Optimization of an evacuation plan with uncertain demands using fuzzy credibility theory and genetic algorithm

Evacuation planning is an important activity in disaster management. Due to the sudden occurrence of disasters, evacuation should be preplanned. It is necessary for evacuation plan to be as close as possible to a real evacuation operation. However, evacuation planning is challenging because of inherent uncertainty in required information. One important source of uncertainty in evacuation is the uncertainty in evacuation demands. This paper presents an evacuation vehicle routing problem to design evacuation routes for public vehicles. In this problem, demand (number of evacuees) at each pick-up point is introduced as a fuzzy number and the assignment of the pick-up points to the vehicle routes happens based on a credibility preference index. A genetic algorithm based on fuzzy credibility theory is designed to optimize the problem. The optimum parameter set for genetic algorithm is obtained by Taguchi experimental design. The presented problem and algorithm are applied on a part of Tehran transportation network. The impact of credibility preference index on the achieved solutions is evaluated and its best value is determined for the case study. Moreover, different tests are implemented to analyze and discuss the influence of uncertainty level on the final results.