Hyper Heuristic for Solving Quay Crane Scheduling Problem

Abstract

Quay cranes scheduling at container terminals is a fertile area of study that is attracting researchers as well as practitioners in different parts of the world especially in OR and AI. The efficiency of this process may significantly affect the general accomplishment and the competitive merits of the terminal as well. In this work, four Local Search algorithms (LSs) are utilized to address Quay crane scheduling problem (QCSP), these are hill climbing (HC), simulated annealing (SA), tabu search (TS) and iterated local search (ILS). The experimental results obtained in the present work demonstrated that none of these LSs succeeded to achieve good results on all available instances. This is because different QCSP instances have different characteristics, in addition to the NP-hardness nature of these instances. As a result, it is difficult to determine which LS has the ability to yield the best available results for all instances. Consequently, selecting the appropriate LS should depend on the problem instance type as well as the search status. To achieve such a selection, this work proposes the hyper heuristic algorithm (hyper-H). The hyper-H is composed of two separate stages: the upper (LS-controller) and the lower (QCSP-solver). The LS-controller embed an adaptive selection mechanism to adaptively select which LS is to be adopted by the QCSP-solver to solve the given problem. The efficiency of the proposed methods were tested on benchmark instances suggested by Meisel and Bierwirth (2011). The experimental results revealed that the hyper-H outperformed others as it attained better results over most instances and competitive results in some others.