Constraint Directed Scheduling

Abstract

Scheduling is a decision-making process, which is employed to allocate resources to tasks in a given time. Scheduling problems are in general NP-hard. In order to solve scheduling problems, three common types of methods have been used: exact methods (e.g., branch & bound and dynamic programming), population based metaheuristics (e.g., genetic algorithm and ant colony optimisation), and local search (LS) algorithms (e.g., simulated annealing and iterated local search). Exact methods are not able to address the practical-sized problems effectively with regard to both CPU times and solution quality. LS algorithms have recently attracted much more attention because of their simplicity, being easy to implement, robustness, and high effectiveness. However, the available LS algorithms in the literature typically use a generic structure for speci fic problems. In other words, the biggest disadvantage of those methods is the lack of problem speci fic components into their algorithmic structures. To ll in this gap, in this thesis, we consider constraint-based local search (CBLS) algorithms to solve scheduling problems because of their effectiveness and also because they are not used much in the scheduling literature. The key difference of CBLS with other LS algorithms is in the use of the problem specifi c information in the search process. CBLS helps the search focus more on areas where efforts will bring more effect, and thus increase the scalability of the search. In other words, CBLS attempts to exploit the essence of the problem and, based on the speci ficities of the problem, defi nes the procedures that will guide the search towards better local optima. The effectiveness of our proposed CBLS techniques is shown throughout this thesis by solving several scheduling problems, such as flowshops with blocking constraints, aircraft operations, and customer order problems. The first scheduling problem is permutation flowshop scheduling problem (PFSP). It is one of the most thoroughly studied scheduling problems. However, mixed blocking PFSP (MBPFSP) is a generalised and more realistic version of PFSP with real-life applications such as cider industry. MBPFSP is an important branch of `zero capacity buffer' scheduling problems. The second scheduling problem is aircraft scheduling problem (ASP). ASP involves allocation of aircraft to runways for arrival and departure flights, minimising total delays. In this thesis, we focus on both single-runway and multiple-runway ASP cases. The third scheduling problem is customer order scheduling problem (COSP), which has many applications including the pharmaceutical industries and the paper industries. All of the three above-mentioned scheduling problems are NP-hard. They have made signi ficant progress in recent years. However, within practical time limits, existing algorithms still either find low quality solutions or struggle with practical-sized problems. In this thesis, we aim to advance their search by better exploiting the problem speci fic structural knowledge, extracted from the constraints and the objective functions. We run our experiments on a range of respective standard benchmark problem instances. Experimental results and comprehensive analyses show that our new algorithms signi ficantly outperform respective state-of-the-art scheduling algorithms.