Evolutionary multitasking for bidirectional adaptive codec: A case study on vehicle routing problem with time windows

Wu, Yanlin; Cai, Yi; Fang, Chien

doi:10.1016/j.asoc.2023.110605

Cited by 7 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the scale of the problem gradually increases, partial literatures proposed using heuristic algorithms to solve VRP. The basic idea of heuristic algorithms is to design heuristic functions to provide a feasible solution to the problem to be solved within an acceptable range [11][12][13][14][15][16]. Compared to the former, when handling large scale VRPs, the heuristic algorithms showcased better feasibility, and can search for the optimal solution of the problem in a short time, which improved vehicle routing's accuracy further.…”

Section: Introductionmentioning

confidence: 99%

Green Vehicle Routing Optimization Based on Dynamic Constraint Selection Co-evolutionary Algorithm

Zhou,

Zhang,

2024

Preprint

View full text Add to dashboard Cite

Aiming at the problems of single solution objective in the existing green vehicle routing optimization process and real-time speed change during vehicle travel, a multi-objective green vehicle routing problem with time window constraints in time-varying conditions(MOGVRPTW-TV) is established, then a co-evolutionary framework-based constrained multi-objective evolutionary algorithm for the solving of the model is proposed. First, in order to better match the actual logistics distribution, this model considers the impact of time-varying speed and capacitated variation on carbon emissions based on capacitated constraints and time window constraints. Second, a constrained multi-objective evolutionary algorithm based on the co-evolutionary framework was proposed for model solving. The algorithm treats the complete problem model as a complex task and introduces a shift crowding distance calculation that considers both individual distribution and convergence information when solving this complex task, effectively balancing the convergence and diversity of solutions. Then, a dynamic constraint selection strategy is designed for the implementation of the simple task, which only considers the effects of some constraints on the populations, and the two populations exchange information through offspring populations to achieve overall optimization. Simulations are performed on different instances and the results show that the proposed algorithm is effective in avoiding traffic congestion periods, decreasing overall distribution costs, and reducing fuel consumption and carbon emissions of vehicles.

show abstract

Section: Introductionmentioning

confidence: 99%

Green Vehicle Routing Optimization Based on Dynamic Constraint Selection Co-evolutionary Algorithm

Zhou,

Zhang,

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Since it was first proposed by Dantzig and Ramser [4] in 1959, VRP has been a hot topic in the operations research literature. Recently, more and more researchers focus on variation VRPs, such as the VRP with time windows [5,6], the VRP with simultaneous delivery and pickup [7,8] and the VRP with heterogeneous fleet [9,10]. These VRPs come from reality and have high practical application values [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup

Liu,

Qin,

Liu

2023

Mathematics

View full text Add to dashboard Cite

The Split Vehicle Routing Problem with Simultaneous Delivery and Pickup (SVRPSDP) consists of two subproblems, i.e., the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP) and the Split Delivery Vehicle Routing Problem (SDVRP). Compared to the subproblems, SVRPSDP is much closer to reality. However, some realistic factors are still ignored in SVRPSDP. For example, the shipments are integrated and cannot be infinitely subdivided. Hence, this paper investigates the Granularity-based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup (GSVRPSDP). The characteristics of GSVRPSDP are that the demands of customers are split into individual shipments and both the volume and weight of each shipment are considered. In order to solve GSVRPSDP efficiently, a Genetic-Simulated hybrid algorithm (GA-SA) is proposed, in which Simulated Annealing (SA) is inserted into the Genetic Algorithm (GA) framework to improve the global search abilities of individuals. The experimental results indicate that GA-SA can achieve lower total costs of routes compared to the traditional meta-algorithms, such as GA, SA and Particle Swarm Optimization (PSO), with a reduction of more than 10%. In the further analysis, the space utilization and capacity utilization of vehicles are calculated, which achieve 86.1% and 88.9%, respectively. These values are much higher than those achieved by GA (71.2% and 74.8%, respectively) and PSO (60.9% and 65.7%, respectively), further confirming the effectiveness of GA-SA. And the superiority of simultaneous delivery and pickup is proved by comparing with separate delivery and pickup. Specifically, the costs of separate delivery and pickup are more than 80% higher than that of simultaneous delivery and pickup.

show abstract

Standardized validation of vehicle routing algorithms

Jastrzab,

Myller,

Tulczyjew

et al. 2024

Appl Intell

View full text Add to dashboard Cite

Designing routing schedules is a pivotal aspect of smart delivery systems. Therefore, the field has been blooming for decades, and numerous algorithms for this task have been proposed for various formulations of rich vehicle routing problems. There is, however, an important gap in the state of the art that concerns the lack of an established and widely-adopted approach toward thorough verification and validation of such algorithms in practical scenarios. We tackle this issue and propose a comprehensive validation approach that can shed more light on functional and non-functional abilities of the solvers. Additionally, we propose novel similarity metrics to measure the distance between the routing schedules that can be used in verifying the convergence abilities of randomized techniques. To reflect practical aspects of intelligent transportation systems, we introduce an algorithm for elaborating solvable benchmark instances for any vehicle routing formulation, alongside the set of quality metrics that help quantify the real-life characteristics of the delivery systems, such as their profitability. The experiments prove the flexibility of our approach through utilizing it to the NP-hard pickup and delivery problem with time windows, and present the qualitative, quantitative, and statistical analysis scenarios which help understand the capabilities of the investigated techniques. We believe that our efforts will be a step toward the more critical and consistent evaluation of emerging vehicle routing (and other) solvers, and will allow the community to easier confront them, thus ultimately focus on the most promising research avenues that are determined in the quantifiable and traceable manner.

show abstract

Evolutionary multitasking for bidirectional adaptive codec: A case study on vehicle routing problem with time windows

Cited by 7 publications

References 48 publications

Green Vehicle Routing Optimization Based on Dynamic Constraint Selection Co-evolutionary Algorithm

Green Vehicle Routing Optimization Based on Dynamic Constraint Selection Co-evolutionary Algorithm

An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup

Standardized validation of vehicle routing algorithms

Contact Info

Product

Resources

About