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IEEE International Conference on Fuzzy Systems

作者： Bijay Baran Pal Mousumui Kumar Department of Mathematics University of Kalyani Department of Mathematics Alipurduar College

ISBN: (纸本)9781479900213

This paper presents a linear goal programming method to solve thermal power generation and dispatch problems with interval data uncertainty. In the proposed approach, the nonlinear objectives inherently involved to a problem are first transformed into their linear equivalents in the decision situation. Then, target intervals for achieving objectives are defined in power generation planning horizon. In the model formulation, the objectives with target intervals are converted into standard form of goals in goal programming methodology. In goal achievement function, both the modeling aspects of goal programming, minsum and minimax formulations, are addressed for achieving objective values within target intervals specified in the decision environment. To illustrate the potential use of the approach, the model is tested on the standard IEEE 6-Generator 30-Bus System.

关键词： Economic-environmental power generation-dispatch Goal programming interval programming Target interval Taylor Series approximation Goal programming Power production Thermoelectric power generation Dispatching Scientific notation

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Evolutionary technique based goal programming approach to chance constrained interval valued bilevel programming problems

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OPSEARCH 2016年第2期53卷 390-408页

作者： Chakraborti, Debjani Narula Inst Technol Dept Math 15 Nilgunj Rd2nd FloorPO Sodepur Kolkata 700110 W Bengal India

The real world multiobjective decision environment involves great complexity and uncertainity. Many decision making problems often need to be modelled as a class of bilevel programming problems with inexact coefficients and chance constraints. To deal with these problems, a genetic algorithm (GA) based goal programming (GP) procedure for solving interval valued bilevel programming (BLP) problems in a large hierarchical decision making and planning organization is proposed. In the model formulation of the problem the chance constraints are converted to their deterministic equivalent using the notion of mean and variance. Further, the individual best and least solutions of the objectives of the decision makers (DMs) located at different hierarchical levels are determined by using GA method. The target intervals for achievement of each of the objectives as well as the target interval of the decision vector controlled by the upper-level DM are defined. Then, using interval arithmetic technique the interval valued objectives and control vectors are transformed into the conventional form of goal by introducing under-and over-deviational variables to each of them. In the solution process, both the aspects of minsum and minmax GP formulations are adopted to minimize the lower bounds of the regret intervals for goal achievement within the specified interval from the optimistic point of view. The potential use of the approach is illustrated by a numerical example

关键词： Bilevel programming Chance constrained programming Genetic algorithm Goal programming interval programming interval valued bilevel programming

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Weighted sum of maximum regrets in an interval MOLP problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第5期25卷 1659-1676页

作者： Rivaz, S. Yaghoobi, M. A. Shahid Bahonar Univ Kerman Fac Math & Comp Dept Appl Math Kerman Iran

This paper presents a multiobjective linear programming problem with interval objective function coefficients. Considering the concept of maximum regret, the weighted sum problem of maximum regrets is introduced and its properties are investigated. It is proved that an optimal solution of the weighted sum problem of maximum regrets is at least possibly weakly efficient. Further, the circumstances under which the optimal solution is necessarily efficient (necessarily weakly efficient or possibly efficient) are discussed. Moreover, using a relaxation procedure, an algorithm is proposed, which for a given set of weights finds one feasible solution that minimizes the weighted sum of maximum regrets. A numerical example is provided to illustrate the proposed algorithm.

关键词： multiple objective programming linear programming interval programming uncertainty modeling

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interval programming Model for the Journal Selection Problem

Interval Programming Model for the Journal Selection Problem

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International Conference on Advanced Research on Computer Science and Information Engineering

作者： Zhou, Yueyi Lib Hangzhou Dianzi Univ Hangzhouzhou 310018 Peoples R China

The problem of choosing a set of journals to order or to cancel is significant in applications of operations research to library decision making. This paper describes a zero-one interval linear programming approach th... 详细信息

ISBN: (纸本)9783642214011

关键词： journal selection problem interval programming satisficing solution

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Fuzzy Energy and Reserve Co-optimization With High Penetration of Renewable Energy

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2017年第2期8卷 782-791页

作者： Liu, Cong Botterud, Audun Zhou, Zhi Du, Pengwei Elect Reliabil Council Texas Taylor TX 76574 USA Argonne Natl Lab Energy Syst Div 9700 S Cass Ave Argonne IL 60439 USA

In this paper, we propose a fuzzy-based energy and reserve co-optimization modelwith consideration of high penetration of renewable energy. Under the assumption of a fixed uncertainty set of renewables, a two-stage robust model is proposed for clearing energy and reserves in the first stage and checking the feasibility and robustness of re-dispatches in the second stage. Fuzzy sets and their membership functions are introduced into the optimization model to represent the satisfaction degree of the variable uncertainty sets. The lower bound of the uncertainty set is expressed as fuzzy membership functions. The solutions are obtained by transforming the fuzzy mathematical programming formulation into traditional mixed integer linear programming problems.

关键词： Economic dispatch fuzzy set interval programming reserve renewables unit commitment

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An Integrated Air Quality Model and Optimization Model for Regional Economic and Environmental Development: A Case Study of Tangshan, China

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AEROSOL AND AIR QUALITY RESEARCH 2017年第6期17卷 1592-1609页

作者： Sun, Xiaowei Cheng, Shuiyuan Li, Jianbing Wen, Wei Beijing Univ Technol Key Lab Beijing Reg Air Pollut Control Beijing 100124 Peoples R China Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100081 Peoples R China Univ Northern British Columbia Environm Engn Program Prince George BC V2N 4Z9 Canada Chinese Meteorol Adm Environm Meteorol Forecast Ctr Beijing Tianjin He Beijing 100089 Peoples R China

An interval programming optimization model was formulated to develop effective and feasible regional economic structure adjustment plan using Tangshan Municipality in China as a case study. The optimization model was coupled with a WRF-CAMx-PSAT air quality simulation system through the estimated industrial emission sensitivity coefficients and equivalent coefficients for PM2.5 concentrations. Seven categories of industries were examined, and the results indicated that industries with higher emission sensitivity coefficients should be given priority for control. The effectiveness of the obtained optimal schemes was further assessed by the air quality simulation system. It indicated that PM2.5 concentrations in Tangshan would decrease by [33.5%, 39.3%] than those in 2013. This study provided an effective method framework for industries to maximize profits while meeting certain air quality constraints under uncertainty through the coupling of air quality simulation and optimization models.

关键词： Air quality model Emission sources interval programming Optimization model Sensitivity coefficient

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An interval multistage water allocation model for crop different growth stages under inputs uncertainty

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AGRICULTURAL WATER MANAGEMENT 2017年 186卷 86-97页

作者： Chen, Shu Shao, Dongguo Gu, Wenquan Xu, Baoli Li, Haoxin Fang, Longzhang Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China

Due to different responses of crop growth stages to the water deficit, it is necessary to optimize water allocation between different growth stages to obtain the maximum food production in reservoir irrigation systems which are widely distributed throughout Southern China and India. In order to address the inputs uncertainties and dynamics existing in the above agricultural water management, an interval multistage water allocation model is developed. By incorporating multistage stochastic programming and interval parameter programming, the developed model can deal with uncertain inputs both expressed as interval parameters and probability distributions, and realize a dynamic irrigation among different growth stages from a reservoir. In the model, water requirement targets are first treated as first-stage decision variables to tackle the unique problem of agricultural water management. Additionally, given that net benefit and penalty of each growth stage are key parameters due to their determinative roles for allocation between different growth stages, a crop water production function is introduced into the calculation to make them factually reflect the competition among different growth stages. The model is then applied to the Yangshudang Irrigation District to plan rice irrigation and demonstrate its applicability. Rainfall has been divided into five levels with probability distributions in each growth stage and parameters have been characterized as interval numbers to show system uncertainty. Five scenarios that represent different initial active storage levels of the reservoir are set to acquire more detailed results. Through the parameter estimation, net benefits are [1.08,1.29], [5.04, 6.01], [11.79,14.08] and [1.61,1.92] RMB/m(3), and penalties are [2.39, 2.48], [11.13, 11.54], [26.05, 27.01] and [3.55, 3.68] RMB/m(3) for tillering stage, booting stage, heading stage and milky stage respectively. Through the model simulation, water requirement targets in booti

关键词： Crop growth stage interval programming Irrigation water allocation Multistage stochastic programming Water production function Inputs uncertainty

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Development of a fuel management model for a multi-source district heating system under multi-uncertainty and multi-dimensional constraints

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ENERGY CONVERSION AND MANAGEMENT 2017年 153卷 243-256页

作者： Fu, D. Z. Zheng, Z. Y. Gui, J. Xiao, R. Huang, G. H. Li, Y. P. Chinese Acad Sci Shenyang Inst Automat Key Lab Network Control Syst Shenyang 110016 Liaoning Peoples R China Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China Univ Penn Perelman Sch Med Dept Biostat & Epidemiol Philadelphia PA 19104 USA Univ Regina Fac Engn & Appl Sci Environm Syst Engn Program Regina SK S4S 0A2 Canada North China Elect Power Univ SC Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China

In this paper, an interval single-sided fuzzy chance-constrained mixed-integer programming model is developed for the fossil fuel management of a multi-source district heating system under multiple uncertainties, where the heat-supply-capacity expansion planning can also be reflected. The non-dimensional comprehensive equations method is simultaneously improved to quantify the ambiguous heat provisions, utilized as a type of boundary condition inputs to the proposed model. A real-world case study of a heating system located in northeastern China is undertaken to show the feasibility and applicability of the proposed methods. To obtain the reasonable fossil-fuel management schemes, multi-dimensional constraints are incorporated into the model based on a comprehensive consideration in terms of fuel supply and demand, quality and quantity, economic cost and environment protection, as well as their interactions. Results obtained from the case study indicate that the solutions for both continuous and binary variables have been generated, which are useful for identifying suitable fuel-supply patterns and heat-source operational modes for a heating system under different system reliabilities and heating-load distribution states. In addition, the results also reveal that the fossil-fuel management and heating-capacity-expansion pattern, as well as the economic cost and pollutant emission performances are sensitive to the thermal coefficient and system reliability level, which may provide in-depth analyses of tradeoffs for further supporting robust fossil-fuel management under uncertainty.

关键词： Decision making Uncertainty interval programming Fossil fuel management Heating system Fuzzy programming

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An interval multistage classified model for regional inter- and intra-seasonal water management under uncertain and nonstationary condition

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AGRICULTURAL WATER MANAGEMENT 2017年 191卷 98-112页

作者： Chen, Shu Shao, Dongguo Tan, Xuezhi Gu, Wenquan Lei, Caixiu Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan Hubei Peoples R China Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada Hubei Prov Water Resources & Hydropower Planning Wuhan Hubei Peoples R China

In regional water management, various uncertainties such as randomness, non-stationarities, dynamics and complexities, lead to difficulties for water managers. To deal with the above problems, a new methodology is proposed by introducing two methods nonstationary analysis, where the generalized additive model is selected to analyze and fit the distribution of water inflow;and model optimization, where an interval multistage water classified-allocation model (IMWCA) is formulated to optimally allocate the available water. By incorporating multistage stochastic programming, interval parameter programming and classification thought, the IMWCA model can tackle both stochastic and imprecise uncertainties, realize inter-seasonal dynamic allocation, and address the complexity of various water users. The methodology is applied to the Zhanghe Irrigation District to optimize water allocation for municipality, industry, hydropower and agriculture among winter, spring, summer and autumn. The Zhanghe Reservoir seasonal inflow is found to be nonstationary for all the seasons and can be well fitted by the corresponding distributions, showing the sense of nonstationary analysis. Additionally, the comparison with the other model demonstrates the need for classification. From the results, municipality and industry are more competitive than hydropower. The Dongbao, Dangyang and Zhanghe districts have a higher priority than the Jingzhou and Shayang districts for irrigation water. Water requirements are more likely to be satisfied in autumn. These solutions of optimal targets and optimal water allocation are valuable for optimizing inter- and intra-seasonal water resource allocation under uncertainty. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Classification interval programming Nonstationary analysis Multistage stochastic programming Uncertainty Inter-seasonal water management

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Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method

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Journal of Shanghai Jiaotong university(Science) 2017年第6期22卷 657-663页

作者：侯远杭游园梁霄 Transportation Equipment and Ocean Engineering College Dalian Maritime University Dalian 116026LiaoningChina

In the ship hull optimization design based on simulation-based design(SBD) technology, low precision of the approximate model leads to an uncertainty form of optimization model. In order to enable the approximate model with finite precision to maximize the effectiveness, uncertainty optimization method is introduced *** resistance coefficient approximation model, built by back propagation(BP) neural network, is represented as a form of interval. Afterwards, a minimum resistance optimization model is established with the design space constituted by principal dimensions and ship form coefficients. Double-level nested optimization architecture is proposed: for outer layer, improved particle swarm optimization(IPSO) algorithm with learning factor improvement strategy is used to generate design variables, and for inner layer, modified very fast simulated annealing(MVFSA) algorithm is used to solve the objective function interval with uncertainty region. Cases calculation proves the effectiveness and superiority of uncertainty optimization method for ship hull SBD optimization design,thus providing a good way for finding optimal designs.

关键词： ship hull optimization approximation model uncertainty optimization interval programming

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