A Collaborative Berth Planning Approach for Disruption Recovery

Xiaohuan Lyu,Rudy R Negenborn,Xiaoning Shi,Frederik Schulte

doi:10.1109/ojits.2022.3150585

Xiaohuan Lyu, Rudy R Negenborn + Show 2 more

Open Access

PDF Available

https://doi.org/10.1109/ojits.2022.3150585

Copy DOI

Export

Save

Cite

Abstract
Highlights/Summary
Full-Text PDF
Similar Papers

Abstract

Listen

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).

Highlights

International maritime trade has been greatly increasing over the last decades, and the global container port throughput reached its peak, 811.2 million Twenty-foot Equivalent Units (TEU) in 2019 [1]
P: proactive; R: reactive; UA: uncertainty of arrival time; UH: uncertainty of handling time; QB: quay crane breakdown; TR: transshipment between feeder and mother vessels; CP: collaborative planning; SS: scenario simulation; PD: probability distribution; MIP: mixed integer programming; RO: robustness optimization; SO: stochastic programming; D: discrete; C: continuous; BA: berth allocation problem; Berth Allocation and Quay Crane Assignment Problem (BACAP): berth allocation and quay crane assignment problem; Berth Allocation and Quay Crane Scheduling Problem (BACSP): berth allocation and quay crane scheduling problem the baseline schedule as a reference and propose a MixedInteger Programming (MIP) to minimize the cost incurred by the deviation from the baseline
The concept of Squeaky Wheel Optimization (SWO) is to figure out the ‘bottle neck’ elements which contribute relatively large proportion to the objective value and to give them higher priority during resource allocation to search for better solutions

Summary

Introduction

International maritime trade has been greatly increasing over the last decades, and the global container port throughput reached its peak, 811.2 million Twenty-foot Equivalent Units (TEU) in 2019 [1]. These large volumes require efficient and robust quay-side operations for the calling vessels. The berthing plan determines when and where to load or discharge containers for the calling vessels as well as the number of quay cranes to be allocated. Terminal operators form a weekly berthing plan before the calling of vessels. Uncertainties cannot be ignored, and a well-functioning berthing plan should incorporate both efficiency and disruption recovery [2]

Methods

Results

Conclusion