A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect

Date

2020-11-01

Author

Tirkolaee, Erfan Babaee
Aydin, Nadi Serhan
Ranjbar-Bourani, Mehdi
Weber, Gerhard Wilhelm

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper proposes a novel bi-objective mixed-integer linear programming (MILP) model for allocating and scheduling disaster rescue units considering the learning effect. When a natural phenomenon (e.g., earthquake or flood) occurs, the presented decision support model is expected to help decision-makers of emergency relief centers to provide efficient planning for rescue units to minimize the total weighted completion time of rescue operations, as well as the total delay in rescue operations. The problem has some features in common with unrelated parallel machine scheduling (UPMS) problem and traveling salesman problem (TSP). To deal with the inherent uncertainty and bi-objective nature of the problem, an uncertainty-set based robust optimization technique and multi-choice goal programming (MCGP) with utility functions are applied. To demonstrate the applicability of the proposed model, a real case study in Mazandaran province in Iran is presented. The computational results confirm the high complexity of the problem and the significant impacts of the uncertainty on the solution. Moreover, the analytical results provide useful insights to decision-makers for disastrous situations.

Subject Keywords

General Engineering, General Computer Science

URI

https://hdl.handle.net/11511/69633

Journal

COMPUTERS & INDUSTRIAL ENGINEERING

DOI

https://doi.org/10.1016/j.cie.2020.106790

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

E. B. Tirkolaee, N. S. Aydin, M. Ranjbar-Bourani, and G. W. Weber, “A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect,” COMPUTERS & INDUSTRIAL ENGINEERING, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69633.