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作者机构:Politecn Milan Energy Dept Via La Masa 34 I-20156 Milan Italy City Univ Hong Kong Dept Mech Engn Kowloon Hong Kong 999077 Peoples R China Aramis Srl Via Pergolesi 5 I-20121 Milan Italy PSL Res Univ CRC MINES ParisTech F-06904 Sophia Antipolis France
出 版 物:《ENERGIES》 (能源)
年 卷 期:2021年第14卷第9期
页 面:2684页
核心收录:
学科分类:0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理]
主 题:energy supply chain oil and gas supply chain multi-objective optimization agent-based modeling uncertainty structure dynamics Monte Carlo simulation non-dominated sorting genetic algorithm
摘 要:The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted in the literature is developed to simulate the agent s uncertain behaviors and the transaction processes stochastically occurring in dynamically changing ESC structures. These are important realistic characteristics that are rarely considered. The simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II)-based optimization scheme to identify the Pareto solutions for which the ESC total profit is maximized and the disequilibrium among its agent s profits is minimized, while uncertainty is accounted for by Monte Carlo (MC) sampling. An oil and gas ESC model with five layers is considered to show the proposed framework and its capability of enabling efficient management of the ESC sustained production while considering the agent s uncertain interactions and the dynamically changing structure.