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    This study introduces a novel modified genetic algorithm (MGA) for the integrated process planning and scheduling (IPPS) problem. The MGA effectively solves complex manufacturing challenges, setting new performance records on benchmark problems.

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    Area of Science:

    • Manufacturing Systems Engineering
    • Operations Research
    • Artificial Intelligence in Manufacturing

    Background:

    • The integration of process planning and shop scheduling is crucial for intelligent manufacturing systems.
    • The integrated process planning and scheduling (IPPS) problem is a complex, NP-hard challenge in manufacturing.
    • Existing solutions for IPPS have limitations, leaving open problems.

    Purpose of the Study:

    • To develop novel integrated encoding and decoding methods for the IPPS problem.
    • To design a modified genetic algorithm (MGA) capable of simultaneously addressing process planning and scheduling.
    • To improve the efficiency and effectiveness of solving the IPPS problem.

    Main Methods:

    • Proposed novel integrated encoding and decoding methods considering OR-nodes in process network graphs.
    • Developed a modified genetic algorithm (MGA) where process planning and scheduling are represented in a single individual.
    • Designed specific operators to ensure the feasibility of operation sequences, addressing precedence constraints.

    Main Results:

    • The MGA achieved superior performance, setting nine new records on 37 benchmark IPPS problems.
    • Four of the newly established records reached the known lower bounds for these problems.
    • The MGA demonstrated effectiveness on a real-world case from a Chinese machine tool company, outperforming comparative algorithms.

    Conclusions:

    • The proposed MGA offers a powerful and effective approach to solving the complex IPPS problem.
    • The novel encoding/decoding methods and specialized operators contribute to the algorithm's success.
    • The MGA shows significant potential for practical application in intelligent manufacturing environments.