A hybrid metaheuristic algorithm for the multi-objective location-routing problem in the early post-disaster stage

Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order cover basic needs victims, emergency logistics should be implemented time. Location-routing problem (LRP) tackles facility location vehicle routing simultaneously obtain overall optimization. response shortage relief materials early post-disaster stage, a multi-objective model for LRP considering fairness is constructed by evaluating urgency coefficients demand points. The objectives are lowest cost, delivery time degree dissatisfaction. Since NP-hard problem, hybrid metaheuristic algorithm Discrete Particle Swarm Optimization (DPSO) Harris Hawks (HHO) designed solve model. addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, introduced enhance its execution efficiency. Finally, effectiveness performance verified case study Wuhan. It demonstrates that more competitive with higher accuracy ability jump out local optimum than other algorithms.