼等人(2000年)审查了九个发的关于产品在不同行业的回收物流网络设计的案例研究，确定了一些产品回收网络的一般性特点，并与传统的物流结构进行比较。他们产品分类回收网络在三个子地区:可重复使用的项目网络，再制造网络，和回收网络。 其他处理这个主题的相关引用(例如 ，克里克1998;萨尔基斯2001;弗莱施曼2001)。在这个领域中大多数模型的开发都类似于传统的选址问题， 在特定位置 的分配模型中(可以看到克朗和夫里延斯1995;安蒙斯等1999;斯彭勒等1997;马林和贝利格林 1998;贾亚拉曼等1999;克里克等1999， 2001;弗莱施曼等2000)。 在大多数模型、运输和加工成本已最小化，而环境设计网络的成本经常被忽略。 2.2 动态批量问题 关于它的动态形式有很多种，其中最简单的是，从设施方面考虑，在有限的资源条件下，仓库或者零售商，面临着单个项目的动态需求。(见瓦格纳，于1958年圣灵降临节)。该项目的设施场所是根据供应机构的订单来决定的。比如说，这是一个有无限量产品的制造商或供应商。该模型假设成本是按照一个固定顺序(所设置的)来支出的，采购成本为一个单位的线性支出，在单位时间内，库存都是具有线性持有成本的。考虑到时间的价值以及成本参数，问题在于在每个阶段的什么时候以及怎样订购设施才能使得在满足生产需要的同时使得成本最小化。 动态调整很多问题得到了很好的研究，因为它是第一个推出了超过四十年前的方法。精确解技术，称为基于动态规划的瓦格纳瓦锡 算法，是众所周知的生产规划与库存控制。有关此模型的详细信息，请参阅由布拉默尔和辛智列维书(1997年)，约翰逊和蒙哥马利(1974年)和银等(1996年)。各种启发式方法还提议，例如银粉银和餐中所述的启发式算法(1973)托特等人(2006年)认为很多规模模型是一种维修库存也可以是在制造的操作，利用回收和生产维修库存与新制造的库存并无区别。再制造的例子包括一次性使用的相机和复印机。可谓是再制造库存系统图。 1.研究的模型进行了以下假设: —— 没有回报的处理; —— 持有成本为服务成本大于持有成本的回报率 ; —— 变制造与再制造费用不包括在内。 我们的目标是再次尽量减少设置成本和持有成本的总和。这被认为是两种变体。第一个变量是假定建立两个程度相当的联合生产线，这将花费适当地费油。第二个变量是假定制造与再制造费用单独设置。 3 废物管理 由于固定废物生产被广泛认为有所增加再加上环境问题被日益关注，所以近年来，各国政府废物管理政策集中在避免产生废物、再利用和回收利用。因此在这些 管理领域，特别是对于较发达的国家方面取得了重大进展。在环境方面废物管理指有关废物材料的运输活动，显然这是绿色物流议程的一部分。 4 车辆路径与调度 车辆路径与调度问题(VRSP)涉及到确定路线和车队的时间表以满足客户的需求。可以按以下方式描述制约车辆路径基本能力的问题(CVRP)。我们在已知位置和的中央仓库设置车辆流量Q，以满足从中央仓库交货的要求。车辆的每个路径必须满足每个交付客户的需求，且不能超过车辆的能力。其目的是确定车辆，确定总成本最低的路线。如果车辆的数量是固定的，可能还包括一个额外的任期，如果路线的数量不同，那么与车辆数目成正比。 CVRP 和及其各种形式也曾泽范和拉姆泽 (1959年)中推出了文学研究。其确切的解决方案是很难确定大规模的问题，这是一个很难解决的问题。专门的算法能够始终如一的为高达50家客户找到最优的解决方案;虽然更大的问题已经解决，但是在某些情况下，往往最优性的代价就是花费相当长的时间。在实践中，其他的变化和附加约束，通常必须考虑到使车辆路径以及其最优性。有许多的解决方案过程基于启发式算法，旨在提供良好可行的解决方案，在可接受的计算时间内，但不保证最优性。 有几本书和调查文章来汇总不同的方法，并且在文章中有所引用(例如，黄金与阿萨德 1988年;托特和维 2001年)，这些在杂志撰写的文章中被大量引用。同时有很多关于CVRP的研究工作。对于此问题已定制的确切的几种方法 (如拉波特和诺贝特; 1987年阿格沃尔等1989 年; 里斯加德2004 年;深泽 等l2006 年)。有人建议用近似方法和启发式技术来解决复杂的问题和算出合理的时间(请参见 戈德诺等.2002 年;拉波特和塞姆特 2002年;科尔多和拉波特 2004年;科尔多 等2005 年)。大多数的这些做法基于本地搜索技术。 大多数论文认为车厂和客户之间行驶费用和时间是已知的，固定的。他们一般就是用表或者图形或者网络计算最小路径。然而在实践中，时间和最短路径可能有所不同，尤其是通过一天的时间。 5 结论 本白皮书描述了绿色物流所涉领域，并描述了一些新的问题出现时，所考虑的并不仅仅是经济，而是更加涉及到环境和社会因素。有许多不同类型的模型在处理 绿色物流问题中，扮演了关键角色，但在本文中，我们都集中在描述用组合优化模型解决及设计方案。据预计由于环境因素承担的重要性日益增加，组合优化模型和技术将面临更多的挑战。在英国，绿色物流模式有许多对不同方面有研究的联合会和在绿色物流项目的网站上可以找到详细信息 。绿色物流项目包括涵盖的这项讨论了逆向物流和物流配送车辆调度，绿色物流议程上的政策的影响等有关主题的几个模块。