Evolutionary game of information sharing on supply chain network based on memory genetic algorithm

Tan, Jian; Guo-qiang, Jiang; Wang, Zuogong

doi:10.3166/jesa.50.507-519

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…x ijk = 1 Vehicle k travels directly from nodeito node j 0 Otherwise (14) where x ijk reflects whether vehicle k travels directly from node i to node j. If yes, x ijk = 1; otherwise, x ijk = 0.…”

Section: B Model Constructionmentioning

confidence: 99%

“…Ai et al [12] put forward the VRP with simultaneous pickup and delivery (VRPSPD), solved it by a particle swarm optimization (PSO) algorithm with multiple social structures, and verified the solution using three benchmark datasets. Marinakis and Marinaki [13] successfully solved the VRP through the combination of the genetic algorithm (GA) [14] and the PSO [15]. Archetti [16] designed two exact branch-and-cut algorithms for the split delivery VRP (SDVRP), which excludes any feasible solution to relaxed constraints that does not satisfy the model constraints from the search space of the relaxed problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization

Liu

2020

IEEE Access

View full text Add to dashboard Cite

This paper aims to solve the last-mile distribution of rural e-commerce logistics (RECL) for the survival of third-party logistics enterprise. Considering the features of the RECL (long transport chain and low consumption density), A route optimization model is constructed for RECL's last-mile distribution to maximize the profit of the logistics enterprise, which is subsidized by the government. To solve the model, the ant colony optimization (ACO) was improved to suit the RECL's last-mile distribution by modifying the heuristic information, the update rule of pheromone, and the solution construction. Next, the optimal combinations of the default parameters in the improved ACO were determined through Matlab tests on five test datasets in different sizes. The other parameters were configured according to the scale of the RECL. On this basis, the improved ACO was proved effective through example analysis on the said test datasets. The analysis results also reflect how the number of vehicles affects the maximum profit of the logistics enterprise and the coverage of the RECL logistics network. INDEX TERMS Rural e-commerce logistics (RECL), last-mile distribution, route optimization, ant colony optimization (ACO).

show abstract

Section: B Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization

Liu

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Solving Vehicle Routing Problem: A Big Data Analytic Approach

Zheng

2019

IEEE Access

View full text Add to dashboard Cite

In the transport industry, the cost effectiveness relies heavily on the rational design of the transport routes. However, the traditional theories and methods on vehicle routing problem (VRP) cannot describe the dynamic features of travel time accurately. To solve the problem, this paper puts forward a dynamic VRP model based on big data analysis on traffic flow, and solves it by the genetic algorithm (GA). It is assumed that the real-time traffic data are updated every 15mins in the transport network, and the customer demand is updated dynamically from time to time. The example analysis shows that my model and its route adjustment strategy can minimize the total transport cost by routing the vehicles from multiple depots under the soft time window. The research findings help transport enterprises to make effective use of vehicles and receive more profits.INDEX TERMS Big data analysis, vehicle routing problem (VRP), genetic algorithm (GA), minimum transport cost, vehicle sharing.

show abstract

An improved flower pollination algorithm for optimization of intelligent logistics distribution center

Hu¹

2019

Adv produc engineer manag

View full text Add to dashboard Cite

It is easy to fall into local optimal solution in solving the optimal location of intelligent logistics distribution center by traditional method and the result of optimization is not ideal. For this, the study puts forward an optimization method of intelligent logistics distribution center based on improved flower pollination algorithm. This method uses the logic self-mapping function to carry out chaotic disturbance to the pollen grains, so that the pollen grain set lacking the mutation mechanism has strong self-adaptability, and the convergence of the optimal solution in the later stage of the algorithm is effectively prevented. The boundary buffer factor is used to buffer the cross-boundary pollen grains adaptively so as to prevent the algorithm from the local optimization, and the convergence speed and the optimization accuracy of the algorithm can be improved obviously in processing the optimal location of intelligent logistics distribution center. The convergence of the algorithm is analyzed theoretically by using the real number coding method, and the biological model and theoretical basis of the algorithm are given. The experimental results show that the proposed method has better performance than the traditional one, and the algorithm outperforms a genetic algorithm and particle swarm algorithm. It provides a feasible solution for the intelligent logistics distribution center location strategy. It affords a good reference for improving and optimizing the internal logistics of the manufacturing system and the operational efficiency of the entire intelligent logistics system.

show abstract

Evolutionary game of information sharing on supply chain network based on memory genetic algorithm

Cited by 5 publications

References 0 publications

Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization

Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization

Solving Vehicle Routing Problem: A Big Data Analytic Approach

An improved flower pollination algorithm for optimization of intelligent logistics distribution center

Contact Info

Product

Resources

About