Low-carbon logistics distribution route planning with improved particle swarm optimization algorithm

Zhang, Keming; Qiu, Bin; Mu, Dong

doi:10.1109/liss.2016.7854443

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The method is simple and rough, and the calculation accuracy of the influence value on the road condition is very low. In the route optimization, congestion and section gradient are taken into account, and the equivalent consumption is transformed into a flat road with a certain length, which is optimized by the traditional algorithm [31][32][33]. There is also a logistics distribution route optimization algorithm based on travel time prediction by using the historical average method to predict the road travel time [34][35].…”

Section: Complexity Of Logistics Distribution Route Optimizationmentioning

confidence: 99%

Research on logistics distribution route optimization based on deep learning model and block chain technology

Yang¹,

Guan²

2023

3C Empresa

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The growing data age is reflected in all aspects of today's society. In the field of logistics, especially when the road conditions in urban areas are complex, how to select the optimal distribution path and reduce the distribution time is a problem worthy of attention. Aiming at the problems faced by traditional algorithms in solving the distribution of logistics vehicles in urban areas, however, the method based on regional chain technology can better solve the path optimization problem. A deep reinforcement learning algorithm based on attention mechanism and LSTM model is designed and applied to the distribution path planning of logistics vehicles. The distribution optimization path of logistics vehicles is obtained through sample training experiments, Thus, it provides a new idea for the optimization of logistics distribution path.

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Section: Complexity Of Logistics Distribution Route Optimizationmentioning

confidence: 99%

Research on logistics distribution route optimization based on deep learning model and block chain technology

Yang¹,

Guan²

2023

3C Empresa

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show abstract

“…In order to verify the overall performance of REDMOFWA, two representative multi-objective optimization algorithms that are Green Vehicle Routing Problem (G-VRP) [21] and Bi-objective NSGA-II [6] Table 1 Parameter settings in the low-carbon VRP model.…”

Section: Validating the Overall Performance Of The Proposed Algorithmmentioning

confidence: 99%

“…Therefore, more and more metaheuristic algorithms are applied to low-carbon VRP. De Oliveira Da Costa et al [21] adopted a genetic algorithm (GA) to solve green VRP model, which reduces carbon emissions effectively in the transportation. Zhang et al [22] designed an improved particle swarm optimization to optimize vehicle transportation costs and carbon emissions in the lowcarbon VRP.…”

Section: Introductionmentioning

confidence: 99%

A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

Shen

You

et al. 2022

Complex Syst. Model. Simul.

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A constrained multi-objective optimization model for the low-carbon vehicle routing problem (VRP) is established. A carbon emission measurement method considering various practical factors is introduced. It minimizes both the total carbon emissions and the longest time consumed by the sub-tours, subject to the limited number of available vehicles. According to the characteristics of the model, a region enhanced discrete multi-objective fireworks algorithm is proposed. A partial mapping explosion operator, a hybrid mutation for adjusting the sub-tours, and an objective-driven extending search are designed, which aim to improve the convergence, diversity, and spread of the non-dominated solutions produced by the algorithm, respectively. Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies. Furthermore, comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP. It provides a promising scalability to the problem size.

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Shortest Path Searching for Logistics Based on Simulated Annealing Algorithm

Chen

et al. 2020

Advances in Intelligent Systems and Computing

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Low-carbon logistics distribution route planning with improved particle swarm optimization algorithm

Cited by 4 publications

References 8 publications

Research on logistics distribution route optimization based on deep learning model and block chain technology

Research on logistics distribution route optimization based on deep learning model and block chain technology

A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

Shortest Path Searching for Logistics Based on Simulated Annealing Algorithm

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