A bi-objective optimization framework for designing an efficient fuel supply chain network in post-earthquakes
Artikel i vetenskaplig tidskrift, 2020

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.

Disaster management

Fuel supply chain


Bi-objective optimization

Multi-objective particle swarm optimization (MOPSO)

Non-dominated sorting genetic algorithm (NSGA-II)


Mahdieh Rezaei

University of Tehran

Mohsen Afsahi

University of Science and Culture

Mahmood Shafiee

University Of Kent

Michael Patriksson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Computers and Industrial Engineering

0360-8352 (ISSN)

Vol. 147 106654







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