Simulated Annealing for a Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows

2020

IEEE Access

Self Cite

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.

Section: ) Algorithmsmentioning

confidence: 99%

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

Zhang

2020

IEEE Access

Self Cite

“…This article will be focus mainly on SA and VNS which have been widely used in the variants of MDVRP. -Nasab, Ahari, and Ghazanfari, 2013;Moh and Chiang, 2000;Polacek, Hartl, and Doerner, 2004;Wang et al, 2013;Yu, Redi, Hidayat, andWibowo, 2017 GRASP Nadjafi andNadjafi, 2016;Polat, 2017 3.1.1 Simulated Annealing (SA) SA is an algorithmic approach and stochastic gradient method to solve combinatorially and the global optimization problem (Ghaffari-Nasab, Ahari, & Ghazanfari, 2013).…”

Section: Single-based Metaheuristic Algorithmmentioning

confidence: 99%

A Review of Single and Population-Based Metaheuristic Algorithms Solving Multi Depot Vehicle Routing Problem

Samsuddin

Othman

Yusuf

2018

IJSECS

Multi-Depot Vehicle Routing Problem (MDVRP) arises with rapid development in the logistics and transportation field in recent years. This field, mainly, faces challenges in arranging their fleet efficiently to distribute the goods to customers by minimizing distance and cost. Therefore, the decision maker needs to specify the vehicles to reach the particular depot which, serves the customers with the predetermined capacity. Hence, to solve the stated problems, there is a need to apply metaheuristic methods to get minimal transportation costs. This article reviews on single and population-based metaheuristic methods solving MDVRP from the year 2013 until 2018. The methods discussed were simulated annealing (SA), variable neighborhood search (VNS), ant colony algorithm (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). From the previous works, it can be concluded that the application of populationbased metaheuristic gives better solutions in solving MDVRPs.

“…Wang and Chen [5] proposed a co-evolution genetic algorithm for VRPSPDTW, and developed 65 benchmark instances [13] revised from the well-known Solomon benchmark for VRP with time windows (VRPTW) [14]. Recently, Wang et al [15] and Kassem and Chen [4] adopted simulated annealing to deal with VRPSPDTW. Deng et al [16] proposed an improved simulated annealing for VRPSDPTW with soft time windows.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most popular techniques is to use scalar techniques. In [5], [15], and [16], several algorithms are proposed to optimize a weighted linear aggregation function of objectives. However, this kind of techniques needs to set the weights according to the importance of the objectives, which is a difficult task [18].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms

Zhou

et al. 2016

IEEE Trans. Cybern.

168

This paper investigates a practical variant of the vehicle routing problem (VRP), called VRP with simultaneous delivery and pickup and time windows (VRPSDPTW), in the logistics industry. VRPSDPTW is an important logistics problem in closed-loop supply chain network optimization. VRPSDPTW exhibits multiobjective properties in real-world applications. In this paper, a general multiobjective VRPSDPTW (MO-VRPSDPTW) with five objectives is first defined, and then a set of MO-VRPSDPTW instances based on data from the real-world are introduced. These instances represent more realistic multiobjective nature and more challenging MO-VRPSDPTW cases. Finally, two algorithms, multiobjective local search (MOLS) and multiobjective memetic algorithm (MOMA), are designed, implemented and compared for solving MO-VRPSDPTW. The simulation results on the proposed real-world instances and traditional instances show that MOLS outperforms MOMA in most of instances. However, the superiority of MOLS over MOMA in real-world instances is not so obvious as in traditional instances.