Dynamic integrated process planning, scheduling and due date assignment (DIPPSDDA)

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The study offers several perspectives for future research and application. Firstly, it opens up possibilities for further exploring the integration of process planning, scheduling, and due date assignment in dynamic manufacturing environments. Researchers can delve into more advanced algorithms and optimization techniques to improve the performance of DIPPSDDA. Additionally, the use of hybrid algorithms, such as GA/SA and GA/TA, can be further investigated to find optimal solutions. Furthermore, the study highlights the importance of considering real-time job arrival and dynamic events in manufacturing systems. Future research can focus on developing models and algorithms that effectively handle unexpected changes and disruptions in the production process. This could involve incorporating machine learning or artificial intelligence techniques to enhance adaptability and decision-making in DIPPSDDA problems. The findings of this study can also be applied in practical manufacturing settings. Manufacturers can benefit from the insights provided to enhance their process planning, scheduling, and due date assignment strategies. Implementing the proposed metaheuristic algorithms and optimization techniques can lead to improved efficiency, reduced earliness and tardiness, and better utilization of resources. This can ultimately result in cost savings and enhanced customer satisfaction. Overall, the study sets a foundation for future research to advance the field of integrated process planning, scheduling, and due date assignment. It offers valuable perspectives for exploring new algorithms, addressing dynamic manufacturing challenges, and applying the findings to optimize real-world manufacturing systems.

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