This study serves as a primary application of the integrated system dynamics and multiple-objective programming (ISDMOP) model for strategic planning of Beijing city, which is here divided into six subsystems as popul...
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This study serves as a primary application of the integrated system dynamics and multiple-objective programming (ISDMOP) model for strategic planning of Beijing city, which is here divided into six subsystems as population, resources, energy, economy, environment and ecosystem, with the planning horizon spanning from 2003 to 2020. Comparison between the original system dynamics (ORSD) model based on the existing economic structure of Beijing and the optimized system dynamics (OPSD) model adjusted according to the solutions of the multiple-objective programming (MOP) are conducted. The developing trend of each subsystem is simulated and illuminated, based on which constructive suggestions are provided for urban strategic planning of Beijing. The ISDMOP model is proved effective for investigating urban dynamics and realizing the multiple-objective programming. (C) 2007 Elsevier B.V. All rights reserved.
The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable...
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The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multipleobjectives and compared to other local search approaches. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its simplicity as it does require the setting of only very few parameters. The metaheuristic is a key element of the Multi-objective Optimization and Production Planning Solver MOOPPS. The software has been awarded the European Academic Software Award in Ronneby, Sweden (http://***/llab/easa_***). and has since been used for research and higher education in the mentioned problem domain (Geiger, 2006). (C) 2011 Elsevier Ltd. All rights reserved.
In this paper, we propose a new interactive method for multiobjectiveprogramming (MOP) called the PROJECT method. Interactive methods in MOP are techniques that can help the decision maker (DM) to generate the most p...
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In this paper, we propose a new interactive method for multiobjectiveprogramming (MOP) called the PROJECT method. Interactive methods in MOP are techniques that can help the decision maker (DM) to generate the most preferred solution from a set of efficient solutions. An interactive method should be capable of capturing the preferences of the DM in a pragmatic and comprehensive way. In certain decision situations, it may be easier and more reliable for DMs to follow an interactive process for providing local tradeoffs than other kinds of preferential information like aspiration levels, objective function classification, etc. The proposed PROJECT method belongs to the class of interactive local tradeoff methods. It is based on the projection of utility function gradients onto the tangent hyperplane of an efficient set and on a new local search procedure that inherits the advantages of the reference-point method to search for the best compromise solution within a local region. Most of the interactive methods based on local tradeoffs assume convexity conditions in a MOP problem, which is too restrictive in many real-life applications. The use of a reference-point procedure makes it possible to generate any efficient solutions, even the nonsupported solutions or efficient solutions located in the nonconvex part of the efficient frontier of a nonconvex MOP problem. The convergence of the proposed method is investigated. A nonlinear example is examined using the new method, as well as a case study on efficiency analysis with value judgements. The proposed PROJECT method is coded in Microsoft Visual C++ and incorporated into the software PROMOIN (Interactive MOP).
In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all consi...
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In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all considered as the uncertain parameters. To develop a robust model, two additional objective functions are added into the traditional comprehensive supply chain design problem. So, our multi-objective model includes (i) the minimization of the sum of current investment costs and the expected future processing, transportation, shortage and capacity expansion costs, (ii) the minimization of the variance of the total cost and (iii) the minimization of the financial risk or the probability of not meeting a certain budget. The ideas of unreliable suppliers and capacity expansion, after the realization of uncertain parameters, are also incorporated into the model. Finally, we use the goal attainment technique to obtain the Pareto-optimal solutions that can be used for decision-making. (c) 2008 Elsevier B.V. All rights reserved.
We describe implementation of main methods for solving polynomial multi-objective optimization problems by means of symbolic processing available in the programming language MATHEMATICA. Symbolic transformations of un...
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We describe implementation of main methods for solving polynomial multi-objective optimization problems by means of symbolic processing available in the programming language MATHEMATICA. Symbolic transformations of unevaluated expressions, representing objective functions and constraints, into the corresponding representation of the single-objective constrained problem are especially emphasized. We also describe a function for the verification of Pareto optimality conditions and a function for graphical illustration of Pareto optimal points and given constraint set.
Large urban public housing authorities (PHAs) in the US have abandoned the traditional model of high-rise public housing developments and increasingly use lower-density project-based subsidized housing as well as tena...
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This paper presents an overview of a global multiple-objective system capable of rigourously handling a wide variety of multiple-objective and goal programming (GP) models. The system has two efficient solution engine...
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This paper presents an overview of a global multiple-objective system capable of rigourously handling a wide variety of multiple-objective and goal programming (GP) models. The system has two efficient solution engines that complement one another. These are an intelligent conventional optimisation system and a genetically driven heuristic search-based solver. The development of a linear and nonlinear sparse storage system capable of handling additional multiple-objective factors such as priority levels, goals, objectives, and upper and lower bounds is described. An appropriate input/output style for multiple-objective programmes is given. The resulting package provides an efficient and user-friendly environment for the interactive solution and analysis of a wide variety of multiple-objective programming (MOP) models. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper presents an optimization model for evaluation of alternative spatial configurations of rent-subsidized housing in a large metropolitan area as well as associated monetary and nonmonetary impacts. Groups aff...
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Structural design, like other complex decision problems, involves many tradeoffs among competing criteria. While mathematical programming models are increasingly realistic, there are often relevant issues that cannot ...
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Structural design, like other complex decision problems, involves many tradeoffs among competing criteria. While mathematical programming models are increasingly realistic, there are often relevant issues that cannot be easily captured, if at all, in a formal system. This paper describes an approach to modelling that recognizes these limitations and allows a designer to explore unmodelled issues in a joint human-computer cognitive system. A prototype based on this approach is presented for topological truss optimization, and three modelling techniques are contrasted for their effectiveness in producing ''different'' alternatives. The results show that alternatives produced using these techniques are good with respect to modelled objectives, and yet are different, and often better, with respect to interesting objectives not present in the model.
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