Jannes Verstichel

A Late Acceptance Algorithm for the Lock Scheduling Problem

In this paper we present a number of (meta-)heuristics for tackling the lock scheduling problem. The lock scheduling problem is introduced as the problem of minimising both the water usage of the lock and the waiting time of all the ships. The performance of several local search heuristics with the best improving criterion and the late acceptance criterion is examined. First parameter values for the late acceptance list length are determined.With these values all heuristics and criteria are applied to a large test set. From these experiments we conclude that the performance of the heuristics is increased by the late acceptance criterion.

Exact and heuristic methods for placing ships in locks

The ship placement problem constitutes a daily challenge for planners in tide river harbours. In essence, it entails positioning a set of ships into as few lock chambers as possible while satisfying a number of general and specific placement constraints. These constraints make the ship placement problem different from traditional 2D bin packing. A mathematical formulation for the problem is presented. In addition, a decomposition model is developed which allows for computing optimal solutions in a reasonable time. A multi-order best fit heuristic for the ship placement problem is introduced, and its performance is compared with that of the left-right-left-back heuristic. Experiments on simulated and real-life instances show that the multi-order best fit heuristic beats the other heuristics by a landslide, while maintaining comparable calculation times. Finally, the new heuristic’s optimality gap is small, while it clearly outperforms the exact approach with respect to calculation time.

The Multi-Mode Resource-Constrained Multi-Project Scheduling Problem

Scheduling projects is a difficult and time consuming process, and has far-reaching implications for any organization’s operations. By generalizing various aspects of project scheduling, decision makers are enabled to capture reality and act accordingly. In the context of the MISTA 2013 conference, the first MISTA challenge, organized by the authors, introduced such a general problem model: the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP). The present paper reports on the competition and provides a discussion on its results. Furthermore, it provides an analysis of the submitted algorithms, and a study of their common elements. By making all benchmark datasets and results publicly available, further research on the MRCMPSP is stimulated.

An improved best-fit heuristic for the orthogonal strip packing problem

The best-fit heuristic is a simple and powerful tool for solving the two-dimensional orthogonal strip packing problem. It is the most efficient constructive heuristic on a wide range of rectangular strip packing benchmark problems. In this paper, the results of the original best-fit heuristic are further improved by adding new item orderings and item placement strategies, resulting in the three-way best-fit heuristic. By applying these steps, significantly better results are obtained in comparable computation time. Furthermore, some data structures are implemented, which increase the scalability of the heuristic for large problem instances and a slightly altered heuristic with an optimal O(n log n) time complexity is proposed. Both heuristics produce similar results, but the optimal time heuristic is significantly faster than the three-way best-fit heuristic on all but the smallest instances.

An effective shaking procedure for 2D and 3D strip packing problems

Strip packing intends to minimise the height required for placing a set of rectangular items into a strip with infinite height. The present paper introduces a fast, yet effective shaking algorithm for the two- and three-dimensional strip packing problems, which are both NP-hard. The proposed heuristic procedure starts from an ordered item list, from which it alternates between forward and backward construction phases. The algorithm builds upon the common (deepest) bottom-left-fill algorithm, but shows significantly better results. Improvements on the solution quality of more than 9% can be observed. Moreover, applying the shaking procedure as a post processing algorithm to existing high performance heuristics also leads to improvements.

Scheduling Serial Locks: A Green Wave for Waterbound Logistics

The present chapter focuses on locks and their impact on (inland) waterbound logistics. Examples of lock systems are given, and the main characteristics of the serial lock scheduling problem discussed.