Simultaneous planning of liner ship speed optimization, scheduling and fleet deployment with container transhipment

Owing to substantial growth in global waterborne trade volumes and changes in climate, shipping companies must enhance operational and energy efficiency. A multi-objective mixed-integer nonlinear programming (MINLP) model is proposed to optimize service schedules, fleet, vessel speed, and cargo flow...

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Vydáno v:Engineering optimization Ročník 57; číslo 10; s. 3064 - 3100
Hlavní autoři: Mandal, Jasashwi, Goswami, Adrijit, Thakur, Lakshman, Tiwari, Manoj Kumar, Bhide, Nitish
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 03.10.2025
Taylor & Francis Ltd
Témata:
Cargo ships
Clustering
container transhipment
Decisions
Evolutionary algorithms
fleet deployment
Genetic algorithms
Liner ship speed optimization
Mixed integer
multi-objective evolutionary algorithm (MOEA)
Multiple objective analysis
Nonlinear programming
Optimization
payload-speed dependent fuel consumption
Scheduling
Shipping
Sorting algorithms
Tradeoffs
ISSN:0305-215X, 1029-0273
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Shrnutí:Owing to substantial growth in global waterborne trade volumes and changes in climate, shipping companies must enhance operational and energy efficiency. A multi-objective mixed-integer nonlinear programming (MINLP) model is proposed to optimize service schedules, fleet, vessel speed, and cargo flow, including transhipment operations. Innovative features of this research reside in the multi-objective model formulation that integrates these complex and crucial operational decisions of the maritime industry. This MINLP model presents a trade-off between economic and environmental aspects considering shipping time and shipping cost as the two conflicting objectives. Two evolutionary algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Online Clustering-based Evolutionary Algorithm (OCEA), are applied to attain the near-optimal solution. The results indicate that the proposed model can contribute to saving fuel costs, reducing emissions and finding trade-offs between shipping cost and time. Furthermore, the study reflects how shipping companies can use this model to make data-driven decisions in their operations.
Bibliografie:ObjectType-Article-1
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2024.2424370