Scheduling offshore cargo handling with operational dynamics including cost composites, downtime periods, and cargo attributes

Byung Kwon Lee (), Seyyed Amir Babak Rasmi () and Jakkarin Sae-Tiew ()
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Byung Kwon Lee: Korea Railroad Research Institute
Seyyed Amir Babak Rasmi: Curtin University
Jakkarin Sae-Tiew: National University of Singapore

Flexible Services and Manufacturing Journal, 2024, vol. 36, issue 1, No 10, 279-314

Abstract: Abstract An efficient cargo logistics service for vessels anchored offshore becomes imperative to meet the increasing demand of transporting dry bulk cargoes and overcome insufficient infrastructural resources of ports. Offshore cargo handling service providers propose a fleet of offshore floating terminals (OFTs) performing the cargo handling service directly nearby anchorages at sea. This approach reduces the time and the usage of resources compared with relying on ports. In this paper, we develop a scheduling mathematical model for the fleet of OFTs to improve the operating efficiency in serving cargo vessels and their barges. This study proposes the model with various objective functions that can be used for a service provider depending on its goals and customer satisfaction policies. This study introduces real-world considerations inspired by an offshore service provider in Singapore and addresses complicated combinations of practical requirement owing to cargo/vessel/operation attributes, maintenance periods, and operating cost components in development of an optimization model via mixed-integer linear programming. An efficient genetic algorithm is also developed to solve the model with finding near-optimal effective solutions. Through examining a large set of real data provided by the service provider, the proposed genetic algorithm shows a high effectiveness in terms of solution quality and computational time.

Keywords: Offshore cargo logistics; Offshore floating terminals; Dry bulk cargo; Scheduling; Mixed-integer linear programming; Genetic algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10696-022-09482-7

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