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• W2964984777 abstract "Scheduling is a decision-making process, which is employed to allocate resources totasks in a given time. Scheduling problems are in general NP-hard. In order to solvescheduling 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 toaddress the practical-sized problems effectively with regard to both CPU times andsolution quality. LS algorithms have recently attracted much more attention because oftheir simplicity, being easy to implement, robustness, and high effectiveness. However,the available LS algorithms in the literature typically use a generic structure for speci ficproblems. In other words, the biggest disadvantage of those methods is the lack ofproblem speci fic components into their algorithmic structures. To ll in this gap, in thisthesis, we consider constraint-based local search (CBLS) algorithms to solve schedulingproblems because of their effectiveness and also because they are not used much in thescheduling literature. The key difference of CBLS with other LS algorithms is in the useof the problem specifi c information in the search process. CBLS helps the search focusmore on areas where efforts will bring more effect, and thus increase the scalability ofthe 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 thesearch 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 blockingPFSP (MBPFSP) is a generalised and more realistic version of PFSP with real-lifeapplications such as cider industry. MBPFSP is an important branch of `zero capacitybuffer' scheduling problems. The second scheduling problem is aircraft schedulingproblem (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 andmultiple-runway ASP cases. The third scheduling problem is customer order schedulingproblem (COSP), which has many applications including the pharmaceutical industriesand the paper industries.All of the three above-mentioned scheduling problems are NP-hard. They have madesigni ficant progress in recent years. However, within practical time limits, existing algorithmsstill 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 ficstructural knowledge, extracted from the constraints and the objective functions. Werun 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." @default.
• W2964984777 created "2019-08-13" @default.
• W2964984777 creator A5062208062 @default.
• W2964984777 date "2019-07-24" @default.
• W2964984777 modified "2023-09-27" @default.
• W2964984777 title "Constraint Directed Scheduling" @default.
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• W2964984777 doi "https://doi.org/10.25904/1912/1826" @default.
• W2964984777 hasPublicationYear "2019" @default.
• W2964984777 type Work @default.

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