Low-carbon Path Planning For Garbage Collection Vehicles Based On Improved Particle Swarm Algorith

With the development of society and the improvement of living standards,solid municipal waste and carbon dioxide emissions also increase.How to manage the growing solid municipal waste is an urgent problem at present.Waste collection and transportation is an important part of waste management.It is necessary to plan the transportation routes of vehicles reasonably to reduce transportation costs and the negative impact on the environment.As a typical metaheuristic algorithm,particle swarm optimization has the advantages of simple structure and good global performance,so it is suitable for solving NP-hard problems such as vehicle routing problem.Based on the above background,particle swarm optimization and its application in energy-efficient vehicle routing for waste collection is studied in this paper.Firstly,the mathematical model of low-carbon traveling salesman problem is established and its validity is verified.Based on the heuristic information of low-carbon traveling salesman problem,a heuristic discrete particle swarm optimization is proposed,which designs a new type of discrete individual generation operator including multi-mutation strategy and greedy crossover strategy.In addition,the personal best is searched locally based on the priority unloading information,and the global best is searched in a refined way according to the degree of population assimilation,which improve the search accuracy and reduce the rate of population assimilation.The effectiveness of the improved strategy and the proposed algorithm is verified on a set of instances in TSBLIB,and compared with six existing meta-heuristic algorithms applied in the traveling salesman problem,the results show that the proposed algorithm has higher solving accuracy.Secondly,a mathematical model of energy-efficient multi-trip vehicle routing problem for waste collection is established,which incorporates practical factors like the limited capacity,maximum working hours and multiple trips of each vehicle.Considering both economy and environment,fixed costs,fuel costs and carbon emission costs are minimized together.To solve the formulated model effectively,contribution-based adaptive particle swarm optimization is proposed.It adopts greedy decoding,contribution-based adaptive learning strategy and enhanced local search operator to eliminate constraints,improve search efficiency and improve search accuracy respectively.The effectiveness of the proposed strategy is verified by a real waste collection example and nine synthetic instances with different scales.Comparisons with five state-of-the-art algorithms show that the proposed algorithm can find the solution with the lowest total waste collection cost.Finally,based on the energy-efficient multi-trip vehicle routing problem for waste collection,the mathematical model of energy-efficient dynamic vehicle routing problem for waste collection is established considering the dynamic information such as newly added waste sites and vehicle faults,and a Q-learning-based hyper-heuristic particle swarm optimization dynamic scheduling algorithm is proposed.In this algorithm,dynamic response mechanism is designed to quickly respond to dynamic events and generate an initial population with high quality.In addition,the high-level strategy based on Q learning provides efficient low-level heuristic search operators for populations in different states,avoiding blind search of the algorithm.Experiments results show that the proposed algorithm has better convergence than the existing meta-heuristic algorithms for dynamic vehicle routing problem.