Route Planning with Dynamic Information from the EPLOS System

Kłodawski, Michał; Jacyna, Marianna; Vašek, Rostislav; Klimek, Piotr; Jachimowski, Roland; Szczepański, Emilian; Lewczuk, Konrad

doi:10.31803/tg-20200710130158

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…For example, Sabar et al [14] constructed a dynamic vehicle routing problem (DVRP), in which the objective of the problem was to maximize the total distribution cost with considerations to different traffic congestion in various periods; they also proposed a self-adaptive evolutionary algorithm that used variable parameters to solve it. Klodawski et al [15] generated a simulation model of VRP with dynamic information in the FlexSim. Yahyaoui et al [16] proposed a GA based on partially matched crossover to solve the multi-compartment vehicle routing problem in oil delivery and found that solutions generated by the proposed algorithm on all involved standard cases were optimal.…”

Section: State Of the Artmentioning

confidence: 99%

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Wang¹,

Huang²

2022

Int. j. simul. model.

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To minimize the total time for the distribution of relief commodities participated by both private companies and the government, a vehicle routing problem (VRP) model in emergencies was proposed. Considering the differences in the starting points of vehicles, the VRP of general logistics, and departments of vehicles, constraints, such as vehicle capacity limitation and time windows, were introduced into the model, which was close to meeting the practical demands of emergency relief. A hybrid code genetic algorithm (HCGA) was proposed, and it used dynamic mutations to avoid early traps in local optimization and to accelerate convergence. This algorithm was programmed by MATLAB. Furthermore, the vehicle routing optimization plans in an emergency was calculated by a simple genetic algorithm (SGA) and the HCGA, respectively. Results demonstrate that the total time for relief distribution in the HCGA is 11.62 % lower and the calculation time is 14.24 % shorter than that of the SGA. The HCGA is not only convenient in processing the constraints of the model and the natural description of problem solutions, but it is also effective in improving the complexity.

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Section: State Of the Artmentioning

confidence: 99%

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Wang¹,

Huang²

2022

Int. j. simul. model.

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“…NP-complete class of problems is the hardest class of NP problems, and NP-hard class of problems are problems that are at least hard as NP-complete problems. Considering that VRP is an NPhard problem, it means that optimal solution cannot be calculated in a time acceptable for practical use by exact algorithms [6]. Because of that, finding an optimal solution for both problems often requires the use of heuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Current State of Dynamic Vehicle Routing Problems Solved by Ant Colony Optimization Algorithm

Olivari

Đukić

2021

Teh. glas. (Online)

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Dynamic Vehicle Routing Problem is a more complex version of Vehicle Routing Problem, closer to the present, real-world problems. Heuristic methods are used to solve the problem as Vehicle Routing Problem is NP-hard. Among many different solution methods, the Ant Colony Optimization algorithm is proven to be the efficient solution when dealing with the dynamic version of the problem. Even though this problem is known to the scientific community for decades, the field is extremely active due to technological advancements and the current relevance of the problem. As various sub-types of routing problems and solution methods exist, there is a great number of possible problem-solution combinations and research directions. This paper aims to make a focused review of the current state in the field of Dynamic Vehicle Routing Problems solved by Ant Colony Optimization algorithm, to establish current trends in the field.

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Multi-Trip Vehicle Routing Problem with Time Windows and Resource Synchronization on Heterogeneous Facilities

2023

Systems

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Inspired by long-distance road transport in industrial logistics in China, this paper studies a simultaneous loading scheduling and vehicle routing problem over a multi-workday planning horizon. Industrial cargo often requires specialized facilities, and these facilities vary in performance and quantity and are subject to available time constraints. Consequently, achieving coordinated optimization of vehicle routing and loading scheduling becomes a significant challenge in practice. We describe the studied problem as a multi-trip vehicle routing problem with time windows and resource synchronization on heterogeneous facilities. First, we develop a mixed integer programming model in a multi-workday setting to minimize the total travel distance and the number of vehicles. Moreover, a three-phase heuristic approach is developed. An initial solution is constructed using a sequential strategy in the first phase, and then an adaptive large neighbourhood search and a post-optimization procedure based on ejection chains are, respectively, designed to optimize the two hierarchical objective functions. Finally, extensive computational experiments are conducted to demonstrate the effectiveness of the proposed method. Specifically, the research results indicate that in long-distance road transport in industrial scenarios, expanding the planning horizon from a single workday to a multi-workday period could significantly reduce logistics operational costs and improve service quality.

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Route Planning with Dynamic Information from the EPLOS System

Cited by 4 publications

References 31 publications

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Current State of Dynamic Vehicle Routing Problems Solved by Ant Colony Optimization Algorithm

Multi-Trip Vehicle Routing Problem with Time Windows and Resource Synchronization on Heterogeneous Facilities

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