An evolutionary approach comprising tailor-made variation operators for rescue unit allocation and scheduling with fuzzy processing times.

This paper addresses the rescue unit allocation and scheduling problem (RUASP) with fuzzy processing times through an evolutionary approach. The goal of RUASP is to efficiently assign and schedule the rescue units to process the incidents in the event of a natural disaster. This problem can be considered as a variant of the unrelated parallel-machine scheduling problem with sequence and machine-dependent setup times. A steady-state grouping genetic algorithm (SSGGA) approach is presented to minimize the total weighted completion time of the incidents, where the weights correspond to the severity levels of the incidents. The crossover and mutation operators used in this approach are designed as per the characteristics of RUASP and its objective. The proposed approach uses a combination of greedy and random heuristics while generating the initial solutions, thereby yielding superior quality diverse initial solutions. The performance of the proposed approach is compared with the state-of-the-art approach available in the literature. The proposed approach always yields better results in much shorter execution times in comparison to the state-of-the-art approach. Moreover, the robustness analysis test demonstrates the proposed approach to be more robust in comparison to the state-of-the-art approach. [ABSTRACT FROM AUTHOR]