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 during disasters. The optimization objectives include minimizing the emergency rescuing time and the total scheduling expense. An improved bacterial foraging optimization (IBFO) algorithm was proposed to satisfy these two objectives simultaneously. This algorithm leverages the symmetry inherent in the structure of resource scheduling problems, particularly in balancing the trade-off between local exploitation and global search. The loop structure was enhanced, information interaction between bacteria was incorporated to provide better guidance in the chemotaxis operator, and the migration operator was reconstructed to strengthen the local exploitation in potential optima areas while maintaining global searching capability. The symmetrical nature of the problem allows for more efficient optimization by better exploiting patterns within the solution space. The experimental results show that the IBFO algorithm demonstrates improved convergence accuracy and faster convergence speed compared with the original bacterial foraging optimization, particle swarm optimization, and genetic algorithm. These findings confirm that the IBFO algorithm effectively solves the emergency resource scheduling problem in chemical industry parks by utilizing symmetries to enhance performance.