Flexible Job-Shop Rescheduling for New Job Insertion by Using Discrete Jaya Algorithm.

Area of Science:

• Operations Research
• Manufacturing Engineering
• Computer Science

Background:

• Flexible job-shop rescheduling (FJRP) is crucial for incorporating new priority jobs.
• Schedule instability is a key metric for evaluating rescheduling quality.
• Pump remanufacturing presents unique FJRP challenges for new job insertion.

Purpose of the Study:

• To formulate the FJRP for new job insertion in pump remanufacturing.
• To address bi-objective optimization minimizing instability and other metrics (makespan, flow time, workload).
• To develop and evaluate an effective metaheuristic for FJRP.

Main Methods:

• Formulation of the flexible job-shop rescheduling problem (FJRP) for new job insertion.
• Discretization and enhancement of the Jaya metaheuristic into DJaya.
• Integration of high-quality solution initialization heuristics.
• Development of objective-oriented local search operators and ensembles.

Main Results:

• DJaya demonstrates effectiveness and efficiency in solving FJRPs.
• The proposed method successfully handles bi-objective optimization for FJRP.
• Experiments on real-life pump remanufacturing cases validate DJaya's performance.
• DJaya outperforms state-of-the-art algorithms in the tested scenarios.

Conclusions:

• DJaya is a highly effective and efficient algorithm for flexible job-shop rescheduling.
• The approach provides a robust solution for new job insertion problems in manufacturing.
• The study contributes a novel metaheuristic optimization strategy for complex scheduling.