Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization
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 ve...
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| Vydáno v: | Complex System Modeling and Simulation Ročník 3; číslo 3; s. 202 - 219 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Tsinghua University Press
01.09.2023
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| ISSN: | 2096-9929, 2096-9929 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Wang, Shuo Shen, Xiaoning Ge, Zhongpei Chen, Wenyan Song, Liyan Pan, Hongli |
| Author_xml | – sequence: 1 givenname: Xiaoning surname: Shen fullname: Shen, Xiaoning organization: School of Automation, Nanjing University of Information Science and Technology,Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis Technology,Nanjing,China,210044 – sequence: 2 givenname: Hongli surname: Pan fullname: Pan, Hongli organization: Nanjing University of Information Science and Technology,School of Automation,Nanjing,China,210044 – sequence: 3 givenname: Zhongpei surname: Ge fullname: Ge, Zhongpei organization: Nanjing University of Information Science and Technology,School of Automation,Nanjing,China,210044 – sequence: 4 givenname: Wenyan surname: Chen fullname: Chen, Wenyan organization: Nanjing University of Information Science and Technology,School of Automation,Nanjing,China,210044 – sequence: 5 givenname: Liyan surname: Song fullname: Song, Liyan organization: Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China,518055 – sequence: 6 givenname: Shuo surname: Wang fullname: Wang, Shuo organization: University of Birmingham,School of Computer Science,Birmingham,UK,B15 2TT |
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| Title | Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization |
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