Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

• Published in: IEEE access Vol. 8; pp. 86915 - 86922
• Main Authors: Gao, Kaizhou, Yang, Fajun, Li, Junqing, Sang, Hongyan, Luo, Jianping
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Dynamic scheduling; Electric breakdown; flexible job shop scheduling; Gauges; Genetic algorithms; Jaya algorithm; Job shop scheduling; Job shops; machine recovery; Pumps; Recovery; Remanufacturing; Rescheduling; Schedules; Scheduling; scheduling and rescheduling; Stability
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2992478

Machine recovery is met from time to time in real-life production. Rescheduling is often a necessary procedure to cope with it. Its instability gauges the number of changes to the existing scheduling solutions. It is a key criterion to measure a rescheduling solution's quality. This work aims at solving a flexible job shop problem with machine recovery, which arises from the scheduling and rescheduling of pump remanufacturing systems. In their scheduling phase, the objective is to minimize makespan. In their rescheduling phase, two objectives are to minimize both instability and makespan. By introducing two novel local search operators into the original Jaya algorithm, this work proposes an improved Jaya algorithm to solve it. It performs experiments on ten different-scale cases of real-life remanufacturing environment. The results show that the improved Jaya is effective and efficient for solving a flexible job shop scheduling and rescheduling problems. It can effectively balance instability and makespan in a rescheduling phase.