Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization
<p>Waste collection is an important part of waste management system. Transportation costs and carbon emissions can be greatly reduced by proper vehicle routing. Meanwhile, each vehicle can work again after achieving its capacity limit and unloading the waste. For this, an energy-efficient mult...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-09-01
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| Series: | Complex System Modeling and Simulation |
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| Online Access: | https://www.sciopen.com/article/10.23919/CSMS.2023.0008 |
| _version_ | 1797662822074679296 |
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| author | Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang |
| author_facet | Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang |
| author_sort | Xiaoning Shen |
| collection | DOAJ |
| description | <p>Waste collection is an important part of waste management system. Transportation costs and carbon emissions can be greatly reduced by proper vehicle routing. Meanwhile, each vehicle can work again after achieving its capacity limit and unloading the waste. For this, an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection, which incorporates practical factors like the limited capacity, maximum working hours, and multiple trips of each vehicle. Considering both economy and environment, fixed costs, fuel costs, and carbon emission costs are minimized together. To solve the formulated model effectively, contribution-based adaptive particle swarm optimization is proposed. Four strategies named greedy learning, multi-operator learning, exploring learning, and exploiting learning are specifically designed with their own searching priorities. By assessing the contribution of each learning strategy during the process of evolution, an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the algorithm. Moreover, an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are improved. Performance of the proposed algorithm is tested on ten waste collection instances, which include one real-world case derived from the Green Ring Company of Jiangbei New District, Nanjing, China, and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark datasets. Comparisons with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.</p> |
| first_indexed | 2024-03-11T19:05:37Z |
| format | Article |
| id | doaj.art-bb7e3624742b4d13b93b0172db408a36 |
| institution | Directory Open Access Journal |
| issn | 2096-9929 |
| language | English |
| last_indexed | 2024-03-11T19:05:37Z |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Complex System Modeling and Simulation |
| spelling | doaj.art-bb7e3624742b4d13b93b0172db408a362023-10-10T08:36:01ZengTsinghua University PressComplex System Modeling and Simulation2096-99292023-09-013320221910.23919/CSMS.2023.0008Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm OptimizationXiaoning Shen0Hongli Pan1Zhongpei Ge2Wenyan Chen3Liyan Song4Shuo Wang5Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, and also with the Jiangsu Key Laboratory of Big Data Analysis Technology, School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaGuangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK<p>Waste collection is an important part of waste management system. Transportation costs and carbon emissions can be greatly reduced by proper vehicle routing. Meanwhile, each vehicle can work again after achieving its capacity limit and unloading the waste. For this, an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection, which incorporates practical factors like the limited capacity, maximum working hours, and multiple trips of each vehicle. Considering both economy and environment, fixed costs, fuel costs, and carbon emission costs are minimized together. To solve the formulated model effectively, contribution-based adaptive particle swarm optimization is proposed. Four strategies named greedy learning, multi-operator learning, exploring learning, and exploiting learning are specifically designed with their own searching priorities. By assessing the contribution of each learning strategy during the process of evolution, an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the algorithm. Moreover, an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are improved. Performance of the proposed algorithm is tested on ten waste collection instances, which include one real-world case derived from the Green Ring Company of Jiangbei New District, Nanjing, China, and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark datasets. Comparisons with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.</p>https://www.sciopen.com/article/10.23919/CSMS.2023.0008municipal solid waste collectionenergy conservationmulti-tripcontributionparticle swarm optimization |
| spellingShingle | Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization Complex System Modeling and Simulation municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization |
| title | Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization |
| title_full | Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization |
| title_fullStr | Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization |
| title_full_unstemmed | Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization |
| title_short | Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization |
| title_sort | energy efficient multi trip routing for municipal solid waste collection by contribution based adaptive particle swarm optimization |
| topic | municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization |
| url | https://www.sciopen.com/article/10.23919/CSMS.2023.0008 |
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