A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management
<i>Background</i>: The logistics network design with cross-docking operations enables shipping service providers to integrate the physical flow of products between vendors and dealers in logistics management. The collective goal is to synchronize the goods in both pickup and delivery ope...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Logistics |
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| Online Access: | https://www.mdpi.com/2305-6290/6/3/62 |
| _version_ | 1797485612886917120 |
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| author | Shih-Che Lo |
| author_facet | Shih-Che Lo |
| author_sort | Shih-Che Lo |
| collection | DOAJ |
| description | <i>Background</i>: The logistics network design with cross-docking operations enables shipping service providers to integrate the physical flow of products between vendors and dealers in logistics management. The collective goal is to synchronize the goods in both pickup and delivery operations concurrently to reduce the handling cost, inventory cost, and operation cost generated. Therefore, the optimal vehicle routing plan is crucial to generate a truck routing schedule with minimal total cost, fulfilling the purchasing requirements and the distribution demand. Global warming and climate change are important topics due to increasing greenhouse gas emissions. Sustainable logistics management with optimized routes for trucks can assist in reducing greenhouse gas emissions and easing the effects of temperature increases on our living environment. <i>Methods</i>: A heuristic approach based on Particle Swarm Optimization, called ePSO, was proposed and implemented in this paper to solve the vehicle routing problems with cross-docking and carbon emissions reduction at the same time. <i>Results</i>: Performance comparisons were made with the Genetic Algorithm (GA) through the experiments of several vehicle routing problems with pickup and delivery benchmark problems to validate the performance of the ePSO procedure. <i>Conclusions</i>: Experimental results showed that the proposed ePSO approach was better than the GA for most cases by statistical hypothesis testing. |
| first_indexed | 2024-03-09T23:22:17Z |
| format | Article |
| id | doaj.art-3d6a8604f1dd47b9934afc48b16f1619 |
| institution | Directory Open Access Journal |
| issn | 2305-6290 |
| language | English |
| last_indexed | 2024-03-09T23:22:17Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Logistics |
| spelling | doaj.art-3d6a8604f1dd47b9934afc48b16f16192023-11-23T17:25:07ZengMDPI AGLogistics2305-62902022-09-01636210.3390/logistics6030062A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics ManagementShih-Che Lo0Department of Industrial Management, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan<i>Background</i>: The logistics network design with cross-docking operations enables shipping service providers to integrate the physical flow of products between vendors and dealers in logistics management. The collective goal is to synchronize the goods in both pickup and delivery operations concurrently to reduce the handling cost, inventory cost, and operation cost generated. Therefore, the optimal vehicle routing plan is crucial to generate a truck routing schedule with minimal total cost, fulfilling the purchasing requirements and the distribution demand. Global warming and climate change are important topics due to increasing greenhouse gas emissions. Sustainable logistics management with optimized routes for trucks can assist in reducing greenhouse gas emissions and easing the effects of temperature increases on our living environment. <i>Methods</i>: A heuristic approach based on Particle Swarm Optimization, called ePSO, was proposed and implemented in this paper to solve the vehicle routing problems with cross-docking and carbon emissions reduction at the same time. <i>Results</i>: Performance comparisons were made with the Genetic Algorithm (GA) through the experiments of several vehicle routing problems with pickup and delivery benchmark problems to validate the performance of the ePSO procedure. <i>Conclusions</i>: Experimental results showed that the proposed ePSO approach was better than the GA for most cases by statistical hypothesis testing.https://www.mdpi.com/2305-6290/6/3/62sustainable logistics managementcross-dockingparticle swarm optimizationvehicle routing problem |
| spellingShingle | Shih-Che Lo A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management Logistics sustainable logistics management cross-docking particle swarm optimization vehicle routing problem |
| title | A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management |
| title_full | A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management |
| title_fullStr | A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management |
| title_full_unstemmed | A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management |
| title_short | A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management |
| title_sort | particle swarm optimization approach to solve the vehicle routing problem with cross docking and carbon emissions reduction in logistics management |
| topic | sustainable logistics management cross-docking particle swarm optimization vehicle routing problem |
| url | https://www.mdpi.com/2305-6290/6/3/62 |
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