Abstract
The primary objective of this research is to solve the job-shop scheduling problems (JSSPs), by minimizing the makespan, with and without process interruptions. In this paper, we first developed a genetic algorithm for solving JSSPs, and then improved the algorithm by integrating it with two simple priority rules and a hybrid rule. The performance of the developed algorithm was tested by solving 40 benchmark problems and comparing their results with that of a number of well-known algorithms. In addition, we have studied the job-shop scheduling under process interruptions such as machine unavailability and breakdown. For convenience of implementation, we have developed a decision support system (DSS). In the DSS, we built a graphical user interface for user friendly data inputs, model choices, and output generation. An overview of the DSS and an analysis of the experimental results are provided. The incorporation of priority rules, and a hybrid rule, not only improves the solutions but also reduces the overall computational time. The experimental results show that when the machine unavailability information is known in advance, the effect on the schedule is very little compared to the sudden machine breakdown scenario.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
