A decision support system for urban waste management

This paper describes a decision support system, for urban waste management in a regional area, to be used for evaluating general policies for service organisation of the collection and for identifying areas suitable for locating waste treatment and disposal plants. The Decision Support System (DSS) is applied to a selected provinces in Sicily. The decision support system allows the generation and evaluation of suitable alternatives with respect to salient features of the problem, especially environmental consequences. The paper describes the identification and collection of relevant information, the structuring of a database, the design of combinatorial optimisation algorithms for solving the core location problem, the study of models for evaluating the different alternatives and their framing in a complete multicriteria decision model. Finally, the solution of the case study, by means of the DSS, is described.     

A heuristic solution method for node routing based solid waste collection problems

This paper considers a real world waste collection problem in which glass, metal, plastics, or paper is brought to certain waste collection points by the citizens of a certain region. The collection of this waste from the collection points is therefore a node routing problem. The waste is delivered to special sites, so called intermediate facilities (IF), that are typically not identical with the vehicle depot. Since most waste collection points need not be visited every day, a planning period of several days has to be considered. In this context three related planning problems are considered. First, the periodic vehicle routing problem with intermediate facilities (PVRP-IF) is considered and an exact problem formulation is proposed. A set of benchmark instances is developed and an efficient hybrid solution method based on variable neighborhood search and dynamic programming is presented. Second, in a real world application the PVRP-IF is modified by permitting the return of partly loaded vehicles to the depots and by considering capacity limits at the IF. An average improvement of 25% in the routing cost is obtained compared to the current solution. Finally, a different but related problem, the so called multi-depot vehicle routing problem with inter-depot routes (MDVRPI) is considered. In this problem class just a single day is considered and the depots can act as an intermediate facility only at the end of a tour. For this problem several instances and benchmark solutions are available. It is shown that the algorithm outperforms all previously published metaheuristics for this problem class and finds the best solutions for all available benchmark instances.     

An integrated approach for collection network design,capacity planning and vehicle routing in reverse logistics

This study considers network design, capacity planning and vehicle routing for collection systems in reverse logistics. The network design and capacity planning problems are to determine the static locations and capacities of collection points as well as the dynamic allocations of demand points to the opened collection points over a planning horizon, and the vehicle routing problem is to determine the number and routes of vehicles in such a way that each collection point must be visited exactly once by one vehicle starting and terminating at the depot while satisfying the return demands at collection points and the vehicle capacity. The objective is to minimize the sum of fixed costs to open collection points and to acquire vehicles as well as variable costs to transport returns at demand points to the opened collection points and travel the opened collection points by vehicles. Unlike the location-routing problems, the integrated problem considered in this study has several features: multi-period dynamic model, capacity planning for collection points, maximum allowable collection distances, etc. To solve the integrated problem, two types of tabu search algorithms, hierarchical and integrated ones, are suggested, and their test results are reported. In particular, the efficiency of the integrated approach is shown by comparing the two algorithm types.     

On the application of a transportation model for revenue optimization in waste management: a case study

This paper describes an interactive decision support system called Opti-Link which has been developed for a company operating in the area of waste and raw material management. Built around a specific transportation problem, the system is used to maximize the revenue generated by selling waste paper to paper mills. Furthermore, the dual variables of the linear program allow the planner to identify upper bounds for setting bid prices to buy waste paper from waste collection companies. First operational results indicate a significant increase in profit while at the same time the duration of the planning process could be cut by more than half.     

Effective location models for sorting recyclables in public management

The recycling of urban solid wastes is a critical point for the “closing supply chains” of many products, mainly when their value cannot be completely recovered after use. In addition to environmental aspects, the process of recycling involves technical, economic, social and political challenges for public management. For most of the urban solid waste, the management of the end-of-life depends on selective collection to start the recycling process. For this reason, an efficient selective collection has become a mainstream tool in the Brazilian National Solid Waste Policy. In this paper, we study effective models that might support the location planning of sorting centers in a medium-sized Brazilian city that has been discussing waste management policies over the past few years. The main goal of this work is to provide an optimal location planning design for recycling urban solid wastes that fall within the financial budget agreed between the municipal government and the National Bank for Economic and Social Development. Moreover, facility planning involves deciding on the best sites for locating sorting centers along the four-year period as well as finding ways to meet the demand for collecting recyclable materials, given that economic factors, consumer behavior and environmental awareness are inherently uncertain future outcomes. To deal with these issues, we propose a deterministic version of the classical capacity facility location problem, and both a two-stage recourse formulation and risk-averse models to reduce the variability of the second-stage costs. Numerical results suggest that it is possible to improve the current selective collection, as well as hedge against data uncertainty by using stochastic and risk-averse optimization models.     

Elective course planning

Efficient planning increasingly becomes an indispensable tool for management of both companies and public organizations. This is also the case for high school management in Denmark, because the growing individual freedom of the students to choose courses makes planning much more complex. Due to reforms, elective courses are today an important part of the curriculum, and elective courses are a good way to make high school education more attractive for the students. In this article, the problem of planning the elective courses is modeled using integer programming and three different solution approaches are suggested, including a Branch-and-Price framework using partial Dantzig-Wolfe decomposition. Explicit Constraint Branching is used to enhance the solution process, both on the original IP model and in the Branch-and-Price algorithm. To the best of our knowledge, no exact algorithm for the Elective Course Planning Problem has been described in the literature before. The proposed algorithms are tested on data sets from 98 of the 150 high schools in Denmark. The tests show that for the majority of the problems, the optimal solution can be obtained within the one hour time bound. Furthermore the suggested algorithms achieve better results than the currently applied meta-heuristic.     

Continuous-time method and its discretization to inverse problem of intensity-modulated radiation therapy treatment planning

We propose a novel approach for solving box-constrained inverse problems in intensity-modulated radiation therapy (IMRT) treatment planning based on the idea of continuous dynamical methods and split-feasibility algorithms. Our method can compute a feasible solution without the second derivative of an objective function, which is required for gradient-based optimization algorithms. We prove theoretically that a double Kullback–Leibler divergence can be used as the Lyapunov function for the IMRT planning system.Moreover, we propose a non-negatively constrained iterative method formulated by discretizing a differential equation in the continuous method. We give proof for the convergence of a desired solution in the discretized system, theoretically. The proposed method not only reduces computational costs but also does not produce a solution with an unphysical negative radiation beam weight in solving IMRT planning inverse problems.The convergence properties of solutions for an ill-posed case are confirmed by numerical experiments using phantom data simulating a clinical setup.     

Genetic algorithms for the bus driver scheduling problem: a case study

This paper describes an application of genetic algorithms to the bus driver scheduling problem. The application of genetic algorithms extends the traditional approach of Set Covering/Set Partitioning formulations, allowing the simultaneous consideration of several complex criteria. The genetic algorithm is integrated in a DSS but can be used as a very interactive tool or a stand-alone application. It incorporates the user's knowledge in a quite natural way and produces solutions that are almost directly implemented by the transport companies in their operational planning processes. Computational results with airline and bus crew scheduling problems from real world companies are presented and discussed.     

An interval nonlinear program for the planning of waste management systems with economies-of-scale effects—A case study for the region of Hamilton,Ontario, Canada

In practical environmental systems with the effects of economies-of-scale (EOS), most relationships among different system components are nonlinear in nature, which can be described precisely only if a nonlinear model is employed. In this study, an interval nonlinear programming (INLP) model is developed and applied to the planning of a municipal solid waste (MSW) management system with EOS effects on system costs. The INLP has a nonlinear objective function and linear constraints. It handles nonlinearity presented as exponential functions. When exponential term p = 1 (in the INLP’s objective function), the model becomes an interval linear program; when p = 2, it becomes an interval quadratic program. Therefore, the INLP is flexible in reflecting a variety of system complexities. A solution algorithm with satisfactory performance is proposed. Application of the proposed method to the planning of waste management activities in the Hamilton-Wentworth Region, Ontario, Canada, indicated that reasonable solutions have been generated. In general, the INLP model could reflect uncertain and nonlinear characteristics of MSW management systems with EOS effects. The modeling results provided useful decision support for the Region’s waste management activities.     

PROMISE: A DSS for multiple objective stochastic linear programming problems

Most of the multiple objective linear programming (MOLP) methods which have been proposed in the last fifteen years suppose deterministic contexts, but because many real problems imply uncertainty, some methods have been recently developed to deal with MOLP problems in stochastic contexts. In order to help the decision maker (DM) who is placed before such stochastic MOLP problems, we have built a Decision Support System called PROMISE. On the one hand, our DSS enables the DM to identify many current stochastic contexts: risky situations and also situations of partial uncertainty. On the other hand, according to the nature of the uncertainty, our DSS enables the DM to choose the most appropriate interactive stochastic MOLP method among the available methods, if such a method exists, and to solve his problem via the chosen method.     

Solid Waste Collection: A Case Study

Planning for urban solid waste management requires an assessment of many complex interactions among transportation systems, land use patterns, urban growth and development, and public health considerations. The authors discuss the application of a data acquisition and analysis system and a simulation model to the complex problems of the solid waste management system in Cleveland, Ohio. As the result of applying these techniques, Cleveland's solid waste managers reduced their annual budget of $14.8 million in 1970 to a low of $8.8 million in 1972. The total savings in a 3-yr period were $14.6 million based on the 1970 budget. In addition, the city reduced its solid waste work force from 1640 to 850 and the number of collection routes from 224 to 102, and it redesigned its collection routing system. The management structure of the Division of Solid Waste Collection and Disposal was completely reorganized to take advantage of and to supplement these major organizational changes. The City Council has, moreover, passed an ordinance which will allow the construction of transfer stations, as recommended in the study; one such facility has been built, and land has been purchased for the other.     

Metaheuristics with disposal facility positioning for the waste collection VRP with time windows

This paper improves heuristic algorithms presented in Benjamin and Beasley (Comput Oper Res 37(12):2270–2280, 2010) for solving the waste collection vehicle routing problem with time windows, particularly the real life waste collection benchmark problems from Kim et al. (Comput Oper Res 33(12):3624–3642, 2006). These consist of ten test problems, involving up to 2,092 customers and 19 waste disposal facilities. The main difference between this paper and Benjamin and Beasley (Comput Oper Res 37(12):2270–2280, 2010) is that here we use a disposal facility positioning (DFP) procedure to evaluate routes for our algorithms. Since the problem involves multiple disposal facilities, the objective of DFP is to choose the best disposal facilities to go on the vehicle route. Computational results indicate that our algorithms with DFP produce substantially better quality routes than previous approaches in the literature.     

Map-Route: a GIS-based decision support system for intra-city vehicle routing with time windows

This paper presents a Decision Support System (DSS) that enables dispatchers–schedulers to approach intra-city vehicle routing problems with time windows interactively, using appropriate computational methods and exploiting a custom knowledge base that contains information about traffic and spatial data. The DSS, named Map-Route, generates routes that satisfy time and vehicle capacity constraints. Its computational engine is based on an effective heuristic method for solving the underlying optimization problem, while its implementation is developed using MapInfo, a popular Geographical Information System (GIS) platform. Map-Route provides very efficient solutions, is particularly user-friendly, and can reach answers for a wide variety of ‘what if’ scenarios with potentially significant cost implications. We have implemented Map-Route in an actual industrial environment and we report on the experience gained from this real-life application.     

制造商竞争环境下逆向供应链的政府奖惩机制研究

主要运用博弈论方法探讨制造商竞争环境下的逆向供应链奖惩机制,建立了5个决策模型,分别是逆向供应链集中式决策、以逆向供应链和不回收再制造的制造商的总利润为目标决策、逆向供应链分散式决策、政府对制造商实施奖惩机制以及政府对回收商实施奖惩机制下逆向供应链的决策模型。研究表明:竞争对回收率提高有益,竞争越激烈回收率越高;积极回收再制造的制造商的新产品零售价较低,具有价格竞争优势;奖惩制造商和奖惩回收商均能起到提高回收率的作用,奖惩力度越大,回收率越高,新产品零售价越低;奖惩制造商比奖惩回收商更能调动制造商和回收商的积极性;奖惩制造商时的回购价高于奖惩回收商时的回购价;在实施奖惩机制时,回收再制造的制造商利润高于不回收再制造的制造商的利润;不回收再制造的制造商的利润随奖惩力度的增大而降低。     

Algorithms of motion planning for a six-legged walking machine

This paper investigates algorithms of motion planning for a six-legged walking machine over complex terrain. Application of the Rodrigues-Hamilton parameters for describing the orientation of the body enables one to develop algorithms of motion planning for the body and legs in an absolute coordinate frame with automatic adaptation to the surface. Experiments with a laboratory scale walking machine have demonstrated the efficiency of proposed algorithms. __________ Translated from Fundamentalnaya i Prikladnaya Matematika, Vol. 11, No. 7, pp. 197–206, 2005.     

An Algorithm for Constructing Single Processor Static–Dynamic Schedules

An extended approach is proposed for using algorithms to find the maximum flow in a transport network when constructing schedules for performing work with interruptions in a new class of problems of creating static–dynamic schedules. This is accomplished by projecting realtime systems with the architecture of integrated module avionics. The results from an experimental study confirm the high accuracy of the proposed algorithm.     

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.     

A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces

Literature illustrates the difficulties in obtaining the lowest-cost optimal solution to an ore blending problem for blast furnaces by using the traditional trial-and-error method in iron and steel enterprises. To solve this problem, we developed a cost optimization model which we have implemented in a multi-role-based decision support system (DSS). On the basis of analyzing the business flow and working process of ore blending, we propose an architecture of DSS which is built based on multi-roles. This DSS construction pre-processes the data for materials and elements, builds a general database, abstracts the related optimal operations research models and introduces the reasoning mechanism of an expert system. A non-linear model of ore blending for blast furnaces and its solutions are provided. A database, a model base and a knowledge base are integrated into the expert system-based multi-role DSS to meet the different demands of data, information and decision-making knowledge for the various roles of users. A comparison of the results for the DSS and the trial-and-error method is provided. The system has produced excellent economic benefits since it was implemented at the Xiangtan Iron & Steel Group Co. Ltd., China.