سیستم کارشناسی جامع / رویکرد تحقیق در عملیات برای بهینه سازی مشکلات مکان های دستگاه زباله سوز

This research aims at developing an integrated decision support system for the optimization of waste incinerator siting problems. In this integrated approach, both expert system and operations research techniques are used to model the siting problems of waste incinerator. Furthermore, an expert decision support system (EDSS) is implemented for the above problem and thus providing the decision makers a useful tool for decision-making. This EDSS is based on multi-criteria decision analysis in finding the best incinerator site by minimizing costs and environmental impacts. The proposed approach identifies a hierarchy of objectives for the siting problem. First of all, several potential sites need to be screened as a set of feasible alternative sites. Second, those alternative feasible sites will be further evaluated via the multi-criteria decision making methods. For the evaluation process, we solve a 0/1 combinatorial optimization problem at the upper level and proceed the multi-attribute utility function at the lower level to get the optimal solutions. An empirical application of a real world waste incinerator site selection existing in Taichung City, Taiwan is followed in the end. Computational results both of the cost minimization and of the whole systems are also provided.

The research considers the issue of the siting problems of a NIMBY (Not In My Back Yard) type facility location such as a waste incinerator in local areas. Considering the siting of waste incinerator facilities, which has become one of the most pressing problems in local government, various difficulties arise in the setting up process. The purpose of a siting problem is to identify particular locations for a given type of facilities or services. Within any region of interest, there are often several potential locations for a certain type of facility. The desirability of one location relative to another depends on a multitude of factors like economic and socioeconomic concerns, health and safety concerns, environment and public attitudes. A reduction of some objective obstacles will depend on the capacity of decision makers for justifying adequately the choice of areas that, because of their characteristics, are the most suitable for waste incinerators with considerable environmental and social impact. The objective of the research is to construct a decision support system that enhances optimized operations of the waste incinerator siting problems in order to satisfy customer demands with minimal transportation cost and environmental impacts. The system consists of an expert system and a mathematical model. The integrated approach of combining two fundamentally different decision-making techniques (the expert system (ES) and operations research (OR)) has been adopted because most planning and evaluation process and cognitive problems cannot be solved by either OR or ES techniques alone. The operator's critical decision-making processes usually involve both qualitative and quantitative information. Hence, both OR and ES techniques are needed for modeling these processes. Expert systems are used in problems where no mathematical models can be formulated to provide acceptable answers, but the knowledge of an experienced human expert can give a satisfactory solution. This technique has shown exceptional performance in inferential process control and evaluating when the working knowledge of the system is non-linear and incomplete. Operations research, on the other hand, can be used for problems in which well-constructed mathematical models are available, or can be developed. In this context, the development of an expert decision support system (EDSS) for the siting of waste incinerator facilities, seems appropriate. The research presented here documents certain aspects of the setting up of such system. The problem faced raises both theoretical and architectural concerns. From a theoretical viewpoint it is in fact well-known that the location and transportation problems that we gave to face can be modeled only by means of complex integer or mixed-integer programming techniques: this bars the possibility of a thorough analysis of large-sized problem instances such as the one under consideration [8], let alone in a multi-criteria framework. Speaking of the relevant literature of decision support systems, from the architectural standpoint, the need to integrate spatial data with advanced algorithmic techniques has given rise to a research niche in the context of DSSs, considering so called Spatial Decision Support Systems (SDSS) [3] and [4], concerned with how to integrate spatially referenced information in a decision making environment in order to positively affect the performance of an individual decision-maker. In particular, in recent years it has been shown how, by these means, spatially integrated DSS can be used to bridge the gap between policy makers and complex computerized models [1] and [5]. It is in this framework that we analyzed and designed a system to support public policymakers in the study of alternative facility location plans. On the other hand, expert system technology is well known as a tool to support planners in their decision-making processes in site planning and evaluation tasks. One good example is that O'Keefe [9] provides guidelines and methods for the evaluation of decision support systems, and suggests that expert systems can function as effective decision support systems, and can complete tasks at a level close to human performance. However, there are problems that expert systems based on human heuristics cannot solve. In the research studied here, human knowledge about the NIMBY facility siting problem is incomplete and uncertain, and an expert system that relies solely on expertise provided by site planners would provide poor guidance to users. By contrast, OR techniques are designed to cope with problems that involve constraints such as penalty costs, transportation costs, and planning seasons. In our research, expert system and mathematical model, the two techniques complement each other, and are combined to construct the expert decision support system to optimize waste incinerator siting problems. Other objectives of this research also include: documenting the planning knowledge of key and senior urban site planners and engineers, and using the expert system as a training tool for training new planners. This paper proceeds as follows: Section 2 presents the main algorithmic issues faced. Specifically, it presents the top-level single-criterion problem, the identification of efficient solutions and the formulation of the multi-criteria problem to be solved by advanced off-the-shelf techniques. Then, the integrated expert system/operations research solution for optimizing waste incinerator siting problems is presented. Section 3 discusses the establishment of the incinerator site selection expert decision support system. It also explains the knowledge acquisition and data analysis that precedes system development. Section 4 introduces the real-world empirical example we faced and presents the computational results we obtained. It discusses how the integrated system can function in the real-world operational environment. Section 5 contains a discussion of the conclusions we have drawn from our experience, and discusses some of the benefits of the integrated approach to decision making.

Our proposed system described above combines the element of operations research and expert systems. Operations research and expert systems technologies complement each other and are easily combined because of common characteristics. Both techniques are well suited for dealing with decision-making and other knowledge oriented problems. There are generally two main aims in constructing decision support systems. The first aim concerns the improvement of the quality of the decisions taken. This is attained in various ways. First, all the analytical structuring of the problem can stimulate constructive comparisons and provide a reference framework for identifying and solving the conflicts. Furthermore, they can produce a deeper knowledge of the problem, not obvious given its complex nature. Another important function is that of supplying a framework of integrating specialist information relative to the various disciplines involved in the problem. In a certain sense, the decision support system plays the role of the non-existent overall expert. The second aim of a formalized decision study is to supply technical documentation in support of decisions both in front of authorities and of public opinion. Decision support system, not only indicate what information was used and where it came from, but also how the information has been used and why this means that the decision taken is the best. In fact, decision support systems are not intended to replace the decision maker in solving the problem: they are constructed to help the user to make responsible and clearly documented decisions, which use the potential available as much as possible. The system described in the paper has been developed making both these elements clear. We present a fully developed system integrating advanced computational functionalities with complex database management, graphic presentation and interaction procedures. This paper specifically deals with the algorithmic issues faced in the project. The main point we like to stress is the decomposition of the efficient set identification procedure into two steps. This was possible, given the almost hierarchical nature of the different objectives that had to be considered, a situation common to several real-world applications where financial considerations are prominent but not all-inclusive. Our work testifies to the feasibility of the decomposed approach, the possibility to integrate advanced combinatorial optimization modules with off-the-shelf MCDA systems in order to provide full-featured decision support and the effectiveness of the resulting architecture in a complex real-world application. At last, we reach the purpose of utilizing this model to aid planners in determining the most appropriate waste incinerator sites to be selected, given a set of conditions and constraints; the ‘most appropriate’ set of sites is defined as the set that satisfies the customer demand at minimal costs.