This study investigates the efficacy of a constraint programming (CP) model in optimizing flexible flow shop scheduling (FFSP) problems. Intending to minimize the makespan, it enhances the CP model with realistic features, such as job- and sequence-specific setup times, machine eligibility criteria, and precedence constraints. Initially, the thesis elaborates on the fundamental methodologies and outlines the proposed problem's characteristics. Subsequently, to enhance the interpretability of solutions, the study integrates additional, logically redundant constraints into the foundational CP model. Following this enhancement, the study presents the development of an accurate lower bound that bolsters confidence in the Model's solutions. The findings indicate that the incorporation of redundant constraints, along with the establishment of a robust lower bound, substantially improves the clarity and reliability of solutions across diverse FFSP scenarios. Furthermore, the method outlined offers flexibility and can be readily adapted to different scheduling challenges.
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This study investigates the efficacy of a constraint programming (CP) model in optimizing flexible flow shop scheduling (FFSP) problems. Intending to minimize the makespan, it enhances the CP model with realistic features, such as job- and sequence-specific setup times, machine eligibility criteria, and precedence constraints. Initially, the thesis elaborates on the fundamental methodologies and outlines the proposed problem's characteristics. Subsequently, to enhance the interpretability of sol...
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