Finally, we show that ant colony optimization algorithm generates close optimal solutions and superior results than simulated annealing algorithm.
📌 3 Key Takeaways
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Objective & Hypothesis:This study focuses on identical parallel machine scheduling of jobs with deteriorating processing times and rate-modifying activities.
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Key Findings & Data:Rate modifying activities (RMAs) are also considered to recover the increase in processing times of jobs due to deterioration.
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Conclusion & Impact:Finally, we show that ant colony optimization algorithm generates close optimal solutions and superior results than simulated annealing algorithm.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
This study focuses on identical parallel machine scheduling of jobs with deteriorating processing times and rate-modifying activities. We consider nonlinearly increasing processing times of jobs based on their position assignment. Rate modifying activities (RMAs) are also considered to recover the increase in processing times of jobs due to deterioration. We also propose heuristics algorithms that rely on ant colony optimization and simulated annealing algorithms to solve the problem with multiple RMAs in a reasonable amount of time. Finally, we show that ant colony optimization algorithm generates close optimal solutions and superior results than simulated annealing algorithm.
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Cite This Article
OZTURKOGLU, O. (2022). Identical parallel machine scheduling with nonlinear deterioration and multiple rate modifying activities. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 7 - Sayı: 2, 167–176. https://ajindex.com/articles/440685
Mohring, R. and Rademacher, F., An Introduction to Stochastic Scheduling Problems. In: Neumann, K. and Pallaschke, D. (Eds.), Contributions to Operations Research, Springer, Berlin, (1985)
Righter, R., Stochastic Scheduling. In: Skaked, M. and Shanthikumar, G. (Eds.), Academic Press, San Diego, CA, (1994)
Pinedo, M., Scheduling, Theory, Algorithms and Systems. Prentice-Hall, Englewood Cliffs, NJ, (1995)
Boudreau, J., Hopp, W., McClain, J., and Thomas, L., On the Interface Between Operations and Human Resources Management. Manufacturing and Service Operations Management, 5(3), 179–202, (2003)
Gupta, J. and Gupta, S., Single Facility Scheduling with Nonlinear Processing Times. Computers and Industrial Engineering, 14, 387–393, (1988)
Gupta, S., Kunnathur, A., and Dandapani, K., Optimal Repayment Policies for Multiple Loans. OMEGA, 15(4), 323–330, (1987)
Browne, S. and Yechiali, U., Scheduling Deteriorating Jobs on a Single Processor. Operations Research, 38, 495–498, (1990)
Gawiejnowicz, S. and Pankowska, L., Scheduling Jobs with Varying Processing Times. Information Processing Letters, 54(3), 175–178, (1995)
Kunnathur, A. and Gupta, S., Minimizing the Makespan with Late Start Penalties Added to Processing Times in a Single Facility Scheduling Problem. European Journal of Operational Research, 47(1), 56– 64, (1990)
Mosheiov, G., Scheduling Jobs With StepDeterioration; Minimizing Makespan on a Single and Multi-Machine. Computers and Industrial Engineering, 28(4), 869–879, (1995
Ozturkoglu, Y. and Bulfin, R. L., A Unique Integer Mathematical Model for Scheduling Deteriorating Jobs with Rate-Modifying Activities on a Single Machine. The International Journal of Advanced Manufacturing Technology, 57(5-8), 753–762, (2011)
Alidaee, B. and Womer, N., Scheduling with Time Dependent Processing Times: Review and Extensions. Journal of the Operational Research Society, 50(7), 711–721, (1999)
Cheng, T., Ding, Q., and Lin, B., A Concise Survey of Scheduling with Time-Dependent Processing Times. European Journal of Operational Research, 152, 1–13, (2004)
Lodree, E., Geiger, C., and Jiang, X., Taxonomy for Integrating Scheduling Theory and Human Factors: Review and Research Opportunities. International Journal of Industrial Ergonomics, 39, 39–51, (2009)