A bi-objective optimization framework for designing an efficient fuel supply chain network in post-earthquakes. (September 2020)

• Record Type: Journal Article
• Authors: Rezaei, Mahdieh; Afsahi, Mohsen; Shafiee, Mahmood; Patriksson, Michael
• Is Part Of: Computers & industrial engineering. Volume 147(2020)
• Publication Date: 2020-09
• Subjects: Disaster management -- Earthquake -- Bi-objective optimization -- Fuel supply chain -- Non-dominated sorting genetic algorithm (NSGA-II) -- Multi-objective particle swarm optimization (MOPSO); Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106654
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A bi-objective non-linear optimization model for fuel distribution in post-earthquakes. Minimizing the penalties due to unsatisfied and/or lost fuel demands. Minimizing the difference between satisfied demands in different affected areas. Developing NSGA-II and MOPSO algorithms to solve the model. A Taguchi's design and experiment method to tune the algorithms' parameters. A real case study to demonstrate the applicability of the proposed framework. Abstract: Earthquakes are the most sudden and unpredictable natural disaster which can cause serious damages in terms of deaths, injuries, and property loss. When an earthquake occurs, it is very important to respond immediately to peoples' emergency needs through proper distribution of critical resources such as medical care, water, food, shelters, etc. Fuel is also one of the most critical needs which must be provided without delay to the population affected by the earthquake, especially the vulnerable children and elderly people. This paper develops a nonlinear bi-objective optimization framework for operating an efficient and effective fuel supply chain network in earthquake-hit areas. The objective functions include minimizing the penalties due to unsatisfied and/or lost fuel demands and minimizing the difference between the satisfied demands in different damaged areas. Some assumptions and constraints, such as the existence of multiple central depots, limited vehicle capacities, time available to respond to the incident, are also considered in the modeling. Two multi-objective evolutionary algorithms (MOEAs), including a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective particle swarm optimization (MOPSO), are proposed to solve the optimization problem. Since the performance of these algorithms is significantly dependent on their parameters, a Taguchi method is used to tune the algorithms' parameters. In addition, four performance metrics are defined to evaluate and compare the performance of the algorithms. A hypothetical earthquake with actual dimensions and realistic data in Yazd province of Iran is presented as a case study, and finally, helpful managerial insights are provided through conducting a sensitivity analysis.