Scheduling Optimization of Emergency Resources to Chemical Industrial Parks based on Improved Bacterial Foraging Optimization

Emergency resource scheduling is a critical facet of disaster management, particularly within the complex environments of chemical parks. A model with multiple disaster sites, multiple rescue sites, and multiple emergency resources was constructed considering the problem of resource scheduling in chemical parks when occurring disasters, and the shortest emergency rescuing time and the lowest total scheduling expense were set as its optimization objective. An improved bacterial foraging optimization algorithm was proposed to solve the model. In this algorithm, the loop structure was improved, the information interaction between bacteria was introduced to provide better guidance in the chemotaxis operator, and the migration operator was reconstructed to strengthen the local exploitation in the potential optima area while maintaining the global searching capability. Experimental results on tests demonstrate that the improved bacterial foraging optimization algorithm has better convergence accuracy and faster convergence speed compared with the original bacterial foraging optimization, particle swarm optimization, and genetic algorithm, which proves the IBFO algorithm is an efficient approach to solve emergency resource scheduling problem of chemical industry parks