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A bi-objective optimization framework for designing an efficient fuel supply chain network in post-earthquakes

Rezaei, Mahdieh, Afsahi, Mohsen, Shafiee, Mahmood, Patriksson, Michael (2020) A bi-objective optimization framework for designing an efficient fuel supply chain network in post-earthquakes. Computers & Industrial Engineering, 147 . ISSN 0360-8352. (doi:10.1016/j.cie.2020.106654) (KAR id:82221)


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.

Item Type: Article
DOI/Identification number: 10.1016/j.cie.2020.106654
Additional information: Article number: 106654
Uncontrolled keywords: Disaster management, Earthquake, Bi-objective optimization, Fuel supply chain, Non-dominated sorting genetic algorithm (NSGA-II), Multi-objective particle swarm optimization (MOPSO)
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 24 Jul 2020 08:14 UTC
Last Modified: 10 Jul 2023 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)

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