A Scheduling Optimization Approach for Heterogeneous Feeder Container Fleet with User Timeliness Satisfaction

Jun Jiang; Mingjie Li

doi:10.54097/h0bp3240

Authors

Jun Jiang
Mingjie Li

DOI:

https://doi.org/10.54097/h0bp3240

Keywords:

User Timeliness Satisfaction, Heterogeneous Fleet, Feeder Container Ships, Ship Scheduling, Adaptive Single-valued Veutrosophic Large Neighborhood Search Algorithm

Abstract

Against the background of global trade expansion and the shipping industry’s low-carbon transition, feeder transportation faces challenges in delivery timeliness and scheduling heterogeneous fleets composed of vessels powered by different fuel types. To address this issue, this study takes oil-fueled feeder container ships (OFSs) and LNG-fueled feeder container ships (LFSs) as examples to investigate the impact of actual cargo delivery times on users’ irrational psychological behaviors, as well as to propose solutions for heterogeneous feeder fleet scheduling problems. Specifically, the main contributions of this research are as follows. Firstly, a heterogeneous feeder container fleet operation strategy is proposed, integrating OFSs and LFSs to reduce carbon emissions and enhance operational efficiency. Secondly, an optimization model of heterogeneous feeder container fleet with user timeliness satisfaction (HFCF-UTS) is constructed. This model quantitatively measures user timeliness satisfaction by combining prospect theory and fuzzy time windows. Thirdly, an adaptive single-valued neutrosophic large neighborhood search (ASVNLNS) algorithm is designed. This algorithm adopts single-valued neutrosophic sets (SvNSs) and an improved utility function to update the operator scoring mechanism and ultimately achieve rationalized operator scoring. Finally, based on the real operation data of a Norwegian shipping company, the calculation results show that the proposed algorithm reasonably improves the user timeliness satisfaction and reduces the total turnaround cost, outperforming the adaptive large neighborhood search algorithm.

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