Distribution Path Optimization for Intelligent Logistics Vehicles of Urban Rail Transportation Using VRP Optimization Model

Leng, Kaijun; Li, Shanghong

doi:10.1109/tits.2021.3105105

Cited by 29 publications

(10 citation statements)

References 36 publications

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“…Hence, it is important to clarify how to arrange a special vehicle, that is, select feasible nodes from all massive-cargo transportation nodes and determine the order of the vehicle visiting these nodes, so as to transport massive cargos from the origin to the destination. Without loss of generality, the model assumes that: (1) The random interference of the traffic network and the impact of the time window of massive cargos on massive-cargo transportation routes are not considered; (2) The conflicts between multiple massive-cargo logistics transportation routes are ignored; (3) The throughput capacity information of massivecargo transportation networks is obtained in advance. According to the problem description, the symbols and definitions of relevant variables are shown in Table 1, and the mathematical model is established as follows.…”

Section: Massive-cargo Logistics Transportation Routing Modelmentioning

confidence: 99%

“…Previous studies of vehicle routes focused on the related problems considering vehicle constraints and related problem variants [1]. Leng et al [2] established a mathematical model for the vehicle routing problem of multiple distribution centers based on the logistics distribution system of urban rail transit and proposed a new concentration immune algorithm-based particle swarm algorithm to solve the vehicle routing problem by combining the concept of antibody concentration. Islam et al [3] proposed a novel hybrid metaheuristic method combining particle swarm optimization algorithm and variable neighborhood search to solve the clustering vehicle route problem.…”

Section: Introductionmentioning

confidence: 99%

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A Massive-Cargo Transportation Routing Model Based on Reinforcement Learning

Wei

2022

Frontiers in Artificial Intelligence and Applications

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Considering the size (length, width, and height) and weight of massive cargos and on the basis of satisfying the throughput capacity of the road network, a massive-cargo transportation routing model was proposed to find the shortest transportation route based on the origin and destination. According to the characteristics of the routing problem, a reinforcement learning algorithm was designed to solve the problem and determine the strategy, reward function, value function, and environment model. Additionally, based on a case study, an optimized transportation plan was developed and the model sensitivity was analyzed to verify the validity and correctness of the proposed model and algorithm.

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Section: Massive-cargo Logistics Transportation Routing Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Massive-Cargo Transportation Routing Model Based on Reinforcement Learning

Wei

2022

Frontiers in Artificial Intelligence and Applications

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“…Vehicle routing problem (VRP) has been researched more and more popular over the past few years. It is widely used in the fields of city transportation [1], supplies distribution [2], and commercial logistics [3]. With global warming, the destruction of the ecological environment is becoming more and more serious, and the frequency of natural disasters is also increasing.…”

Section: Introductionmentioning

confidence: 99%

“…To sum up, the main contributions of this paper are listed below. (1) We propose a mixed-integer linear programming model for the power delivery problem in post-disaster with the consideration of charging operations, which aims at minimizing the sum of arrival time along the paths plus the penalty when the arrival time of vehicles is earlier than the time constraints. (2) A connection between the dynamics of SoC and related time constraints is established, which helps the department of emergency logistics predict the rescue time in advance.…”

Section: Introductionmentioning

confidence: 99%

A two‐stage algorithm for vehicle routing problem with charging relief in post‐disaster

Liu

et al. 2023

IET Intelligent Trans Sys

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This paper first investigates emergency transportation for power recovery in post‐disaster. The problem is formulated as a mixed‐integer linear programming model called vehicle routing problem with charging relief (VRPCR). The battery state of charge () implies the working hours that the battery can provide. The goal is to make a set of shelters charge before the battery of shelters reaches the minimum bound over time. To this end, a two‐stage algorithm is developed to deal with the problem. In stage I, a reduced road network is obtained from a leading road network by the A‐star search algorithm. Subsequently, to determine the order of power delivery with charging operations at shelters by enhanced genetic algorithm (EGA) in stage II. To evaluate this strategy, the detailed complexity analysis of the three algorithms and results tested on a realistic disaster scenario shows the performance of the A‐star search algorithm for VRPCR that outperforms the Dijkstra and Floyd algorithms. In addition, the EGA is applied to Solomon's benchmarks compared with the state‐of‐the‐art heuristic algorithms, which indicates a better performance of EGA. A real case obtained from a disaster scenario in Ichihara City, Japan is also conducted. Simulation results demonstrate that the method can achieve satisfactory solutions.

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“…rough the statistical machine learning algorithm represented by the artificial neural network, the model is directly established based on a large amount of data. By fitting the distribution relationship of variables and targets, the impact mechanism of logistics transportation efficiency can be analyzed from multiple angles and all directions, avoiding the simplification of a large number of factors in the conventional modeling [16]. erefore, the intelligent optimization algorithm used to solve complex optimization problems can often get better results and has potential application value for logistics distribution problems [17].…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Selection Method of Distribution Sites Driven by the Intelligent Optimization Algorithm

Jin¹

2022

Mobile Information Systems

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For the internal enterprise, the intelligent selection of logistics distribution sites can optimize the distribution route, which is conducive to reducing the total distribution cost and improving the enterprise revenue. This paper takes urban logistics distribution point selection optimization as the research content and compares and analyses the applicability of different intelligent algorithms to logistics data. The AFSA is selected as the optimization algorithm, and knowledge learning is introduced to optimize the algorithm. The optimized AFSA model is applied to the mathematical model of distribution point selection. An intelligent algorithm-driven logistics distribution point selection model is established, and the optimized AFSA is used to solve the problem. Based on the actual case of CSC Logistics Company, the route of the distribution point in a region of CSC Logistics Company is optimized and the model is validated and solved. The results show that GWO and AFSA search capabilities are significantly better than of other intelligent algorithms, but there is some instability in the GWO algorithm. The AFSA is most suitable for solving logistics-related problems. The optimized AFSA considering knowledge learning has high efficiency and good optimization results. CSC Company uses this intelligent algorithm to select distribution sites intelligently, which shortens the total logistics path and improves the distribution efficiency. The total mileage of the initial route is 115 km. After intelligent algorithm optimization, the total mileage of the distribution changes to 83 km, which reduces by 32 km and 28% compared with the original route. The whole distribution process saved about 1.5 hours, which fully optimized the efficiency of distribution.

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Distribution Path Optimization for Intelligent Logistics Vehicles of Urban Rail Transportation Using VRP Optimization Model

Cited by 29 publications

References 36 publications

A Massive-Cargo Transportation Routing Model Based on Reinforcement Learning

A Massive-Cargo Transportation Routing Model Based on Reinforcement Learning

A two‐stage algorithm for vehicle routing problem with charging relief in post‐disaster

The Intelligent Selection Method of Distribution Sites Driven by the Intelligent Optimization Algorithm

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