Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals
Container terminals (CTs) play an important role in the modern logistics and transportation industry. The utilization of automated guided vehicles (AGVs) can be effectively facilitated by reducing their empty running. The existing strategies cannot guarantee the full load of AGVs during their transp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/4820 |
| _version_ | 1797501939051659264 |
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| author | Hongchang Zhang Liang Qi Wenjing Luan Huijuan Ma |
| author_facet | Hongchang Zhang Liang Qi Wenjing Luan Huijuan Ma |
| author_sort | Hongchang Zhang |
| collection | DOAJ |
| description | Container terminals (CTs) play an important role in the modern logistics and transportation industry. The utilization of automated guided vehicles (AGVs) can be effectively facilitated by reducing their empty running. The existing strategies cannot guarantee the full load of AGVs during their transportation because of the complex constraints of container scheduling. This work proposes a double-cycling AGV scheduling model that ensures a full load of AGVs between the quayside and the yard. The objective is to minimize the total waiting time of AGVs and ensure a high loading rate of AGVs by scheduling loading/unloading containers. Furthermore, it takes the randomness of the quay crane’s operational time into consideration. By assigning a time interval to AGVs’ arrival at a quayside, a container scheduling sequence is obtained based on a Hybrid Particle Swarm Optimization (HPSO) algorithm with a penalty function. Via experiments, it shows that the proposed model can obtain the least number of AGVs for container transportation, minimize AGVs’ total waiting time, and ensure the high loading rate of AGVs. |
| first_indexed | 2024-03-10T03:25:45Z |
| format | Article |
| id | doaj.art-ce4038848c5541f5a1fbe241228214de |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:25:45Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ce4038848c5541f5a1fbe241228214de2023-11-23T09:53:38ZengMDPI AGApplied Sciences2076-34172022-05-011210482010.3390/app12104820Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container TerminalsHongchang Zhang0Liang Qi1Wenjing Luan2Huijuan Ma3College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaQingdao New Qianwan Container Terminal Company Ltd., Qingdao 266500, ChinaContainer terminals (CTs) play an important role in the modern logistics and transportation industry. The utilization of automated guided vehicles (AGVs) can be effectively facilitated by reducing their empty running. The existing strategies cannot guarantee the full load of AGVs during their transportation because of the complex constraints of container scheduling. This work proposes a double-cycling AGV scheduling model that ensures a full load of AGVs between the quayside and the yard. The objective is to minimize the total waiting time of AGVs and ensure a high loading rate of AGVs by scheduling loading/unloading containers. Furthermore, it takes the randomness of the quay crane’s operational time into consideration. By assigning a time interval to AGVs’ arrival at a quayside, a container scheduling sequence is obtained based on a Hybrid Particle Swarm Optimization (HPSO) algorithm with a penalty function. Via experiments, it shows that the proposed model can obtain the least number of AGVs for container transportation, minimize AGVs’ total waiting time, and ensure the high loading rate of AGVs.https://www.mdpi.com/2076-3417/12/10/4820container terminals (CTs)automated guided vehicles (AGVs)hybrid particle swarm optimization (HPSO) algorithmdouble-cyclingcontainer scheduling sequence |
| spellingShingle | Hongchang Zhang Liang Qi Wenjing Luan Huijuan Ma Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals Applied Sciences container terminals (CTs) automated guided vehicles (AGVs) hybrid particle swarm optimization (HPSO) algorithm double-cycling container scheduling sequence |
| title | Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals |
| title_full | Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals |
| title_fullStr | Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals |
| title_full_unstemmed | Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals |
| title_short | Double-Cycling AGV Scheduling Considering Uncertain Crane Operational Time at Container Terminals |
| title_sort | double cycling agv scheduling considering uncertain crane operational time at container terminals |
| topic | container terminals (CTs) automated guided vehicles (AGVs) hybrid particle swarm optimization (HPSO) algorithm double-cycling container scheduling sequence |
| url | https://www.mdpi.com/2076-3417/12/10/4820 |
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