Developing a Model for the Emergency Rescue Routing Problem Using Stochastic Programming Theory

According to the randomness and weak economy properties of the Emergency Rescue Routing Problem (ERRP), a chance-constrained programming model is constructed based on a stochastic road network. In this model, we consider there are multiple emergency rescue departments serving for a single emergency site, and the travel times between any two nodes are stochastic variables. As the stochastic ERRP is NP-hard and more complex in computation than a shortest path problem in the corresponding deterministic road network, a Particle Swarm Optimization (PSO) algorithm embedded with stochastic simulation is proposed to solve the chance-constrained programming model. Simulation results show that the algorithm is efficient to solve the stochastic ERRP. It can minimize the total travel cost of all rescue vehicles under the time constraints with high reliability. The algorithm can be extended for other NP-hard stochastic programming problems or shortest path problems.