A heuristic-based simulated annealing algorithm for the scheduling of relief teams in natural disasters

Natural disasters cause heavy casualties and financial losses annually. To reduce these damages, the rescue teams need to be planned effectively. In this regard, in this research, a mixed-integer programming model is offered to allocate and schedule rescue teams in a response phase of disaster management under uncertainty. The objective function minimizes the incident's total weighted completion times. The literature review shows that the uncertain condition and time windows have been less addressed in the previous studies. To cover these gaps, this paper investigates the problem under uncertainty and considers time windows for incidents. Besides, the fatigue effect is considered in this paper. Accordingly, within a planning horizon, incident processing times are not fixed. Since the considered problem is an NP-hard one and exact methods cannot solve it within a reasonable amount of time, this research develops a heuristic-based simulated annealing algorithm. The presented model is solved using the developed algorithm and three known meta-heuristic algorithms. Then, the results obtained by algorithms are compared and analyzed. Finally, the sensitivity analysis is carried out on some crucial parameters of the presented model, and the related results are reported.