Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

Kim, Gitae; Ong, Yew Soon; Cheong, Taesu; Tan, Puay Siew

doi:10.1109/tits.2016.2521779

Cited by 102 publications

(37 citation statements)

References 42 publications

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“…Firstly, all possible travel paths were represented by three n * n matrices. Next, the time, speed, distance, fuel consumption and cost values of the different labeled paths A, B and C, respectively, were plugged into the above matrix, where the values of distance and time were derived from the traffic information management system, the fuel consumption was calculated by Equations (5) and (6) and the cost values were calculated by Equations (7)- (10). The driving speed on the arc (i, j) was s ij = d ij /t ij ; the specific matrix data are shown in the Appendix A, Table A1.…”

Section: The Adjacency-matrix Representation Of a Multi-pathmentioning

confidence: 99%

“…This method has been widely used in other studies. Kim et al [10] used a heuristic algorithm to solve the time-dependent vehicle routing problem (TDVRP) in dynamic vehicle routing problems and reported that using the time-dependent shortest path in TDVRP can significantly reduce vehicle travel time. Bektas and Laporte [11] analyzed the pollution routing problem based on emission and energy consumption models; moreover, the effects of time windows, speed, distance and other factors on vehicle emissions were considered.…”

Section: Introductionmentioning

confidence: 99%

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A Path-Based Selection Solution Approach for the Low Carbon Vehicle Routing Problem with a Time-Window Constraint

Wang

2020

Applied Sciences

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Due to the gradual improvement of urban traffic network construction and the increasing number of optional paths between any two points, how to optimize a vehicle travel path in a multi-path road network and then improve the efficiency of urban distribution has become a difficult problem for logistics companies. For this purpose, a mixed-integer mathematical programming model with a time window based on multiple paths for urban distribution in a multi-path environment is established and its exact solution solved using software CPLEX. Additionally, in order to test the application and feasibility of the model, simulation experiments were performed on the four parameters of time, distance, cost, and fuel consumption. Furthermore, using Jingdong (JD), the main urban area in Chongqing, as an example, the experimental results reveal that an algorithm that considers the path selection can significantly improve the efficiency of urban distribution in metropolitan areas with complex road structures.

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Section: The Adjacency-matrix Representation Of a Multi-pathmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Path-Based Selection Solution Approach for the Low Carbon Vehicle Routing Problem with a Time-Window Constraint

Wang

2020

Applied Sciences

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“…In our approach, we adopted two crossover strategies. The first is that the Best-Cost Route Crossover (BCRC) employed in [12,20]. The second is the closest distance and maximum saving route crossover with priority to satisfy the capacity constraint (CMRCSC).…”

Section: Crossovermentioning

confidence: 99%

Solving Dynamic Vehicle Routing Problem using Enhanced Genetic Algorithm with Penalty Factors

Xia¹

2018

IJPE

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The vehicle routing problem (VRP) has become one of the focus issues in operations research and management sciences over the past two decades. One of its principal branches is the dynamic vehicle routing problem (DVRP), which can receive new order requests during the service process and make a timely response, unlike static vehicle routing problems (SVRP) where all information is known before the optimization starts. In this paper, we solve DVRP while using an enhanced genetic algorithm (GA) that tries to increase both diversity and global searching ability. The maximum saving method and the nearest neighbor method are adopted in the crossover operation to improve the path selection. Considering the near distance priority service principle (NDPSP) in the actual operation, a new assessment scheme with penalty factors is applied to our individual assessment. In addition, a paired-t test as a non-parametric statistical analysis is implemented to demonstrate the efficiency of the enhanced genetic optimization algorithm, based on a publicly available VRP benchmark, which includes 21 data sets. Analysis results show that our approach outperformed the published approached based on optimizing results.

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“…Perhaps the closest to ours CVRP formulation is presented in [21] where both stochastic travel times a.nd online adaptation of the solution by means of 140 Monte Carlo (MC) simulations is proposed. On a less general note, however, both papers differ in the following major aspects.…”

mentioning

confidence: 99%

“…On a less general note, however, both papers differ in the following major aspects. Firstly, in [21] the problem is decomposed into independent single-vehicle problems contrary to global optimization performed in this paper. Secondly, the experiments in [21] are based on an in-house real-time traffic data ( owned by logistic company from Singapore) and not on publicly available benchmarks.…”

mentioning

confidence: 99%