What is it about?
This study focuses on the integration of process planning, scheduling, and due date assignment in manufacturing systems. It introduces the concept of Dynamic Integrated Process Planning, Scheduling, and Due Date Assignment (DIPPSDDA) to address the challenges of job arrival in real-time. The objective is to minimize earliness and tardiness while determining optimal due dates for each job. Different metaheuristic algorithms, including genetic algorithm, tabu algorithm, and simulated annealing, are developed and compared to optimize DIPPSDDA on various shop floors. The study provides insights into improving the global performance of manufacturing systems by considering the interrelated nature of these functions.
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Why is it important?
Process planning, scheduling, due date assignment manufacturing functions are integrated and solved simultaneously combined with dynamically changing job arrivals to make more realistic job shop scheduling model. Practically useful meta-heuristic algorithms are proposed for DIPPSDDA. Job assignments are controlled by applying discrete event simulation. Number of crossover and mutation points are not fixed. They are adjusted proportionally to chromosome length to get better algorithm performances.
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This page is a summary of: Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms, Mathematical Problems in Engineering, May 2019, Hindawi Publishing Corporation, DOI: 10.1155/2019/1572614.
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