Zusammenfassung
In disaster operations management, a challenging task for rescue organizations occurs when they have to assign and schedule their rescue units to emerging incidents under time pressure in order to reduce the overall resulting harm. Of particular importance in practical scenarios is the need to consider collaboration of rescue units. This task has hardly been addressed in the literature. We ...
Zusammenfassung
In disaster operations management, a challenging task for rescue organizations occurs when they have to assign and schedule their rescue units to emerging incidents under time pressure in order to reduce the overall resulting harm. Of particular importance in practical scenarios is the need to consider collaboration of rescue units. This task has hardly been addressed in the literature. We contribute to both modeling and solving this problem by (1) conceptualizing the situation as a type of scheduling problem, (2) modeling it as a binary linear minimization problem, (3) suggesting a branch-and-price algorithm, which can serve as both an exact and heuristic solution procedure, and (4) conducting computational experiments - including a sensitivity analysis of the effects of exogenous model parameters on execution times and objective value improvements over a heuristic suggested in the literature - for different practical disaster scenarios. The results of our computational experiments show that most problem instances of practically feasible size can be solved to optimality within ten minutes. Furthermore, even when our algorithm is terminated once the first feasible solution has been found, this solution is in almost all cases competitive to the optimal solution and substantially better than the solution obtained by the best known algorithm from the literature. This performance of our branch-and-price algorithm enables rescue organizations to apply our procedure in practice, even when the time for decision making is limited to a few minutes. By addressing a very general type of scheduling problem, our approach applies to various scheduling situations. (C) 2018 Elsevier B.V. All rights reserved.
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