Applied Sciences
| Open Access |
DOI: Optimizing Makespan and Minimizing Risk in Job Shop Scheduling: A Review
Paritosh Shinde , Sr. Production Planning Lead, Reckitt, USAAbstract
The job shop deals with customized product deliverables because of which the product mix is high, but the volume is low. For smooth functioning of the Job shop facility, it is important to plan for multiple factors. The review deals with the challenges that come with job shop scheduling (JSP) and provides predictive schedules to optimize some of the performance measures like makespan, makespan risk, stability risk and tardiness. Due to the high variety of products and unpredictable demand the scheduling complexity is substantial. This complexity further increases when we consider unpredictable machine breakdowns, increased setup times and absence of manpower. In order to come up with a robust and reliable scheduling plan the study will mainly focus on using buffered strategies (additional idle time), use of an Artificial Neural Network to correctly estimate the variable solutions for makespan and tardiness and using genetic algorithms to reduce and mitigate risk.
Keywords
Job Shop Scheduling (JSP), Dynamic Job Shop, Predictive Scheduling, Real-Time Rescheduling, Robust Scheduling, Buffered Scheduling, Artificial Neural Network (ANN), Genetic Algorithm (GA), Variable Neighborhood Search (VNS), Hybrid ANN-VNS.
References
Wu, Zigao, et al. “Optimizing Makespan and Stability Risks in Job Shop Scheduling.” Computers & Operations Research, vol. 122, 2020, p. 104963, https://doi.org/10.1016/j.cor.2020.104963.
Wu, Zigao, et al. “Risk Measure of Job Shop Scheduling with Random Machine Breakdowns.” Computers & Operations Research, vol. 99, 2018, pp. 1–12, https://doi.org/10.1016/j.cor.2018.05.022.
Yin, Yunqiang, et al. “Rescheduling on Identical Parallel Machines with Machine Disruptions to Minimize Total Completion Time.” European Journal of Operational Research, vol. 252, no. 3, 2016, pp. 737–49, https://doi.org/10.1016/j.ejor.2016.01.045.
Nouiri, Maroua, et al. “Two Stage Particle Swarm Optimization to Solve the Flexible Job Shop Predictive Scheduling Problem Considering Possible Machine Breakdowns.” Computers & Industrial Engineering, vol. 112, 2017, pp. 595–606, https://doi.org/10.1016/j.cie.2017.03.006.
Adibi, M. A., et al. “Multi-Objective Scheduling of Dynamic Job Shop Using Variable Neighborhood Search.” Expert Systems with Applications, vol. 37, no. 1, 2010, pp. 282–87, https://doi.org/10.1016/j.eswa.2009.05.001.
Gui, Lin, et al. “An Approximate Evaluation Method for Neighbourhood Solutions in Job Shop Scheduling Problem.” IET Collaborative Intelligent Manufacturing, vol. 4, no. 3, 2022, pp. 157–65, https://doi.org/10.1049/cim2.12049.
Al-Hinai, Nasr, and T. Y. ElMekkawy. “Robust and Stable Flexible Job Shop Scheduling with Random Machine Breakdowns Using a Hybrid Genetic Algorithm.” International Journal of Production Economics, vol. 132, no. 2, 2011, pp. 279–91, https://doi.org/10.1016/j.ijpe.2011.04.020.
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