Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals
Although dynamic, energy-efficient container-supply hybrid flow shops have attracted increasing attention, most existing research overlooks how transportation within container production affects makespan, resilience, and sustainability. To bridge this gap, we frame a resilient, energy-efficient cont...
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1153 |
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| author | Huaixia Shi Huaqiang Si Jiyun Qin |
| author_facet | Huaixia Shi Huaqiang Si Jiyun Qin |
| author_sort | Huaixia Shi |
| collection | DOAJ |
| description | Although dynamic, energy-efficient container-supply hybrid flow shops have attracted increasing attention, most existing research overlooks how transportation within container production affects makespan, resilience, and sustainability. To bridge this gap, we frame a resilient, energy-efficient container-supply hybrid flow shop (TDEHFSP) scheduling model that utilizes vehicle transportation to maximize operational efficiency. To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). The algorithm integrates a time gap left-shift scheduling strategy with a machine on–off control mechanism to construct an energy-saving optimization framework. Additionally, a predictive–reactive dynamic rescheduling model is introduced to address unexpected task disturbances. To validate the algorithm’s effectiveness, 36 benchmark test cases with varying scales are designed for horizontal comparison. Results show that the proposed Q-MGCOA outperforms benchmarks on convergence, diversity, and supply-chain resilience while lowering energy utilization. Moreover, it achieves about an 8% reduction in energy consumption compared to traditional algorithms. These findings reveal actionable insights for next-generation intelligent, low-carbon container production. |
| format | Article |
| id | doaj-art-1dd2aa44ab8249bb833b769db419691b |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-1dd2aa44ab8249bb833b769db419691b2025-08-20T03:27:28ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01136115310.3390/jmse13061153Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic ArrivalsHuaixia Shi0Huaqiang Si1Jiyun Qin2Logistics Engineering College, Shanghai Maritime University, Pudong, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Pudong, Shanghai 201306, ChinaChina Institute of FTZ Supply Chain, Shanghai Maritime University, Pudong, Shanghai 201306, ChinaAlthough dynamic, energy-efficient container-supply hybrid flow shops have attracted increasing attention, most existing research overlooks how transportation within container production affects makespan, resilience, and sustainability. To bridge this gap, we frame a resilient, energy-efficient container-supply hybrid flow shop (TDEHFSP) scheduling model that utilizes vehicle transportation to maximize operational efficiency. To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). The algorithm integrates a time gap left-shift scheduling strategy with a machine on–off control mechanism to construct an energy-saving optimization framework. Additionally, a predictive–reactive dynamic rescheduling model is introduced to address unexpected task disturbances. To validate the algorithm’s effectiveness, 36 benchmark test cases with varying scales are designed for horizontal comparison. Results show that the proposed Q-MGCOA outperforms benchmarks on convergence, diversity, and supply-chain resilience while lowering energy utilization. Moreover, it achieves about an 8% reduction in energy consumption compared to traditional algorithms. These findings reveal actionable insights for next-generation intelligent, low-carbon container production.https://www.mdpi.com/2077-1312/13/6/1153container productionresilienceintelligent schedulingsustainabilityhybrid flow shopenergy efficiency |
| spellingShingle | Huaixia Shi Huaqiang Si Jiyun Qin Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals Journal of Marine Science and Engineering container production resilience intelligent scheduling sustainability hybrid flow shop energy efficiency |
| title | Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals |
| title_full | Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals |
| title_fullStr | Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals |
| title_full_unstemmed | Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals |
| title_short | Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals |
| title_sort | energy efficient scheduling for resilient container supply hybrid flow shops under transportation constraints and stochastic arrivals |
| topic | container production resilience intelligent scheduling sustainability hybrid flow shop energy efficiency |
| url | https://www.mdpi.com/2077-1312/13/6/1153 |
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