OPTIMIZATION OF URBAN SOLID WASTE COLLECTION ROUTES AND MULTI-VEHICLE COLLABORATIVE DISPATCH DECISION-MAKING

Addressing the core demands of enhancing transportation efficiency and controlling costs in urban solid waste management, this study constructs a multi-objective optimization model for waste collection routes and multi-vehicle coordination scheduling based on multi-dimensional constraints. During the fundamental route planning phase, a mathematical model minimizing total travel distance is established for single-type waste collection points, alongside an improved genetic algorithm incorporating a Markov chain adaptive mechanism. This algorithm employs a hybrid coding scheme for primary and auxiliary chromosomes, dynamically adjusts crossover and mutation operators, and incorporates local optimization strategies like embedded neighborhood search. It successfully achieves efficient task allocation, with experimental results yielding a total travel distance of 1,144.5 kilometers. During the complex multi-vehicle coordination phase, the model was extended to a joint transportation scenario involving four specialized waste collection vehicle types, comprehensively considering constraints such as vehicle payload, volume, unit transportation cost, and maximum daily driving time. An improved multi-objective genetic algorithm was employed for solution, achieving Pareto-optimal scheduling of multi-source waste streams through segmented sequence encoding and feasibility correction mechanisms. Results demonstrate the model's ability to clearly characterize operational efficiency and cost structures across vehicle categories, with food waste collection and transportation accounting for the highest cost proportion at 38.3%.