A Collaborative Berth Planning Approach for Disruption Recovery

Traditionally, terminal operators create an initial berthing plan before the arrival of incoming vessels. This plan involves decisions on when and where to load or discharge containers for the calling vessels. However, disruptive unforeseen events (i.e., arrival delays, equipment breakdowns, tides,...

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Bibliographic Details
Published in:IEEE open journal of intelligent transportation systems 2022, Vol.3, p.153-164
Main Authors: Lyu, Xiaohuan, Negenborn, Rudy R., Shi, Xiaoning, Schulte, Frederik
Format: Article
Language:English
Subjects:
Berthing
Collaboration
Collaborative berth planning
Containers
Costs
Cranes
Disruption
disruption recovery
Heuristic methods
Integer programming
metaheuristic
Metaheuristics
Mixed integer
mixed-integer program
Operators
Optimization
Planning
Quays
Recovery
Resource management
Terminals
Uncertainty
Vessels
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Summary:Traditionally, terminal operators create an initial berthing plan before the arrival of incoming vessels. This plan involves decisions on when and where to load or discharge containers for the calling vessels. However, disruptive unforeseen events (i.e., arrival delays, equipment breakdowns, tides, or extreme weather) interfere with the implementation of this initial plan. For terminals, berths and quay cranes are both crucial resources, and their capacity limits the efficiency of port operations. Thus, one way to minimize the adverse effects caused by disruption is to ally different terminals to share berthing resources. In some challenging situations, terminal operators also need to consider the extensive transshipment connections between feeder and mother vessels. Therefore, in this work, we investigate a collaborative variant of the berth allocation recovery problem which focuses on the collaboration among terminals and transshipment connections between vessels. We propose a mixed-integer programming model to (re)-optimize the initial berth and quay crane allocation plan and develop a Squeaky Wheel Optimization metaheuristic to find near-optimal solutions for large-scale instances. The results from the performed computational experiments, considering multiple scenarios with disruptive events, show consistent improvements of up to 40% for the suggested collaborative strategy (in terms of costs for the terminal operators).
ISSN:2687-7813
2687-7813
DOI:10.1109/OJITS.2022.3150585