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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。
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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