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A stochastic fuzzy chance-constrained programming model for energy-environment system planning and management in the City of Beijing

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2017年第2期14卷 171-183页

作者： Xu, Ye Huang, Guohe Shao, Liguo North China Elect Power Univ Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing Peoples R China Univ Regina Fac Engn & Appl Sci Regina SK Canada China Ctr Informat Ind Dev Beijing Peoples R China

A stochastic and fuzzy chance-constrained programming (SFCCP) model was developed in this study for supporting energy-environment management in the city of Beijing, China. SFCCP was capable of tackling the variables in constraints as fuzzy random variables, which were integration of randomness and vagueness. SFCCP was applied to an energy-environment management system in the city of Beijing. The study results indicated that SFCCP was useful in helping decision makers gain in-depth insights into proposed management system and establish environment-friendly energy allocation alternatives. The application of SFCCP is expected to provide a good demonstration to energy-environment management problems under complex uncertainties.

关键词： Beijing energy-environment system fuzzy chance-constrained programming fuzzy random variables stochastic chance-constrained programming

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A dual-uncertainty-based chance-constrained model for municipal solid waste management

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APPLIED MATHEMATICAL MODELLING 2013年第22期37卷 9147-9159页

作者： Liu, Feng Wen, Zhi Xu, Ye Univ Sci & Technol Beijing Beijing Key Lab Energy Saving & Emiss Reduct Met Sch Mech Engn Beijing 100083 Peoples R China Univ Sci & Technol Liaoning Sch Met & Mat Anshan 114051 Peoples R China North China Elect Power Univ MOE Key Lab Reg Energy & Environm Syst Optimizat SC Resources & Environm Res Acad Beijing 102206 Peoples R China

A double-sided dual-uncertainty-based chance-constrained programming (DDCCP) model was developed for supporting municipal solid waste management under uncertainty. The model was capable of tackling left-hand- and right-hand-side variables in constraints where those variables were affected by dual uncertainties (i.e. e.g. both fuzziness and randomness);and they were expressed as fuzzy random variables (FRVs). In this study, DDCCP model were formulated and solved based on stochastic and fuzzy chance-constrained programming techniques, leading to optimal solutions under different levels of constraints violation and satisfaction reliabilities. A long-term solid waste management problem was used to demonstrate the feasibility and applicability of DDCCP model. The obtained results indicated that DDCCP was effective in handling constraints with FRVs through satisfying them at a series of allowable levels, generating various solutions that facilitated evaluation of trade-offs between system economy and reliability. The proposed model could help decision makers establish cost-effective waste-flow allocation patterns under complex uncertainties, and gain in-depth insights into the municipal solid waste management system. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Double-sided dual-uncertainty-based chance-constrained programming stochastic chance-constrained programming Fuzzy chance-constrained programming Fuzzy random variable Waste-flow allocation Uncertainty

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A solid waste management model with fuzzy random parameters

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CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS 2014年第1期31卷 64-78页

作者： Xu, Y. Huang, G. H. Shao, L. G. North China Elect Power Univ Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Peking Univ Coll Environm Sci & Engn Beijing 100871 Peoples R China

A novel solid waste management model with fuzzy random parameters (SWMMFRP) was proposed. The model improves the traditional stochastic mathematical programming (SMP) and fuzzy mathematical programming (FMP) methods through tackling dual uncertain parameters in the right-hand side of model constraints. The dual uncertainty is associated with both fuzzy imprecision and randomness. Based on stochastic and fuzzy chance-constrained programming (FCCP) techniques, SWMMFRP was capable of offering more-optimal solution scenarios under multiple uncertainties through incorporating various confidence information into a model framework, and evaluating tradeoffs between system economy and reliability. A long-term municipal waste management case was used to demonstrate the applicability of the proposed method. The results indicated that the model could help generate a spectrum of waste management patterns under different constraint-satisfaction and violation levels and provide support for decision makers to identify desired waste-management strategies. The study case also demonstrated that SWMMFRP is suitable in handling a municipal waste management system that allows somewhat compromise of environmental quality for achieving high economic benefits.

关键词： uncertainty fuzzy random parameter fuzzy chance-constrained programming stochastic chance-constrained programming solid waste management

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Collaborative production planning of supply chain under price and demand uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第3期215卷 590-603页

作者： Zhang, Guoquan Shang, Jennifer Li, Wenli Univ Pittsburgh Katz Grad Sch Business Pittsburgh PA 15260 USA Jilin Univ Coll Quartermaster Technol Changchun 130062 Jilin Peoples R China Dalian Univ Technol Sch Management Dalian 116023 Liaoning Peoples R China

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Supply chain management Uncertainty modeling Production planning Fuzzy sets stochastic chance-constrained programming Evolutionary computations

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An inexact log-normal distribution-based stochastic chance-constrained model for agricultural water quality management

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ENGINEERING OPTIMIZATION 2018年第5期50卷 840-855页

作者： Wang, Yu Fan, Jie Xu, Ye Sun, Wei Chen, Dong Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Reg Sustainable Dev Modeling Beijing Peoples R China Northeast Normal Univ Sch Geog Sci Changchun Jilin Peoples R China North China Elect Power Univ Coll Environm Sci & Engn MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing Peoples R China

In this study, an inexact log-normal-based stochastic chance-constrained programming model was developed for solving the non-point source pollution issues caused by agricultural activities. Compared to the general stochastic chance-constrained programming model, the main advantage of the proposed model is that it allows random variables to be expressed as a log-normal distribution, rather than a general normal distribution. Possible deviations in solutions caused by irrational parameter assumptions were avoided. The agricultural system management in the Erhai Lake watershed was used as a case study, where critical system factors, including rainfall and runoff amounts, show characteristics of a log-normal distribution. Several interval solutions were obtained under different constraint-satisfaction levels, which were useful in evaluating the trade-off between system economy and reliability. The applied results show that the proposed model could help decision makers to design optimal production patterns under complex uncertainties. The successful application of this model is expected to provide a good example for agricultural management in many other watersheds.

关键词： stochastic chance-constrained programming log-normal distribution agriculture Erhai Lake uncertainty

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Hybrid genetic algorithm for solving a class of stochastic location-routing problem

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4th International Conference on Traffic and Transportation Studies

作者： Song, R He, SW Zhao, Q Zhao, H Jiao Tong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

ISBN: (纸本)7030137809

This paper addresses the location-routing problem (LRP) with uncertain demands. A GA-based two phases simulation method has been designed for solving the problem. Based on the location phase of GA procedure, the inserting/swapping heuristic for routing phase has been merged into the GA to combine a hybrid solution. Experimental results show that the hybrid GA performs well both in quality and efficiency. Thus, an improving aided tool for better decision of uncertain LRP problem has been provided.

关键词： location-routing problem hybrid genetic algorithms- heuristic solution uncertain optimization stochastic chance-constrained programming

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Environmental Protection and Security Considered Dynamic Economic Dispatch for Wind Farm Integrated Systems

Environmental Protection and Security Considered Dynamic Eco...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Guan, Yongchang Wang, Yansong Tan, Zhiyong China Univ Petr Qingdao Peoples R China

ISBN: (纸本)9781457705472

Wind power output is a random variable, and before the random variables realized, the power system scheduling decisions must be made. Based on stochastic chance-constrained programming theory, a dynamic economic dispatching model for wind farm integrated systems was established to solve this problem. The power system security and stability, environmental protection and system cost were all taken into the objective function. The model was solved by a hybrid particle swarm optimization and tabu search algorithm(PSO-TS) in which the closed Monte Carlo simulation was introduced. The closed Monte Carlo simulation solves the problem that the Forecasting level of wind power output cannot meet the demand of power system. The results of IEEE30 system with wind farms show that the proposed dispatch model is reasonable and the algorithm is feasible.

关键词： wind farm environmental protection voltage stability dynamic economic dispatching stochastic chance-constrained programming particle swarm algorithm closed Monte Carlo simulation

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A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 147卷

作者： Xu, Haiyan Chang, Yuqing Zhao, Yong Wang, Fuli Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Resources & Civil Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

When wind power is connected to a power grid, intermittency and uncertainty increase the difficulty of power system dispatching and operation. A multi-timescale optimal scheduling model based on wind power uncertainty and demand response are proposed to address the uncertainty of wind power integration. The demand response program is first implemented on the customer side to adjust the electricity consumption pattern and reduce the peak-valley load difference. Then, the robust optimization based on extreme scenarios is used in the day-ahead scheduling stage according to the characteristic that the wind power prediction accuracy gradually improves step by step with the refinement of timescales so that the decision variable meets requirements in all scenarios. In the intraday scheduling stage, the method based on stochastic chance-constrained programming is used, and the out-of-bounds phenomenon is allowed in extreme cases. During real-time scheduling, the deviation remaining after intraday scheduling is corrected. Finally, a 10-unit system is used as an example to demonstrate the feasibility and effectiveness of the proposed scheduling model. The simulation results show that the total economic cost, reserve cost and wind power curtailments of the real-time scheduling model are reduced by 18.21%, 66.06% and 66.57%, respectively, compared with the day-ahead scheduling model. In addition, compared with the intraday scheduling model, the wind power curtailments of the real-time scheduling model are reduced by 20.00%, which proves the necessity of multi-timescale coordinated scheduling.

关键词： Wind power scheduling Demand response Multi-timescale Robust optimization stochastic chance-constrained programming

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A DEA-based simulation-optimisation approach to design a resilience plasma supply chain network: a case study of the COVID-19 outbreak

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2023年第1期10卷 Article: 2224105页

作者： Ghasemi, Peiman Goodarzian, Fariba Simic, Vladimir Tirkolaee, Erfan Babaee Univ Vienna Dept Business Decis & Analyt Vienna Austria Heriot Watt Univ Edinburgh Business Sch EBS Currie Scotland Univ Belgrade Fac Transport & Traff Engn Belgrade Serbia Middle East Univ MEU Res Unit Amman Jordan Istinye Univ Dept Ind Engn Istanbul Turkiye Yuan Ze Univ Dept Ind Engn & Management Taoyuan Taiwan Univ Vienna Dept Business Decis & Analyt Kolingasse 14-16 A-1090 Vienna Austria

This study develops a novel multi-objective mathematical model for a Plasma Supply Chain Network (PSCN) in order to maximise the coverage of blood donors during periods and minimise the blood transportation costs between different nodes, relocation cost of temporary mobile facilities, inventory holding cost of the blood, and the costs of newly established blood centres. Therefore, the major contribution of this work is the simultaneous consideration of resiliency and efficiency in the proposed PCN during the COVID-19 outbreak. To address the uncertain parameters, stochastic chance-constrained programming (SCCP) method is applied to the model. Additionally, to solve the PSCN model, the & epsilon;-constraint method is employed for small- and medium-sized problems and then a multi-objective invasive weed optimisation (MOIWO) algorithm is implemented for large-sized problems. To validate the suggested methodology, a variety of problem instances is designed and solved using the solution techniques, considering two assessment metrics of Hyper Volume (HV) and Min Ideal Distance (MID). Moreover, a real case study and sensitivity analyses on significant parameters are conducted to configure the optimal network. Eventually, the obtained results are examined and useful decision aids are suggested.

关键词： Plasma supply chain network resilience efficiency COVID-19 pandemic stochastic chance-constrained programming simulation-optimisation model >

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