Green Logistics Management

Green Logistics Management Green logistics can be defined as coordinating the general logistical activities comprising freight transport, storage, inventory management, materials handling and all the relevant activities required to move products through the supply chain in a way that meets customer requirements at minimum cost with an environmental concern. The main objective is examining different possible ways to reduce the external costs of logistics associated mainly with climate change, air pollution, noise etc and achieving a more sustainable balance between economic, environmental and social objectives. 2. Literature review on green logistics Researchers have been working on various mathematical techniques, heuristics, combinatorial optimization and practical methods for developing new and more sustainable methodologies to reverse logistics for the collection, recycling and disposal of waste products. With the help of some illustrative examples, the project is focussed on developing basic understanding of how new techniques and the operating practices could contribute to effective domestic waste management. Peirce and Davidson (1982) used a linear optimization technique to formulate the problem of transportation routing among transfer stations, disposal facilities, and long term storage impoundments but limiting the model to determination of cost effective waste transportation routes. Jennings and scholars (1984) formulated the regional hazardous waste management system as simply a vehicle routing problem aiming for either reducing cost or risk. Zografos and Samara (1990) dealt with the problem of a single type of waste to achieve the objectives of minimizing transportation risk, travelling time and disposal costs but, the demerits are that each centre is affected only by its closest facility and all the different source points can send its hazardous waste to only one treatment facility. Hu et al (2002) made use of a linear programming model to investigate the cost reduction of decision making support system used for managing the multi source waste reverse flows again limiting the objective only to cost factor. Alumur and kara (2007) used Multi-objective mixed integer programming model with dual objectives of minimizing cost and risk factor in hazardous waste logistics. They focussed mainly on the factors that decide the appropriate location for treatment facilities, dumping sites and the relevant technology needed to route various sources of waste subject to constraints. The considered model was implemented in Central Anatolian region of Turkey. Their research shed light on using multi-period concept to the existing model to schedule the processing of different types of waste. 2.1 Domestic waste management Though the above research work was quite old, it contributed much towards the waste management. They made a foundation for further research where we can combine both the objectives of cost reduction as well as environmental benefits. Solid waste is a critical environmental problem in both developed and developing countries. The growing environmental concern from citizens, governments and various industrialists demand new methods and technologies to address the problems involved in waste management that pose a threat to the environment. Domestic waste logistics is one of the key areas that could have a huge impact on the environment with the growing population if not given enough attention. The very common problem in developing countries regarding solid waste management is lack of sufficient technical and financial resources. The available resources can think of only collection and logistics costs, leaving no resources for safe final disposal (Collivignarelli et al., 2004). The green logistics objective can be served in several ways addressing sustainable domestic waste disposal. Some of them are mentioned below: What collection system should be applied for easy disposal of different types of wastes such as recyclable and non-recyclable? Where to locate the garbage accumulation areas and collection points? How big the fleet of vehicles should be and how feasible it is to have multiple compartment vehicles to collect different sources of waste separately at one time? How many containers and of what type should be assigned to each area? Which are the most appropriate collection routes depending on the demand, traffic and other practical constraints? What frequency of collection should be applied to each area? There has been a significant amount of research work going on addressing one or more of the above problems with the green logistics objective. For an extensive discussion on green logistics objectives one can refer to Sheu 2007, which is the recent review published in this area where they used coordinated reverse logistics management system which was formulated as multi-objective linear programming model for treating hazardous waste. The time varying waste collection amount associated with each given waste type was regulated by reverse logistics system and this was further coordinated with other activities such as storage, processing, distribution and final treatment. By using these two factors, the author could successfully design a model that searches for system-wide optimization condition considering both the reverse logistics operational costs and also the environmental impact through risk constraints. The model saved 58% operational costs with the inclusion of green logistics ob jective comparatively with the earlier versions of other authors where only a cost factor was considered. Though the above research work proves to be beneficial from cost as well as environmental perspective, is it really the same in every case? As the objective starts shifting the full attention towards green, economical and more of an environmental friendly objectives, the long term results sometimes have to be compromised although it proves more costly. There are various functional elements involved in the waste management such as waste generation, collection, separation, handling, storage and treatment, logistics and final dumping. In general, the same problem requires different objectives depending upon whose requirement it is. From business perspective, the best solution would be the one with the least cost, while for the government the best solution would be the one with the least risk (referring to human life). Therefore, for any proposed mathematical model, there should always be a compromise solution considering these different objectives. Conceptual approach by Chang and Davila (2007) made a great success in diverting recyclables, green waste from the municipal solid waste streams to energy, composting and recycling facilities. They analysed the existing solid waste management strategies for better improvement using minimax regret optimization techniques with multiple criteria. Researchers also shifting their attention towards using Life cycle analysis for evaluating different strategies involved in waste management. Ahluwalia and Nema (2007) presented a life cycle based multi-objective model to support decision makers in integrated waste management. They evaluated the management budget and life cycle of different types of computer waste for different objectives of cost, business risk and environmental impact. The main idea of Life cycle approach is recycling computer waste which otherwise leads to the loss of potential resources and can have a huge impact on the environment as well. Sbihi and Eglese (2007) mentioned the importance of multi-time step model in Combinatorial optimization and Green Logistics. They highlighted the variation in waste generation at any source node with time and about uncertainty with the data related to waste generation. In response to that, Ahluwalia and Nema (2007) identified the factors responsible for the computer waste and their contribution to the environmental pollution. Multiple objectives of economy, health and environmental risk involved with various computer waste management activities were assessed with the help of an integer linear goal programming based multi-time step optimal material flow analysis model. Several treatment and disposal facilities were selected and assigned optimum quantities of waste to them along chosen transportation routes, depending on different priorities to cost and risk. The uncertainty factor related to waste generation quantities also taken into consideration using Monte Carlo simulation. There are also some studies in the literature that are concerned only with the vehicle routing problem but the research objective seem to be very interesting from waste management perspective. These studies attempt to find the best possible routes for a given network with the objective of minimizing transportation cost subject to various constraints. Part of the model developed in our project uses a multi-compartment vehicle in which different sources of domestic wastes can be collected separately at the collection point itself using several vehicles EL Fallahi et al 2008. Using this concept of multi-compartment vehicle for waste collection might reduce the burden of segregating them later at the dumping site. This also might reduce the cost involved in diverting the recyclable and non-recyclable wastes to their corresponding processing centres, making the waste collection process not only economically profitable but can also make the recycling process more environmental friendly. Some times depending on the type of objective that is involved, constraints play a critical role. For instance, while dealing with the logistics of hazardous wastes, public safety is a serious constraint without which the model will be invalid. Highly toxic wastes like by-products of nuclear power plants needed to be transported to a safer environment. YW Chen et al (2008) planned safest transportation of nuclear waste by integrating the multi-objective (minimizing the travel time, transportation risk and the exposed population) shortest route problem having actual road network attributes of GIS (geographic information systems) with environmental systems research institute (ESRI). 8. The importance of environmental protection resulted in a set of new waste management goals in the reverse logistics system planning. Pati RK et al (2008) formulated a mixed integer goal programming to study the inter-relationship between the multiple objectives of a recycled paper distribution network. The objectives considered are reduction in reverse logistics cost; product quality improvement through increased segregation at the source; and environmental benefits through increased waste paper recovery. The model has been illustrated through a problem at paper recycling in India. It says that the model can also be extended to other areas of reverse logistics systems involving conservation of natural resources such as recycling of plastic wastes. This model can also be used for determining the facility location, route and flow of various types of recyclable waste paper in the multi-item, multi-echelon and multi-facility decision making framework. Future research Including non-linearities and stochasticity of parameters in the above linear model. Extending the model to other reverse logistics problem areas involving the environmental issues and conservation of natural resources such as recycling of plastic wastes. - Third report 1. Lund and Clark II (2008) highlighted various transportation technologies and the link to stationary power generation that may help to reduce the impact of both energy and transportation sectors on global warming and climate change. He made a point based on the past literature that no single technology appears to be able to solve the carbon footprint problem on its own. This special issue focussed on the methodologies and practices applied to the analysis of coherent sustainable energy and transportation systems in order to reverse the climate change. He focussed on the information available from literature review, mainly on the use of electric and hybrid technologies in the transportation sector with renewable energy source. But it appears to me that even with the mass introduction of these technologies, the impact on environment may again increase with more electricity generation (exception for wind and hydro electric power). He mentioned in his paper that Professor Woodrow Clark discussed how green energy from renewable energy sources can play a significant role in protecting the environment while providing power for building and transportation. This issue is based on the presentation from special session on Sustainable Energy and Transportation Systems which was part of 4th Dubrovnik Conference on Sustainable Development of Energy, Water and Environment Systems, June 4-8, 2007, Dubrovnik, Croatia. 2. Siu 2007 reviewed a number of innovative light weight transit systems with the objective to serve as a reference to decision makers who are involved in the planning and design of sustainable transportation systems. Siu argues that building more roads to relieve traffic congestion achieves nothing more than encouraging more vehicles to use them, resulting in more carbon emissions. This paper again focussed on the use of latest technologies namely the battery-electric, hybrid-electric and fuel cells buses. It says that the electric drives are appealing the transit operators because of reduced or zero vehicle emissions and increased efficiency. With efficient use of these innovative transit technologies in the distribution and logistics sector would certainly help to achieve the goal of green logistics. 3. Wadhwa et al 2008 proposed a multi-criterion decision making (MCDM) model based on fuzzy set theory. It is a flexible decision modelling of reverse logistics system: A value adding MCDM approach for alternative selection which can be helpful in designing effective and efficient flexible return policy depending on various criteria. This fuzzy decision methodology provides an alternative framework to deal with the complexities involved in reverse logistics and giving the best decision strategy for product recovery system. It requires quantitative and qualitative evaluation based on criteria such as cost, time, legislative factors, environmental impact, quality and quality. This paper combines fuzzy based flexible MCDM and reverse logistics for alternate selections. Future research The model serves to enhance the progressive introduction of applying artificial intelligence future research in terms of developing a group decision support system. 4. EL Fallahi et al 2008. A memetic algorithm and a tabu search for the multi-compartment vehicle routing problem. Computers and Operations research 2008; 35: 1725-1741 A general vehicle routing problem where a customer can order different products which will be delivered using identical vehicles using several compartments, each compartment being dedicated to one product is considered in this paper. The author used two algorithms known as memetic algorithm with a post optimization phase based on path relinking and a tabu search to solve the above problem. Path re-linking is the method to combine intensification and diversification in tabu search. It mainly concentrates on the exploration of links connecting pairs of good solutions in search space hoping for better solutions along these paths. This technique is generally used after the tabu search metaheuristic. Tabu search is a heuristic method designed to guide other methods, including local search algorithms to escape local optima. Its distinctive feature is the use of a memory to search the best possible solutions subject to certain constraints like forbidden moves (tabu). The above algorithms are compared for both multi-compartment and single compartment and found that splitting the compartments improved the results on average. References: Ahluwalia PK, Nema AK. A Goal Programming Based Multi-Time Step Optimal Material Flow Analysis Model for Integrated Computer Waste Management. Journal of Environmental Informatics 2007; 10(2): 82-98 Ahluwalia PK, Nema AK. A life cycle based multi-objective optimization model for the management of computer waste. Resources, Conservation and Recycling 2007; 51: 792-826 Alumur S, Kara BY. A new model for the hazardous waste location-routing problem. 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