A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

Shiyu Chen, Wei Wang and Enrico Zio
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Shiyu Chen: Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy
Wei Wang: Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077, China
Enrico Zio: Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy

Energies, 2021, vol. 14, issue 9, 1-27

Abstract: 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.

Keywords: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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