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

Tirkolaee E. B., Aydin N. S., Ranjbar-Bourani M., Weber G.

COMPUTERS & INDUSTRIAL ENGINEERING, vol.149, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 149
  • Publication Date: 2020
  • Doi Number: 10.1016/j.cie.2020.106790
  • Journal Name: COMPUTERS & INDUSTRIAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Keywords: Disaster rescue units, Resource allocation and scheduling, Multi-choice goal programming with utility functions, Mixed-integer linear programming, Robust Optimization, RESOURCE-ALLOCATION, LEVEL OPTIMIZATION, EMERGENCY RESPONSE, RELIEF, LOGISTICS, COORDINATION, ALGORITHM, SYSTEMS, DEMAND, SEARCH
  • Middle East Technical University Affiliated: No

Abstract

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.