Co-Optimization Model for Multi-Objective Vehicle Paths in Urban Multilevel Waste Separation Logistics System

Research on Route Optimization and Scheduling of Urban Waste Classification Transportation involves challenges such as route planning, fleet scheduling, and dynamic site selection. This paper constructs a multi-level mathematical model system and proposes hybrid intelligent optimization algorithms, including an improved simulated annealing algorithm, a multi-objective vehicle routing problem model, and a dynamic site selection-routing collaborative optimization model. Aiming at the problems of single-type and multi-type fleet scheduling as well as transfer station site selection, this study aims to achieve cost optimization and route efficiency improvement under dual capacity constraints, and effectively balance computational complexity and solution quality. The research results can provide quantitative decision support for urban environmental sanitation systems and have the expansion potential to fields such as emergency logistics. The innovations are: constructing a stepped model system to achieve full-coverage optimization from single vehicles to multi-fleets and from static to dynamic scenarios; proposing an improved SA algorithm under dual capacity constraints, which enhances convergence performance by innovatively designing dynamic penalty factors.