Ranking of sequencing rules in a job shop scheduling problem with preference selection index approach
DOI:
https://doi.org/10.31181/jdaic10028042022bKeywords:
Preference selection index, Sequencing, Scheduling, Multi-criterion decision-makingAbstract
Scheduling different jobs in an appropriate sequence are very important in manufacturing industries due to the influence of conflicting criteria. It becomes difficult to sequence the jobs as the job number increases due to numerous computations involved. In this article, six jobs are considered to be treated on a machine one by one. Seven different priority sequencing rules provide seven different sequencing options for the jobs which are assessed using a set of nine criteria. Preference selection index (PSI) approach, a multi-criterion decision-making (MCDM) technique is proposed to rank them from best to worst. The PSI approach, unlike other MCDM methods, does not require to find the relative significance of the criteria, which reduces work of finding weights of criteria hence is a very easy and effective tool for decision making. A benchmark problem from the previous literature is considered and solved using the PSI approach and the obtained results are found to be correct.
Downloads
References
Ahsan, Z., & Dankowicz, H. (2019). Optimal scheduling and sequencing for large-scale seeding operations. Computers and Electronics in Agriculture, 163(January), 104728. https://doi.org/10.1016/j.compag.2019.01.052
Baker, K. R., & Trietsch, D. (2009). Principles of Sequencing and Scheduling. In Principles of Sequencing and Scheduling. https://doi.org/10.1002/9780470451793
Bari, P., & Karande, P. (2021). Application of PROMETHEE-GAIA method to priority sequencing rules in a dynamic job shop for single machine. Materials Today: Proceedings, xxxx. https://doi.org/10.1016/j.matpr.2020.12.854
Bodaghi, B., Palaneeswaran, E., Shahparvari, S., & Mohammadi, M. (2020). Probabilistic allocation and scheduling of multiple resources for emergency operations; a Victorian bushfire case study, Computers, Environment and Urban Systems, 81. https://www.sciencedirect.com/science/article/pii/S0198971518305118
Borujeni, M. P., & Gitinavard, H. (2017). Evaluating the sustainable mining contractor selection problems: An imprecise last aggregation preference selection index method. Journal of Sustainable Mining, 16(4), 207–218. https://doi.org/10.1016/j.jsm.2017.12.006
Cha, B. C., Moon, I. K., & Park, J. H. (2008). The joint replenishment and delivery scheduling of the one-warehouse, n-retailer system. Transportation Research Part E: Logistics and Transportation Review, 44(5), 720–730. https://doi.org/10.1016/j.tre.2007.05.010
Cheng, M.-Y., Chang, Y.-H., & Korir, D. (2019). Novel Approach to Estimating Schedule to Completion in Construction Projects Using Sequence and Nonsequence Learning. Journal of Construction Engineering and Management, 145(11), 04019072. https://doi.org/10.1061/(asce)co.1943-7862.0001697
Czerniachowska, K. (2019). Scheduling TV advertisements via genetic algorithm. European Journal of Industrial Engineering, 13(1), 81–116. https://doi.org/10.1504/EJIE.2019.097926
Gharbi, A., Labidi, M., & Haouari, M. (2015). An exact algorithm for the single machine problem with unavailability periods. European Journal of Industrial Engineering, 9(2), 244–260. https://doi.org/10.1504/EJIE.2015.068654
Gholami, O., & Sotskov, Y. N. (2014). Scheduling algorithm with controllable train speeds and departure times to decrease the total train tardiness. International Journal of Industrial Engineering Computations, 5(2), 281–294. https://doi.org/10.5267/j.ijiec.2013.11.002
Hafez, H.R., Ismail, E.A-R, & Saleh S.M.A-N, (2018). Dispatching rules for job shop problems, Global Journal of Engineering Science and Research Management, 5(9), 29-38.
Ji, M., Yao, D., Ge, J., & Cheng, T. C. E. (2015). Single-machine slack due-window assignment and scheduling with past-sequence-dependent delivery times and controllable job processing times. European Journal of Industrial Engineering, 9(6), 794–818. https://doi.org/10.1504/EJIE.2015.074380
Joseph, O. A., & Sridharan, R. (2011). Ranking of scheduling rule combinations in a flexible manufacturing system using preference selection index method. International Journal of Advanced Operations Management, 3(2), 201. https://doi.org/10.1504/ijaom.2011.042141
Keshavarz, T., Salmasi, N., & Varmazyar, M. (2019). Flowshop sequence-dependent group scheduling with minimisation of weighted earliness and tardiness. European Journal of Industrial Engineering, 13(1), 54–80. https://doi.org/10.1504/EJIE.2019.097920
Kumar, K. K., Nagaraju, D., Gayathri, S., & Narayanan, S. (2017). Evaluation and Selection of Best Priority Sequencing Rule in Job Shop Scheduling using Hybrid MCDM Technique. IOP Conference Series: Materials Science and Engineering, 197(1). https://doi.org/10.1088/1757-899X/197/1/012059
Kumar, M., & Kumar, A. (2019). Application of preference selection index method in performance based ranking of ceramic particulate (SiO2/SiC) reinforced AA2024 composite materials. Materials Today: Proceedings, 27(xxxx), 2667–2672. https://doi.org/10.1016/j.matpr.2019.11.244
Madić, M., Antucheviciene, J., Radovanović, M., & Petković, D. (2017). Determination of laser cutting process conditions using the preference selection index method. Optics and Laser Technology, 89(October 2016), 214–220. https://doi.org/10.1016/j.optlastec.2016.10.005
Maniya, K., & Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials and Design, 31(4), 1785–1789. https://doi.org/10.1016/j.matdes.2009.11.020
Maniya, K. D., & Bhatt, M. G. (2011). An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems. Computers and Industrial Engineering, 61(3), 542–549. https://doi.org/10.1016/j.cie.2011.04.009
Ojstersek, R., Brezocnik, M., & Buchmeister, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations, 11(3), 359–376. https://doi.org/10.5267/j.ijiec.2020.1.003
Pinedo, M. L. (2004). Planning and Scheduling in Manufacturing and Services. Springer Series in Operations Research and Financial Engineering.
Puspitasari, D., Wijaya, I. D., & Mentari, M. (2020). Decision support system for determining the activities of the study program using the Preference Selection Index. IOP Conference Series: Materials Science and Engineering, 732(1). https://doi.org/10.1088/1757-899X/732/1/012073
Sawant, V. B., Mohite, S. S., & Patil, R. (2011). A decision-making methodology for automated guided vehicle selection problem using a preference selection index method. Communications in Computer and Information Science, 145 CCIS, 176–181. https://doi.org/10.1007/978-3-642-20209-4_24
Siahaan, A. P. U., & Mesran, M. (2017). Determination of Education Scholarship Recipients Using Preference Selection Index. 3(6), 230–234. https://doi.org/10.31227/osf.io/hsfwr
T’Kindt, Vincent, Billaut, J.-C. (2005). Multicriteria Scheduling - Theory, Models and Algorithms. Springer-Verlag.
Tang, L., & Gong, H. (2009). The coordination of transportation and batching scheduling. Applied Mathematical Modelling, 33(10), 3854–3862. https://doi.org/10.1016/j.apm.2009.01.002
Thörnblad, K., Strömberg, A. B., Patriksson, M., & Almgren, T. (2015). Scheduling optimisation of a real flexible job shop including fixture availability and preventive maintenance. European Journal of Industrial Engineering, 9(1), 126–145. https://doi.org/10.1504/EJIE.2015.067451
Vahdani, B., Mousavi, S. M., & Ebrahimnejad, S. (2014). Soft computing-based preference selection index method for human resource management. Journal of Intelligent and Fuzzy Systems, 26(1), 393–403. https://doi.org/10.3233/IFS-120748
Yaghini, M., Alimohammadian, A., & Sharifi, S. (2012). A hybrid method to solve railroad passenger scheduling problem. Management Science Letters, 2(2), 543–548. https://doi.org/10.5267/j.msl.2011.12.012
Yang, T-H., Lin, I-C., Huang, C-F. (2020). A Decision Support System for Wafer Probe Card Production Scheduling, International Journal of Industrial Engineering: Theory, Applications and Practice, 27(1).
Zou, P., Rajora, M., & Liang, S. Y. (2021). Multimodal optimization of permutation flow‐shop scheduling problems using a clustering‐genetic‐algorithm‐based approach. Applied Sciences (Switzerland), 11(8). https://doi.org/10.3390/app11083388