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multiple-objective APPROXIMATION OF FEASIBLE BUT UNSOLVABLE LINEAR COMPLEMENTARITY-PROBLEMS

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1995年第2期86卷 389-405页

作者： ISAC, G KOSTREVA, MM WIECEK, MM CLEMSON UNIV DEPT MATH SCICLEMSONSC

Using a multiple-objective framework, feasible linear complementarity problems (LCPs) are handled in a unified manner. The resulting procedure either solves the feasible LCP or, under certain conditions, produces an approximate solution which is an efficient point of the associated multiple-objective problem. A mathematical existence theory is developed which allows both specialization and extension of earlier results in multiple-objective programming. Two perturbation approaches to finding the closest solvable LCPs to a given unsolvable LCP are proposed. Several illustrative examples are provided and discussed.

关键词： LINEAR COMPLEMENTARITY PROBLEMS multiple-objective programming EFFICIENT POINTS

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Inverse data envelopment analysis with stochastic data

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RAIRO-OPERATIONS RESEARCH 2021年第5期55卷 2739-2762页

作者： Ghomi, Ali Ghobadi, Saeid Behzadi, Mohammad Hassan Rostamy-Malkhalifeh, Mohsen Islamic Azad Univ Sci & Res Branch Dept Stat Tehran Iran Islamic Azad Univ Khomeinishahr Branch Dept Math Esfahan Iran Islamic Azad Univ Sci & Res Branch Dept Math Tehran Iran

The inverse Data Envelopment Analysis (InvDEA) is an exciting and significant topic in the DEA area. Also, uncertain data in various real-life applications can degrade the efficiency results. The current work addresses the InvDEA in the presence of stochastic data. Under maintaining the efficiency score, the inputs/outputs-estimation problem is investigated when some or all of its outputs/inputs increase. A novel optimality concept for multiple-objective programming problems, stochastic (weak) Pareto optimality in the level of significance alpha is an element of[0,1], is introduced to derive necessary and sufficient conditions for input/output estimation. Furthermore, the performance of the developed theory in a banking sector application is verified.

关键词： Data envelopment analysis inverse DEA stochastic data efficiency multiple-objective programming

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A bi-objective study of the minimum latency problem

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JOURNAL OF HEURISTICS 2019年第3期25卷 431-454页

作者： Arellano-Arriaga, N. A. Molina, J. Schaeffer, S. E. Alvarez-Socarras, A. M. Martinez-Salazar, I. A. Univ Autonoma Nuevo Leon PISIS FIME San Nicolas De Los Garza Mexico Univ Malaga Programa Doctorado Econ & Empresa Malaga Spain Univ Malaga Dept Econ Aplicada Matemat Malaga Spain

We study a bi-objective problem called the Minimum Latency-Distance Problem (mldp) that aims to minimise travel time and latency of a single-vehicle tour designed to serve a set of client requests. This tour is a Hamiltonian cycle for which we aim to simultaneously minimise the total travel time of the vehicle and the total waiting time (i.e., latency) of the clients along the tour. This problem is relevant in contexts where both client satisfaction and company profit are prioritise. We propose two heuristic methods for approximating Pareto fronts for mldp: SMSA that is based on a classic multi-objective algorithm and EiLS that is based on a novel evolutionary algorithm with intelligent local search. We report computational experiments on a set of artificially generated problem instances using an exact method and the two proposed heuristics, comparing the obtained fronts in terms of various quality metrics.

关键词： Combinatorial optimisation Distance Genetic algorithms Latency Meta-heuristics Multi-objective optimisation multiple-objective programming

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MultiGen: an integrated multiple-objective solution system

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DECISION SUPPORT SYSTEMS 2003年第2期36卷 177-187页

作者： Mirrazavi, SK Jones, DF Tamiz, M Univ Portsmouth Sch Comp Sci & Math Portsmouth PO1 2EG Hants England Temposoft UK Maidenhead S16 8BE Berks England

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.

关键词： goal programming multiple-objective programming genetic algorithms

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Implementation of polynomial multi-objective optimization in Mathematica

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2008年第4期36卷 411-428页

作者： Stanimirovic, Predrag Stanimirovic, Ivan Univ Nis Dept Math Fac Sci Nish 18000 Serbia

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.

关键词： multiple-objective programming weighting coefficients prioritizing methods goal programming MATHEMATICA

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Decision support for multi-objective flow shop scheduling by the Pareto Iterated Local Search methodology

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第3期61卷 805-812页

作者： Geiger, Martin Josef Helmut Schmidt Univ Univ Fed Armed Forces Hamburg Logist Management Dept D-22043 Hamburg Germany

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 multiple objectives 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.

关键词： Metaheuristics multiple-objective programming Flow shop scheduling Pareto Iterated Local Search

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Multi-objective programming for energy system based on the decomposition of carbon emission driving forces: A case study of Guangdong, China

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JOURNAL OF CLEANER PRODUCTION 2021年 309卷 127410-127410页

作者： Zhang, Yang Fu, Zhenghui Xie, Yulei Li, Zheng Liu, Yanxiao Hu, Qing Guo, Huaicheng Peking Univ Coll Environm Sci & Engn Beijing 100871 Peoples R China Chinese Res Inst Environm Sci Beijing 100871 Peoples R China Univ Sci & Technol Sch Energy & Environm Engn Beijing 100083 Peoples R China

Energy-related carbon emissions are increasing the rate of climate change, and controlling carbon emissions is a common challenge for the international public. Despite attempts to restrict the utilization of fossil energy and advancing technology for cleaner production, there has been little discussion on the determinants of change in carbon emissions for future scenarios and planning energy systems according to the analysis of low carbon development. In this study, a comprehensive energy optimization planning framework under a low-carbon mode is established. A framework based on the gray model (GM) and logarithmic mean Divisia index (LMDI) method are constructed to predict the emission mitigation potential and decompose the carbon emission driving factors. The decomposition results are key input prerequisites for the following energy optimization model: An interval parameter multiple-objective programming (IPMOP) optimization model, which is developed to support regional energy system administration by seeking the trade-offs among economic development, energy utilization, and environmental protection under multiple uncertainties. Furthermore, the proposed approach is applied to a case study in Guangdong, China. The results reveal that (a) the clean production effect (GDP per unit of atmospheric pollutants emission) would become the primary positive force for carbon emission increase, and the pollutant reduction effect (total atmospheric pollutants emission) would play the primary negative role;(b) the coaldominated energy structure in Guangdong is expected to be transformed to a petroleum-dominated energy structure;(c) the GDP in Guangdong would steadily increase over time, but the pace of economic growth will decelerate, and the annual average growth rate of GDP for the coming fifteen years will be [3.67%, 4.26%]. This study provides a new pathway for policymakers to identify the determinants of carbon emission increase and to generate optimal solutions on a regio

关键词： Carbon emissions Driving factors LMDI Energy optimization model multiple-objective programming Uncertainties

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Train Platforming Problem 11

Train Platforming Problem

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11th Symposium on Geoinformatics for Intelligent Transportation (GIS)

作者： Janosikova, L'udmila Krempl, Michal Univ Zilina Fac Management Sci & Informat Dept Math Methods & Operat Res Zilina 01026 Slovakia Tech Univ Ostrava Inst Transport Fac Mech Engn Ostrava 70833 Czech Republic

ISBN: (纸本)9783319114637;9783319114620

The train platforming problem consists in the allocation of passenger trains to platforms in a railway station. One of the important problems a dispatcher has to solve, especially in a large railway station, is to decide, at which platform track an approaching train should arrive. There is a tool helping him in his job called the track occupancy plan. The plan specifies for each arriving or departing train the platform track along with the time slot during which the track will be occupied by the train. This paper deals with a method for computer-aided design of the track occupancy plan. The problem is formulated as a bi-criterion mixed integer programming problem. The first objective is to minimise the deviations of the arrival and departure times proposed by the model from the times specified by the timetable. The second criterion maximises the desirability of the platform tracks to be assigned to the trains. The model is solved using a lexicographic approach and the local branching algorithm. The model was verified by using the real data of Prague main station. Results of the experiments are included.

关键词： Train platforming Scheduling Mixed integer mathematical programming multiple-objective programming

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An optimization model for location of subsidized housing in metropolitan areas

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Location Science 1998年第1-4期6卷 257-279页

作者： Johnson, Michael P. Hurter, Arthur P. H. John Heinz III Sch. Pub. Plcy. M. Carnegie Mellon University Pittsburgh PA 15213-3890 United States Dept. of Indust. Eng. and Mgmt. Sci. Northwestern University Evanston IL 60208-3119 United States

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 affected by these configurations include residents of subsidized housing, owners of nearby single-family housing, employers and society at large. Since impacts of subsidized housing are very localized, the first stage of the model creates potential location patterns for many small geographic areas. The second stage of the model uses local benefits to derive a location scheme for the metropolitan area which balances net social benefit with equity considerations of the geographical impact of subsidized housing. We apply this methodology to a small region in metropolitan Chicago and demonstrate alternative location schemes. © 1999 Elsevier Science Ltd. All rights reserved.

关键词： Equity Housing Location multiple-objective programming Public sector Welfare economics

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Single-period location models for subsidized housing: Project-based subsidies

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Socio-Economic Planning Sciences 2006年第4期40卷 249-274页

作者： Johnson, Michael P. H. John Heinz III School of Public Policy and Management Carnegie Mellon University Pittsburgh PA 15213-3890 United States

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 tenant-based housing subsidies to provide affordable shelter. PHAs generally apply practitioner expertise to locate subsidized housing and do not identify housing alternatives using prescriptive planning models that optimize policy criteria and incorporate well-defined stakeholder impacts. Using policy characteristics and estimated economic impacts of subsidized housing, a new single-period multi-objective integer programming model for location of project-based subsidized housing is developed. Data requirements, relationships with standard planning models, solution characteristics and sensitivity analyses are examined. It is shown that this model gives planners considerable flexibility in choosing alternative housing configurations that can satisfy the needs of various interest groups. © 2005 Elsevier Ltd. All rights reserved.

关键词： Benefit-cost analysis Facility location multiple-objective programming Public policy Subsidized housing

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