Investigation of Flexible Job Shop with Machine Availability Constraints Manuscript Received: 11 August 2023, Accepted: 27 September 2023, Published: 15 March 2024, ORCiD: 0000-0002-0168-0963, https://doi.org/10.33093/jetap.2024.6.1.8

Main Article Content

Nurul Izah Anuar
Muhammad Hafidz Fazli Md Fauadi
Adi Saptari

Abstract

In real-world scheduling applications, machines may be unavailable during certain time periods for deterministic and stochastic reasons. This situation does not pose any problems if the jobs always have more than one machine available for processing. However, it becomes an issue if the only available machine is the one which more than one job needs for processing. Thus, the investigation of limited machine availability, along with the practical requirement to handle this feature of scheduling problems, are of huge significance. This paper examined a flexible job shop environment of a manufacturing firm to optimize different performance criteria related to makespan, due dates, priorities and penalties by using a metaheuristic approach, taking into account the precedence constraints. The work investigated the case in which all machines are available for processing and another case in which some of the machines are known in advance to be unavailable. From the results, the best schedules are analysed and the perspectives of the findings to decision-makers are discussed with the purpose of achieving high machine utilization, cost reduction and customer satisfaction.

Article Details

Section
Articles

References

M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems, 5th ed. Springer, 2016.

C. Laroque, M. Leißau, P. Copado, C. Schumacher and J. Panadero, “A Biased-Randomized Discrete-Event Algorithm for the Hybrid Flow Shop Problem with Time Dependencies and Priority Constraints,” Algorithms, vol. 15, no. 2. pp. 1–14, 2022.

J. Rezaeian, S. Hosseini-Kia and I. Mahdavi, “The Preemptive Just-in-Time Scheduling Problem in a Flow Shop Scheduling System,” J. Optim. Ind. Eng., vol. 12, no. 2, pp. 79–92, 2019.

J. Shen, Y. Shi, J. Shi, Y. Dai and W. Li, “An Uncertain Permutation Flow Shop Predictive Scheduling Problem with Processing Interruption,” Phys. A Stat. Mech. Its Appl., vol. 611, pp. 1–15, 2023.

M. M. Wocker, F. F. Ostermeier, T. Wanninger, R. Zwinkau and J. Deuse, “Flexible Job Shop Scheduling with Preventive Maintenance Consideration,” J. Intell. Manuf., 2023.

D. Lei, “A Pareto Archive Particle Swarm Optimization for Multi-Objective Job Shop Scheduling,” Comput. Ind. Eng., vol. 54, no. 4, pp. 960–971, 2008.

M. Geurtsen, J. B. H. C. Didden, J. Adan, Z. Atan and I. Adan, “Production, Maintenance and Resource Scheduling: A Review,” Eur. J. Oper. Res., vol. 305, no. 2, pp. 501–529, 2023.

T. Xia, Y. Ding, Y. Dong, Z. Chen, M. Zheng, E. Pan and L. Xi, “Collaborative Production and Predictive Maintenance Scheduling for Flexible Flow Shop with Stochastic Interruptions and Monitoring Data,” J. Manuf. Syst., vol. 65, pp. 640–652, 2022.

R. Bürgy and K. Bülbül, “The Job Shop Scheduling Problem with Convex Costs,” Eur. J. Oper. Res., vol. 268, no. 1, pp. 82–100, 2018.

J. G. Kim, H. B. Jun, J. Y. Bang, J. H. Shin and S. H. Choi, “Minimizing Tardiness Penalty Costs in Job Shop Scheduling under Maximum Allowable Tardiness,” Processes, vol. 8, no. 11, pp. 1–15, 2020.

D. Guo, Z. Lyu, W. Wu, R. Y. Zhong, Y. Rong, G. Q. Huang, “Synchronization of Production and Delivery with Time Windows in Fixed-Position Assembly Islands under Graduation Intelligent Manufacturing System,” Robot. Comput. Integr. Manuf., vol. 73, pp. 1–14, 2022.

H. Güçdemir and H. Selim, “Dynamic Dispatching Priority Setting in Customer-Oriented Manufacturing Environments,” Int. J. Adv. Manuf. Technol., vol. 92, no. 5–8, pp. 1861–1874, 2017.

S. Shi, H. Xiong and G. Li, “A No-Tardiness Job Shop Scheduling Problem with Overtime Consideration and the Solution Approaches,” Comput. Ind. Eng., vol. 178, pp. 1–16, 2023.

F. M. Defersha, D. Obimuyiwa and A. D. Yimer, “Mathematical Model and Simulated Annealing Algorithm for Setup Operator Constrained Flexible Job Shop Scheduling Problem,” Comput. Ind. Eng., vol. 171, pp. 1–22, 2022.

M. Hajibabaei and J. Behnamian, “Flexible Job-Shop Scheduling Problem with Unrelated Parallel Machines and Resources-Dependent Processing Times: A Tabu Search Algorithm,” Int. J. Manag. Sci. Eng. Manag., vol. 16, no. 4, pp. 242–253, 2021.

Y. Li, C. Liao, L. Wang, Y. Xiao, Y. Cao and S. Guo, “A Reinforcement Learning-Artificial Bee Colony Algorithm for Flexible Job-Shop Scheduling Problem with Lot Streaming,” Appl. Soft Comput., vol. 146, pp. 1–13, 2023.

M. Huang, D. Guo and F. Guo, “An Improved Ant Colony Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problem,” in 2022 IEEE 10th Int. Conf. Comp. Sci. and Netw. Technol., pp. 1–3, 2022.

X. Luo, Q. Qian and Y. F. Fu, “Improved Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem,” Procedia Comput. Sci., vol. 166, pp. 480–485, 2020.

Y. J. Gao, Q. X. Shang, Y. Y. Yang, R. Hu and B. Qian, “Improved Particle Swarm Optimization Algorithm Combined with Reinforcement Learning for Solving Flexible Job Shop Scheduling Problem,” in Adv. Intell. Comput. Technol. and Appl., D. S. Huang, P. Premaratne, B. Jin, B. Qu, K. H. Jo and A. Hussain, Eds., Singapore: Springer, pp. 288–298, 2023.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in Proc. 1995 IEEE Int. Conf. Neur. Netw., pp. 1942–1948, 1995.

M. A. Zeidan, M. A. Al-Abaji and M. H. Ahmed, “Improved Particle Swarm Algorithm for Permutation Flow Shop Scheduling Problems,” Investig. Operacional, vol. 42, no. 2, pp. 165–203, 2021.

M. F. Tasgetiren, Y. C. Liang, M. Sevkli and G. Gencyilmaz, “A Particle Swarm Optimization Algorithm for Makespan and Total Flowtime Minimization in the Permutation Flowshop Sequencing Problem,” Eur. J. Oper. Res., vol. 177, no. 3, pp. 1930–1947, 2007.

H. Ohta and T. Nakatani, “A Heuristic Job-Shop Scheduling Algorithm to Minimize the Total Holding Cost of Completed and in-Process Products Subject to No Tardy Jobs,” Int. J. Prod. Econ., vol. 101, no. 1, pp. 19–29, 2006.