Location-Routing Problem With Demand Range
This research proposes a new variant of the location-routing problem (LRP) called LRP with Demand Range (LRPDR) by allowing flexibility in the delivery quantity. The goal of the LRPDR is to minimize the objective value calculated by the total cost minus the extra revenue. The total cost consists of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8862809/ |
| _version_ | 1818877652132429824 |
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| author | Vincent F. Yu Panca Jodiawan Yi-Hsuan Ho Shih-Wei Lin |
| author_facet | Vincent F. Yu Panca Jodiawan Yi-Hsuan Ho Shih-Wei Lin |
| author_sort | Vincent F. Yu |
| collection | DOAJ |
| description | This research proposes a new variant of the location-routing problem (LRP) called LRP with Demand Range (LRPDR) by allowing flexibility in the delivery quantity. The goal of the LRPDR is to minimize the objective value calculated by the total cost minus the extra revenue. The total cost consists of the travelling cost of vehicles, the opening cost of the depots, and the activation cost of vehicles. This study proposes a new hybrid algorithm, SAPSO, that combines simulated annealing (SA) and particle swarm algorithm (PSO) for solving the LRPDR. Since this problem has not yet been studied in the literature, a mathematical model is proposed and solved by the Gurobi solver. The results obtained by Gurobi are then compared with those obtained by the proposed SAPSO algorithm. In addition, the performance of the proposed SAPSO algorithm is assessed by solving the LRP benchmark instances, and comparing the results with those of existing state-of-the-art algorithms for LRP. Based on the experimental results, the proposed SAPSO algorithm improves the performance of the basic SA algorithm and outperforms Gurobi. Moreover, the benefits of the LRPDR over LRP are presented in terms of total cost reduction. |
| first_indexed | 2024-12-19T14:01:41Z |
| format | Article |
| id | doaj.art-48f1aef5f426437da223c637f56860d7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T14:01:41Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-48f1aef5f426437da223c637f56860d72022-12-21T20:18:26ZengIEEEIEEE Access2169-35362019-01-01714914214915510.1109/ACCESS.2019.29462198862809Location-Routing Problem With Demand RangeVincent F. Yu0Panca Jodiawan1Yi-Hsuan Ho2Shih-Wei Lin3https://orcid.org/0000-0003-1343-0838Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Information Management, Chang Gung University, Taoyuan, TaiwanThis research proposes a new variant of the location-routing problem (LRP) called LRP with Demand Range (LRPDR) by allowing flexibility in the delivery quantity. The goal of the LRPDR is to minimize the objective value calculated by the total cost minus the extra revenue. The total cost consists of the travelling cost of vehicles, the opening cost of the depots, and the activation cost of vehicles. This study proposes a new hybrid algorithm, SAPSO, that combines simulated annealing (SA) and particle swarm algorithm (PSO) for solving the LRPDR. Since this problem has not yet been studied in the literature, a mathematical model is proposed and solved by the Gurobi solver. The results obtained by Gurobi are then compared with those obtained by the proposed SAPSO algorithm. In addition, the performance of the proposed SAPSO algorithm is assessed by solving the LRP benchmark instances, and comparing the results with those of existing state-of-the-art algorithms for LRP. Based on the experimental results, the proposed SAPSO algorithm improves the performance of the basic SA algorithm and outperforms Gurobi. Moreover, the benefits of the LRPDR over LRP are presented in terms of total cost reduction.https://ieeexplore.ieee.org/document/8862809/Demand rangehybrid algorithmlocation routing problemparticle swarm algorithmsimulated annealing |
| spellingShingle | Vincent F. Yu Panca Jodiawan Yi-Hsuan Ho Shih-Wei Lin Location-Routing Problem With Demand Range IEEE Access Demand range hybrid algorithm location routing problem particle swarm algorithm simulated annealing |
| title | Location-Routing Problem With Demand Range |
| title_full | Location-Routing Problem With Demand Range |
| title_fullStr | Location-Routing Problem With Demand Range |
| title_full_unstemmed | Location-Routing Problem With Demand Range |
| title_short | Location-Routing Problem With Demand Range |
| title_sort | location routing problem with demand range |
| topic | Demand range hybrid algorithm location routing problem particle swarm algorithm simulated annealing |
| url | https://ieeexplore.ieee.org/document/8862809/ |
| work_keys_str_mv | AT vincentfyu locationroutingproblemwithdemandrange AT pancajodiawan locationroutingproblemwithdemandrange AT yihsuanho locationroutingproblemwithdemandrange AT shihweilin locationroutingproblemwithdemandrange |