Incorporation of energy-consumption optimization into multi-objective and robust port multi-equipment integrated scheduling

Port operational efficiency and energy consumption are pivotal, but sometimes contradictory factors influencing its competitiveness. In light of this, the simultaneous optimization of these two objectives within the port integrated scheduling of quay cranes, internal vehicles, and yard cranes, can aid in sustaining port development in the era of digitalization and autonomy. Furthermore, given the persistent fluctuations in uncertain operation time of the cranes and vehicles in port, it becomes imperative to consider the robustness of their scheduling plans collectively. This paper therefore aims to develop a new tri-objective mixed-integer programming model for the first time that enables the incorporation of operational uncertainty and energy efficiency into the context of port operation scheduling consideration. The three objectives are makespan, energy consumption, and scheduling plan robustness, which is represented by anti-cascade and robustness evaluation indices. To effectively address complex optimization challenges, a novel multi-objective solution algorithm has been developed, featured with a dynamic fitness evaluation method selection mechanism. This mechanism utilizes a new crowding distance operator based on the cosine distance of objective value vectors to enhance population diversity in the early stages of the algorithm’s iterations. At the later stages, it employs a fuzzy correlation entropy operator to ensure rapid convergence and high-quality solutions. Comparative experiments conducted in scenarios involving emerging technologies such as U-shaped ports and double-cycling operational mode demonstrate the evident improvements achieved by the new model in terms of makespan, energy consumption, and computational efficiency. Based on the compelling experimental results, meaningful insights and implications are put forward, including the potential time and energy savings in port operations, and the practical applicability of these models and algorithms in both port and various other industries.

• Record URL:
  • https://doi.org/10.1016/j.trc.2024.104755
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X24002766
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Cai, Lei
  • 0000-0001-5050-7641
  • Li, Wenfeng
  • 0000-0001-5493-7200
  • Li, Huanhuan
  • 0000-0002-4293-4763
  • Zhou, Bo
  • 0000-0002-7824-3673
  • He, Lijun
  • Guo, Wenjing
  • 0000-0002-6824-7136
  • Yang, Zaili
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 104755
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 166
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Cranes; Energy consumption; Optimization; Port operations; Scheduling; Uncertainty
• Geographic Terms: China
• Subject Areas: Freight Transportation; Operations and Traffic Management; Terminals and Facilities; Vehicles and Equipment;

Filing Info

• Accession Number: 01926244
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 31 2024 4:07PM