Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/27631

Self-learning approaches based on genetic algorithm for single-stage multi-product scheduling with compound objective

Authors He, Yaohua HKUST affiliated (currently or previously)
Hui, Chi Wai View this author's profile
Issue Date 2006
Source CHISA 2006 - 17th International Congress of Chemical and Process Engineering , 2006
Summary Process scheduling shows much more complexity than machine scheduling, and it has been widely studied mainly by using mathematic programming (MP). Due to the difficulties for MP to solve large-size problems, simple rule-base methods are often used in the industry. However, due to the constraints existing in some scheduling problems, the simple rule-based method may not guarantee the feasibility and optimality of the solution. By random search combined with suitable heuristic rules, better feasible solutions can be acquired than the simple rule-based method. Meta-heuristic methods, such as genetic algorithm and tabu search, combined with suitable heuristic rules, are effective to obtain near-optimal solution for large-size problems. The use of good heuristic rules in random search and meta-heuristic methods is crucial to reduce the solution space. Traditionally, great simulation experiments are needed to select suitable rules for diverse scheduling objectives. This paper proposes novel self-learning approaches to tackle rule selection, rule sequence and subsequent rule combination for a specific problem with a certain scheduling objective. In our approaches, the algorithm itself will automatically select the suitable rule/rule sequence to synthesize an evolved order sequence into a high quality schedule. These approaches are able to solve large-size scheduling problems.
Subjects
Language English
Format Conference paper
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