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A quantitative method for accounting human opinion, preferences and perceptions in ecosystem management

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2003年第2期68卷 193-205页

作者： Pavlikakis, GE Tsihrintzis, VA Democritus Univ Thrace Dept Environm Engn Lab Ecol Engn & Technol GR-67100 Xanthi Greece

Ecosystem management (EM) is a holistic approach, in which public participation in decision-making, and incorporation of human preferences, needs and perceptions in management plans is a main element. The decision-making from human opinion method (DeMHO), presented here. can be used in EM in selecting the more suitable and socially acceptable management plan, in order to protect or restore an ecosystem. The method focuses on the quantification of the human opinion, preferences and perceptions, which are investigated after research on the local population of the ecosystem. The results of this research are the inputs of the method;multi-criteria decision-making procedures, such as the analytic hierarchy process (AHP), the expected utility method (EUM) and compromise programming (CP) are used to assign the appropriate weights and rank according to their importance the interest groups, the issues to be studied, and the alternative management plans. The alternatives are also evaluated by assessing their sustainable character. The paper presents DeMHO and its application in the National Park of Eastern Macedonia and Thrace in Greece, after a research through a questionnaire on the local population. (C) 2003 Elsevier Science Ltd. All rights reserved.

关键词： ecosystem management public opinion decision-making analytic hierarchy process expected utility method compromise programming research questionnaire

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Bi-objective optimisation model for installation scheduling in offshore wind farms

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COMPUTERS & OPERATIONS RESEARCH 2017年 78卷 393-407页

作者： Irawan, Chandra Ade Jones, Dylan Ouelhadj, Djamila Univ Portsmouth Dept Math Ctr Operat Res & Logist Lion Gate BldgLion Terrace Portsmouth PO1 3HF Hants England Inst Teknol Nas Dept Ind Engn Bandung 40124 Indonesia

A bi-objective optimisation using a compromise programming approach is proposed for installation scheduling of an offshore wind farm. As the installation cost and the completion period of the installation are important aspects in the construction of an offshore wind farm, the proposed method is used to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal installation schedule considering several constraints such as weather condition and the availability of vessels. We suggest two approaches to deal with the multi-objective installation scheduling problem, namely compromise programming with exact method and with metaheuristic techniques. In the exact method the problem is solved by CPLEX whereas in the metaheuristic approach we propose Variable Neighbourhood Search (VNS) and Simulated Annealing (SA). Moreover, greedy algorithms and a local search for solving the scheduling problem are introduced. Two generated datasets are used for testing our approaches. The computational experiments show that the proposed metaheuristic approaches produce interesting results as the optimal solution for some cases is obtained. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Variable Neighbourhood Search Simulated Annealing Multi-objectives compromise programming Installation scheduling Offshore wind farm

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SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty

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RESOURCES CONSERVATION AND RECYCLING 2009年第6期53卷 352-363页

作者： Xu, Y. Huang, G. H. Qin, X. S. Cao, M. F. Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada N China Elect Power Univ Sino Canada Ctr Energy & Environm Res Beijing 102206 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore

A hybrid stochastic robust chance-constraint programming (SRCCP) model was developed in this study for supporting municipal solid waste management under uncertainty. The method improves upon the existing robust-optimization (RO) and chance-constraint programming (CCP) approaches by allowing analysis on trade-offs among expected value of the objective function, variation in the value of the objective function and the risk of violating constraints that contain uncertain parameters. SRCCP could be used to examine the balance between solution robustness and model robustness. and was especially useful for analyzing the reliability of satisfying (or risk of violating) system constraints under complex uncertainties. A long-term municipal solid waste management problem was used to demonstrate the applicability of SRCCP, with violations for capacity constraints being assumed under various significance levels. The study results demonstrated that a higher system cost may guarantee that waste-management requirements and environmental criteria be met, and a lower cost may lead to a higher risk of violating the related regulations. The proposed SRCCP model could be used by waste managers for identifying desired waste-management policies under various environmental, economic, and system-reliability constraints and complex uncertainties. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Robust optimization Chance-constrained programming Environment compromise programming Solid waste management Uncertainty

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Designing a resilient competitive supply chain network under disruption risks: A real-world application

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2018年 115卷 87-109页

作者： Ghavamifar, Ali Makui, Ahmad Taleizadeh, Ata Allah Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

We address an intra-supply chain competition where a producer and resellers competing to achieve their goals, while taking into consideration the uncertainties and disruption risks. We utilize a bi-level multi-objective programming approach for designing a competitive supply chain network. A hybrid solution approach, combining the compromise programming and Benders decomposition methods, is developed to solve the model. Furthermore, an efficient inequality constraint is proposed to cope with the computational complexity of the bi-level model. To explore the practical application of the model, a real-world case example is discussed. Finally, the scalability of the solution approach is illustrated for large-scale problems.

关键词： Competition Disruption risks Bi-level programming compromise programming Benders decomposition

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Urban Water Management Using Fuzzy-Probabilistic Multi-Objective programming with Dynamic Efficiency

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WATER RESOURCES MANAGEMENT 2010年第15期24卷 4491-4504页

作者： Zarghami, Mahdi Univ Tabriz Fac Civil Engn Tabriz *** Iran

This paper introduces a new multi-objective optimization model for integrated urban water management The model, based on compromise programming, is applied for the case of Tabriz city in Iran The water demand of this city is rapidly growing and because of the limited resources, water supply is now more vulnerable to any mismanagement Therefore the model attempts to optimize the water supply plan of city concerning three main objectives of maximizing the water supply, minimizing the cost and minimizing the environmental hazards Due to the vagueness in defining the first objective, it is modeled by using the fuzzy set theory Further, the uncertainty in satisfying some constraints is tackled by using the chance constraint approach The decision variables are the extent of water withdrawal from the city aquifer, three different water transfer schemes and also the extent of demand management by leaks detection and pipes rehabilitation Then the fuzzy-probabilistic multi-objective model is solved by considering the new idea of dynamic efficiency in the utility of decision maker and the results provide the optimum water supply in the planning horizon The model results in robust solutions in which the demand management option dominates the new water transfer Implementing the results of this model supports the environmental conservation and sustainable development

关键词： Multi-objective decision making Fuzzy stochastic models compromise programming Urban water management Tabriz city

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Two-stage layout-size optimization method for prow stiffeners

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INTERNATIONAL JOURNAL OF NAVAL ARCHITECTURE AND OCEAN ENGINEERING 2019年第1期11卷 44-51页

作者： Liu, Zhijun Cho, Shingo Takezawa, Akihiro Zhang, Xiaopeng Kitamura, Mitsuru Hiroshima Univ Grad Sch Engn Dept Transportat & Environm Syst Hiroshima Japan Dalian Univ Technol Dept Engn Mech Dalian Peoples R China

Designing sophisticate ship structures that satisfy several design criteria simultaneously with minimum weight and cost is an important engineering issue. For a ship structure composed of a shell and stiffeners, this issue is more serious because their mutual effect has to be addressed. In this study, a two-stage optimization method is proposed for the conceptual design of stiffeners in a ship's prow. In the first stage, a topology optimization method is used to determine a potential stiffener distribution based on the optimal results, whereupon stiffeners are constructed according to stiffener generative theory and the material distribution. In the second stage, size optimization is conducted to optimize the plate and stiffener sections simultaneously based on a parametric model. A final analysis model of the ship-prow structure is presented to assess the validity of this method. The analysis results show that the two-stage optimization method is effective for stiffener conceptual design, which provides a reference for designing actual stiffeners for ship hulls. (C) 2018 Society of Naval Architects of Korea. Production and hosting by Elsevier B.V.

关键词： Prow stiffener Two-stage optimization Manufacturing constraints Multi-objective function compromise programming

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A new approach for multiobjective decision making based on fuzzy distance minimization

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MATHEMATICAL AND COMPUTER MODELLING 2008年第9-10期47卷 808-826页

作者： Terol, Amelia Bilbao Univ Oviedo Dept Quantitat Econ Oviedo Spain

The aim of this paper is to design flexible decision making models in the distance metric optimization framework for problems including parameters which are represented by fuzzy numbers. Multi-criteria decision making methodologies based on distance functions involve the minimization of some form of distance from a desired point (ideal or specified by a decision marker). If it is assumed that the parameters of problem are fuzzy numbers, then it is natural to expect that this point also is so. Thus, in this paper it is supposed that the desired point is a vector of fuzzy numbers obtained from the imprecise information provided by the decision marker or, alternatively, composed by the individual optimum of each objective under consideration. The methodological proposal is an extension of the distance-based models and relies in the first instance, on the constructing of a fuzzy minimum distance obtained by solving linear programming problems. Secondly, it is shown that this fuzzy minimum distance possesses suitable features with respect to the quality and handling of information such that it can be incorporated in distance-based ordinary models which are necessary in order to determine an optimum decision. To illustrate the suitability of the method, a numerical example has been included. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： fuzzy multiobjective programming goal programming compromise programming distance minimization fuzzy number value ambiguity and fuzziness

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Managing forest and marginal agricultural land for multiple tradeoffs: compromising on economic, carbon and structural diversity objectives

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ECOLOGICAL MODELLING 2005年第2-4期185卷 451-468页

作者： Krcmar, E van Kooten, GC Vertinsky, I Univ British Columbia FEPA Res Unit Forest Sci Ctr Vancouver BC V6T 1Z4 Canada Univ Victoria Dept Econ Victoria BC V8W 2Y2 Canada Univ British Columbia Sauder Sch Business Div Operat Logist Vancouver BC V6T 1Z2 Canada

In this paper, we use compromise programming to solve a multiple-objective land use and forest management planning model. Long- and short- ('fast') term carbon uptake, maintenance of structural diversity, and economic (net returns to forestry and agriculture) objectives are simultaneously achieved by minimizing the distance between current objective values and the ideal ones. Two distance metrics are used, representing a risk neutral and highly risk-averse decision maker. An application of the model to public forestland and adjacent private agricultural lands in the (boreal) Peace River region of northeastern British Columbia indicates that both short- and long-term carbon uptake, and maintenance of structural diversity, can be achieved only at the high financial costs. Contrary to earlier studies, we also find conflict between both short- and long-term carbon uptakes and maintenance of landscape structural diversity. Targeting short-term carbon uptake results in the greatest deviation from desired structural diversity, although the deviation is somewhat smaller with respect to the long-term carbon uptake goal. Further, risk neutral and risk-averse decision makers will employ significantly different land use and forest management strategies. Finally, the 'balanced' strategy (which underachieves attainment of the 'ideal' by the same degree for all objectives) attains diversity targets quite closely, but significantly underachieves economic and carbon objectives. Maximization of the weighted sum of objective deviations results in an 'average' strategy that performs much better in attaining carbon objectives, but diversity is sacrificed. (c) 2005 Elsevier B.V. All rights reserved.

关键词： compromise programming carbon sequestration forest management and afforestation structural diversity

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Multi-Objective Planning for Conjunctive Use of Surface and Ground Water Resources Using Genetic programming

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WATER RESOURCES MANAGEMENT 2019年第6期33卷 2123-2137页

作者： Sepahvand, Reza Safavi, Hamid R. Rezaei, Farshad Isfahan Univ Technol Dept Civil Engn Esfahan Iran

In arid and semi-arid regions, climate change causes a drastic decline in the volume of water resources as water demands increase. Thus, the present study is aimed at using a simulation-optimization model to perform conjunctive management of surface-ground water use to achieve two main objectives: (1) minimizing shortages in meeting irrigation water demands and (2) maximizing the total agricultural net benefit for the main crops of an agricultural sector. To meet these main goals, first, the genetic programming (GP) method is used to simulate surface water-groundwater interactions. Then, the simulation model is linked to a multi-objective genetic algorithm (MOGA) as the optimization model, yielding a simulation-optimization model. In order to investigate the impact of different climatic conditions on the optimized surface and ground water allocation and propose an optimal crop pattern for each climatic period, three planning periods (wet, normal and dry) were addressed in modeling the conjunctive water use management problem. Finally, the economic results of this study suggested a maximum increase in the net benefit by 38.19%, 59.37% and 45%, as compared to those obtained in the actual operation in wet, normal and dry years, respectively, for one study sub-area. The net benefit was also increased by at most 84.79%, 83.3% and 120.77% in wet, normal and dry years, respectively, for another study sub-area, demonstrating the competence of the optimal conjunctive use model to enhance net benefits with the least negative socio-environmental impacts resulting from any development and management scheme in the field of water resources.

关键词： Conjunctive use Multi-crop pattern planning Simulation-optimization Multi-objective optimization Genetic programming compromise programming

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Numerical study and optimization of thermohydraulic characteristics of a graphene-platinum nanofluid in finned annulus using genetic algorithm combined with decision-making technique

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ENGINEERING WITH COMPUTERS 2021年第3期37卷 2473-2491页

作者： Khosravi, Raouf Teymourtash, A. R. Passandideh Fard, Mohammad Rabiei, Saeed Bahiraei, Mehdi Ferdowsi Univ Mashhad Dept Mech Engn Mashhad Razavi Khorasan Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

The heat transfer and flow attributes of a cylindrical microchannel heat sink (CMCHS) operated with a hybrid nanofluid containing the graphene nanoplatelets and platinum particles are numerically investigated. The CMCHS is modeled with three different numbers of fins, and the problem is solved for four concentrations and four Reynolds numbers. The effect of these variables on the thermal and frictional parameters-such as the convective heat transfer coefficient, pressure loss, friction coefficient, thermal resistance, and maximum temperature-is evaluated. The heat transfer coefficient increases by raising the Reynolds number, concentration, and fin number. Thereby, with an increase in fin number from 25 to 36 at Reynolds number of 300 and concentration of 0.1%, the convective heat transfer coefficient is enhanced by 134%. The maximum performance evaluation criterion (PEC) is obtained as 1.98 at concentration of 0.1%, Reynolds number of 600, and number of fins of 36. The thermal resistance decreases by increasing each of the parameters of fin number, Reynolds number, and concentration. Based on the obtained data, a predictor model for the output parameters (i.e., heat transfer coefficient, thermal resistance, and pumping power) is derived by a neural network. Then, the optimization is performed using a genetic algorithm combined with decision-making technique considering different designer's viewpoints to achieve the highest convective heat transfer coefficient and the lowest pumping power and thermal resistance.

关键词： Heat sink Cylindrical microchannel Hybrid nanofluid Artificial neural network Genetic algorithm compromise programming

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