A Solution for the Full-Load Collection Vehicle Routing Problem With Multiple Trips and Demands: An Application in Beijing

Abstract

Municipal solid waste (MSW) collection has become a major challenge for clean city management and social sustainable development in developing economies. A new variant of the collection vehicle routing problem (CVRP) is addressed with the characteristics of full loads and multiple trips of the collection vehicles, and multiple demands of the garbage facilities, which is called the collection vehicle routing problem of the garbage facilities (CVRPGF) in this study. Dummy customers are introduced to equivalently transform the CVRPGF problem to the vehicle routing problem with simultaneous pickupdelivery and time windows (VRPSPDTW). A parallel simulated annealing algorithm (Par-SAA) is developed to solve the VRPSPDTW problem. When applied to an international benchmark dataset, the computational results prove the superiority of the proposed algorithm, in which the number of collection vehicles (NV) in four instances is reduced by one. Finally, when the model and algorithm are applied to a real CVRPGF problem in the Xuanwu District of Beijing, the NV needed is reduced by 30%, and the total travel time is decreased by 12%. Thus, the effectiveness of the Par-SAA is demonstrated, and the proposed solution has practical value in China. INDEX TERMS Municipal solid waste collection, vehicle routing, full load, multiple trips, multiple demands, dummy customer, parallel simulated annealing.

Artificial intelligence for waste management in smart cities: a review

“…Later, in an experiment in a city in Tunisia, after ten iterations of the genetic algorithm, the running time was reduced from 15.2 to 10.91 h. After optimizing the genetic algorithm, the running time of the vehicle was reduced by 28.22% (Amal et al 2018 ). In addition, parallel annealing algorithms are also used to optimize vehicle collection paths (Zhang et al 2020 ). Using the parallel annealing algorithm to optimize the waste collection path in Xuanwu District, Beijing, Zhang et al ( 2020 ) found that the optimized scheme of the parallel annealing algorithm can reduce the time by 12% compared with the original scheme.…”

IoT-Based Route Recommendation for an Intelligent Waste Management System

“…However, a sequence of collection tasks has not been considered in these studies. To alleviate this concern, an optimization problem is solved by using metaheuristics such that the number of collection trips is minimized [41]. The work [41] provides a theoretical model for a routing problem with the characteristics of full loads and multiple trips.…”

“…To alleviate this concern, an optimization problem is solved by using metaheuristics such that the number of collection trips is minimized [41]. The work [41] provides a theoretical model for a routing problem with the characteristics of full loads and multiple trips. A traveling salesman problem [42], as a combinatorial optimization problem, has been considered to model vehicle routing problems [43], [44].…”

Dynamic Memory Memetic Algorithm for VRPPD With Multiple Arrival Time and Traffic Congestion Constraints

“…In addition, a restricted dynamic programming heuristic algorithm is proposed that finds the best solution for 32 out of 34 instances. Zhang et al equivalently transform the collection vehicle routing problem of the garbage facilities to the vehicle routing problem with simultaneous pickup-delivery and time windows by introducing dummy customers [29]. Besides, they propose a parallel simulated annealing algorithm to address the real application problem in the Xuanwu District of Beijing.…”