Stochastic floating quay crane scheduling on offshore platforms

A simheuristic approach

Conference paper (2019)

Authors

D. Souravlias Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
M.B. Duinkerken Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
S. Morshuis Mocean Offshore BV
D.L. Schott Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
R.R. Negenborn Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Optimization Simulation Offshore platforms Quay crane scheduling
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Published Date

2019

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

The scheduling of quay cranes is a core logistics challenge that affects significantly the loading and unloading time of a vessel berthed at a container terminal. In this paper, we study the Stochastic Floating Quay Crane Scheduling Problem involving cranes situated on the quay of an offshore modular platform. Specifically, we consider the case in which each crane is situated on a different module of the platform, thereby confining its operation range. Additionally, we assume stochastic crane productivity rates due to the effect of the offshore wind. To tackle the problem, we propose a simheuristic framework, which combines Iterated Local Search with Monte Carlo Sampling into a joint collaborative scheme. The main objective is to minimize the expected completion time of the loading and unloading process taking into account precedence, nonsimultaneity, non-crossing, and spatial constraints of the problem at hand. The performance of the proposed simheuristic is investigated on a set of established problem instances across different configuration parameters and under various real-world environmental scenarios offering insightful conclusions.