Deep learning-enhanced quantum optimization for integrated job scheduling in container terminals
This study explored the challenge of multi-equipment collaborative scheduling and management within container terminals, focusing on essential equipment such as reach stackers, automated guided vehicles (AGVs), quay cranes, and yard cranes. The job shop scheduling problem (JSSP) in container handlin...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 148; s. 110431 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
15.05.2025
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| ISSN: | 0952-1976 |
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| Abstract | This study explored the challenge of multi-equipment collaborative scheduling and management within container terminals, focusing on essential equipment such as reach stackers, automated guided vehicles (AGVs), quay cranes, and yard cranes. The job shop scheduling problem (JSSP) in container handling was modeled using a quadratic unconstrained binary optimization (QUBO) formulation. A novel scheduling approach is developed based on the synergistic combination of an artificial intelligence (AI) algorithm and quantum computing (QC) (or AI-QC scheduler). This strategy integrates deep reinforcement learning (DRL) and a quantum approximate optimization algorithm (QAOA) to minimize the makespan (total completion time) with accurate coordination of port equipment. Based on operating scenarios in port environments, the proposed AI-QC scheduler offers significant advantages over traditional methods such as first-in-first-out (FIFO), shortest processing time (SPT), and genetic algorithms (GA), particularly in handling stochastic disturbances in processing times. The hybrid technique based on deep learning, quantum computing, and (DRL-QAOA) has self-optimization capabilities, adjusting to changes in market demand in port operations. The AI-QC scheduler can outperform benchmark algorithms in allocating and scheduling port resources, achieving a lower makespan and enhanced operational efficiency across various scenarios. Robust performance indicates optimizing equipment utilization and enhancing terminal throughput, particularly in unpredictable scenarios. Hence, deep-learning AI-driven quantum optimization strategies can improve the competitiveness and efficiency of container terminal operations in a rapidly evolving global market.
•AI-QC scheduler effectively models the JSSP in container terminals using QUBO.•Hybrid algorithm with DRL-QAOA is presented to optimize port equipment scheduling.•DRL-QAOA approach outperforms FIFO, SPT, and GA under stochastic processing times.•Detailed analysis demonstrates the efficacy of job scheduler in handling JSSP challenges.•Quantum scheduler enhances equipment utilization and productivity, providing visibility. |
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| AbstractList | This study explored the challenge of multi-equipment collaborative scheduling and management within container terminals, focusing on essential equipment such as reach stackers, automated guided vehicles (AGVs), quay cranes, and yard cranes. The job shop scheduling problem (JSSP) in container handling was modeled using a quadratic unconstrained binary optimization (QUBO) formulation. A novel scheduling approach is developed based on the synergistic combination of an artificial intelligence (AI) algorithm and quantum computing (QC) (or AI-QC scheduler). This strategy integrates deep reinforcement learning (DRL) and a quantum approximate optimization algorithm (QAOA) to minimize the makespan (total completion time) with accurate coordination of port equipment. Based on operating scenarios in port environments, the proposed AI-QC scheduler offers significant advantages over traditional methods such as first-in-first-out (FIFO), shortest processing time (SPT), and genetic algorithms (GA), particularly in handling stochastic disturbances in processing times. The hybrid technique based on deep learning, quantum computing, and (DRL-QAOA) has self-optimization capabilities, adjusting to changes in market demand in port operations. The AI-QC scheduler can outperform benchmark algorithms in allocating and scheduling port resources, achieving a lower makespan and enhanced operational efficiency across various scenarios. Robust performance indicates optimizing equipment utilization and enhancing terminal throughput, particularly in unpredictable scenarios. Hence, deep-learning AI-driven quantum optimization strategies can improve the competitiveness and efficiency of container terminal operations in a rapidly evolving global market.
•AI-QC scheduler effectively models the JSSP in container terminals using QUBO.•Hybrid algorithm with DRL-QAOA is presented to optimize port equipment scheduling.•DRL-QAOA approach outperforms FIFO, SPT, and GA under stochastic processing times.•Detailed analysis demonstrates the efficacy of job scheduler in handling JSSP challenges.•Quantum scheduler enhances equipment utilization and productivity, providing visibility. |
| ArticleNumber | 110431 |
| Author | Kim, Hwan-Seong Bao Long, Le Ngoc Cuong, Truong Ngoc Tan, Nguyen Duy You, Sam-Sang |
| Author_xml | – sequence: 1 givenname: Truong Ngoc orcidid: 0000-0003-3338-0225 surname: Cuong fullname: Cuong, Truong Ngoc organization: Department of Mechatronics, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Viet Nam – sequence: 2 givenname: Hwan-Seong orcidid: 0009-0003-6167-5167 surname: Kim fullname: Kim, Hwan-Seong organization: Department of Logistics, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan, 49112, Republic of Korea – sequence: 3 givenname: Le Ngoc orcidid: 0000-0002-4588-2786 surname: Bao Long fullname: Bao Long, Le Ngoc organization: Department of Convergence Interdisciplinary Education of Maritime & Ocean Contents (Logistics system), Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan, 49112, Republic of Korea – sequence: 4 givenname: Sam-Sang orcidid: 0000-0003-2660-4630 surname: You fullname: You, Sam-Sang email: ssyou@kmou.ac.kr organization: Northeast-Asia Shipping and Port Logistics Research Center, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan, 49112, Republic of Korea – sequence: 5 givenname: Nguyen Duy surname: Tan fullname: Tan, Nguyen Duy organization: Department of Logistics, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan, 49112, Republic of Korea |
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