A Novel Framework Based on Integration of Simulation Modelling and Mcdm Methods for Solving Fms Scheduling Problems

Shafi Ahmad, Zahid A. Khan, Mohammed Ali, Mohammad Asjad

Abstract


Scheduling in Flexible Manufacturing Systems (FMSs) is an important area of research as it significantly affects performance of the systems. In scheduling problems, determination of an appropriate order for jobs to be processed on a machine is a difficult task and to solve such problems, job priority rules (JPRs) are used. Several JPRs have been developed with an aim to obtain better performance, measured in terms of one or more scheduling performance measures (SPMs). However, selection of an appropriate rule is still an area of research as no single rule provides better results for all SPMs considered simultaneously. This work proposes a framework which is based on an integration of simulation and multi criteria decision making (MCDM) methods for the selection of an appropriate JPR yielding optimum results for multiple SPMs taken together. The proposed framework includes development of a simulation model to collect values of the SPMs corresponding to different JPRs. Further, five MCDM methods have been used to determine rank of the JPRs. Since different MCDM methods produce different ranking result therefore, the final rank of the JPRs has been determined by comparing the rank derived from these methods using membership degree method. To exemplify the probable application of the proposed framework, it has been implemented on a specific FMS taken from the literature in order to select the best JPR.

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References


R. El-Khalil and Z. Darwish, “Flexible manufacturing systems performance in U.S. automotive manufacturing plants: a case study,” Production Planning & Control, vol. 30, no. 1, pp. 48–59, Jan. 2019, doi: 10.1080/09537287.2018.1520318.

A. Yadav and S. C. Jayswal, “Modelling of flexible manufacturing system: a review,” International Journal of Production Research, vol. 56, no. 7, pp. 2464–2487, Apr. 2018, doi: 10.1080/00207543.2017.1387302.

D.-K. Lee, J.-H. Shin, and D.-H. Lee, “Operations scheduling for an advanced flexible manufacturing system with multi-fixturing pallets,” Computers & Industrial Engineering, vol. 144, p. 106496, Jun. 2020, doi: 10.1016/j.cie.2020.106496.

H. Tempelmeier and H. Kuhn, Flexible manufacturing systems: decision support for design and operation, vol. 12. John Wiley & Sons, 1993.

K. E. Stecke, “Design, planning, scheduling, and control problems of flexible manufacturing systems,” Ann Oper Res, vol. 3, no. 1, pp. 1–12, Jan. 1985, doi: 10.1007/BF02023765.

A. Prakash, F. T. S. Chan, and S. G. Deshmukh, “FMS scheduling with knowledge based genetic algorithm approach,” Expert Systems with Applications, vol. 38, no. 4, pp. 3161–3171, Apr. 2011, doi: 10.1016/j.eswa.2010.09.002.

C. Low, Y. Yip, and T.-H. Wu, “Modelling and heuristics of FMS scheduling with multiple objectives,” Computers & Operations Research, vol. 33, no. 3, pp. 674–694, Mar. 2006, doi: 10.1016/j.cor.2004.07.013.

P. Sharma and A. Jain, “Effect of routing flexibility and sequencing rules on performance of stochastic flexible job shop manufacturing system with setup times: Simulation approach,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 231, no. 2, pp. 329–345, Jan. 2017, doi: 10.1177/0954405415576060.

M. S. Jayamohan and C. Rajendran, “New dispatching rules for shop scheduling: A step forward,” International Journal of Production Research, vol. 38, no. 3, pp. 563–586, Feb. 2000, doi: 10.1080/002075400189301.

O. Holthaus and C. Rajendran, “New dispatching rules for scheduling in a job shop — An experimental study,” Int J Adv Manuf Technol, vol. 13, no. 2, pp. 148–153, Feb. 1997, doi: 10.1007/BF01225761.

C. Blum and M. Sampels, “An Ant Colony Optimization Algorithm for Shop Scheduling Problems,” Journal of Mathematical Modelling and Algorithms, vol. 3, no. 3, pp. 285–308, Sep. 2004, doi: 10.1023/B:JMMA.0000038614.39977.6f.

M. K. Marichelvam, M. Geetha, and Ö. Tosun, “An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors – A case study,” Computers & Operations Research, vol. 114, p. 104812, Feb. 2020, doi: 10.1016/j.cor.2019.104812.

F. T. S. Chan, H. K. Chan, H. C. W. Lau, and R. W. L. Ip, “Analysis of dynamic dispatching rules for a flexible manufacturing system,” Journal of Materials Processing Technology, vol. 138, no. 1, pp. 325–331, Jul. 2003, doi: 10.1016/S0924-0136(03)00093-1.

P. D. D. Dominic, S. Kaliyamoorthy, and M. S. Kumar, “Efficient dispatching rules for dynamic job shop scheduling,” Int J Adv Manuf Technol, vol. 24, no. 1, pp. 70–75, Jul. 2004, doi: 10.1007/s00170-002-1534-5.

M.Krishnan, T. R. Chinnusamy, and T. Karthikeyan, “Performance Study of Flexible Manufacturing System Scheduling Using Dispatching Rules in Dynamic Environment,” Procedia Engineering, vol. 38, pp. 2793–2798, Jan. 2012, doi: 10.1016/j.proeng.2012.06.327.

G.-H. Tzeng and J.-J. Huang, Multiple Attribute Decision Making: Methods and Applications. CRC Press, 2011.

M. Thürer, M. Stevenson, and T. Qu, “Job sequencing and selection within workload control order release: an assessment by simulation,” International Journal of Production Research, vol. 54, no. 4, pp. 1061–1075, Feb. 2016, doi: 10.1080/00207543.2015.1047978.

M. Z. Baharom, W. Nazdah, and W. Hussin, “Scheduling Analysis for Job Sequencing in Veneer Lamination Line,” Journal of Industrial and Intelligent Information, vol. 3, no. 3, 2015.

H.-H. Doh, J.-M. Yu, J.-S. Kim, D.-H. Lee, and S.-H. Nam, “A priority scheduling approach for flexible job shops with multiple process plans,” International Journal of Production Research, vol. 51, no. 12, pp. 3748–3764, Jun. 2013, doi: 10.1080/00207543.2013.765074.

M. Montazeri and L. N. Van Wassenhove, “Analysis of scheduling rules for an FMS,” The International Journal of Production Research, vol. 28, no. 4, pp. 785–802, 1990.

S. Wadhwa, Y. Ducq, M. Ali, and A. Prakash, “Performance analysis of a flexible manufacturing system,” Global Journal of Flexible Systems Management, vol. 10, no. 3, pp. 23–34, 2009.

V. Vinod and R. Sridharan, “Dynamic job-shop scheduling with sequence-dependent setup times: simulation modeling and analysis,” Int J Adv Manuf Technol, vol. 36, no. 3, pp. 355–372, Mar. 2008, doi: 10.1007/s00170-006-0836-4.

R. Haupt, “A survey of priority rule-based scheduling,” Operations-Research-Spektrum, vol. 11, no. 1, pp. 3–16, 1989.

S. S. Panwalkar and W. Iskander, “A Survey of Scheduling Rules,” Operations Research, vol. 25, no. 1, pp. 45–61, Feb. 1977, doi: 10.1287/opre.25.1.45.

M. Hamidi, “Two new sequencing rules for the non-preemptive single machine scheduling problem,” The Journal of Business Inquiry, vol. 15, no. 2, pp. 116–127, 2016.

V. K. Chawla, A. K. Chanda, S. Angra, and S. Rani, “Simultaneous Dispatching and Scheduling of Multi-Load AGVs in FMS-A Simulation Study,” Materials Today: Proceedings, vol. 5, no. 11, Part 3, pp. 25358–25367, Jan. 2018, doi: 10.1016/j.matpr.2018.10.339.

K. Amoako-Gyampah and J. R. Meredith, “A simulation study of FMS tool allocation procedures,” Journal of Manufacturing Systems, vol. 15, no. 6, pp. 419–431, Jan. 1996, doi: 10.1016/S0278-6125(97)83055-5.

K. Mahmood, T. Karaulova, T. Otto, and E. Shevtshenko, “Performance Analysis of a Flexible Manufacturing System (FMS),” Procedia CIRP, vol. 63, pp. 424–429, Jan. 2017, doi: 10.1016/j.procir.2017.03.123.

Mohd. S. Hussain and M. Ali, “A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems,” Glob J Flex Syst Manag, vol. 20, no. 3, pp. 267–290, Sep. 2019, doi: 10.1007/s40171-019-00214-9.

S. K. Yadav, D. Joseph, and N. Jigeesh, “A review on industrial applications of TOPSIS approach,” International Journal of Services and Operations Management, vol. 30, no. 1, pp. 23–28, Jan. 2018, doi: 10.1504/IJSOM.2018.091438.

Ž. Stević, D. Pamučar, A. Puška, and P. Chatterjee, “Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS),” Computers & Industrial Engineering, vol. 140, p. 106231, Feb. 2020, doi: 10.1016/j.cie.2019.106231.

S. Mufazzal and S. M. Muzakkir, “A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals,” Computers & Industrial Engineering, vol. 119, pp. 427–438, May 2018, doi: 10.1016/j.cie.2018.03.045.

D. Pamučar and G. Ćirović, “The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC),” Expert Systems with Applications, vol. 42, no. 6, pp. 3016–3028, Apr. 2015, doi: 10.1016/j.eswa.2014.11.057.

M. Keshavarz Ghorabaee, E. K. Zavadskas, L. Olfat, and Z. Turskis, “Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS),” Informatica, vol. 26, no. 3, pp. 435–451, 2015.

Ž. Stević and N. Brković, “A Novel Integrated FUCOM-MARCOS Model for Evaluation of Human Resources in a Transport Company,” Logistics, vol. 4, no. 1, Art. no. 1, Mar. 2020, doi: 10.3390/logistics4010004.

A. Ulutaş, D. Karabasevic, G. Popovic, D. Stanujkic, P. T. Nguyen, and Ç. Karaköy, “Development of a Novel Integrated CCSD-ITARA-MARCOS Decision-Making Approach for Stackers Selection in a Logistics System,” Mathematics, vol. 8, no. 10, Art. no. 10, Oct. 2020, doi: 10.3390/math8101672.

N. Z. Khan, T. S. A. Ansari, A. N. Siddiquee, and Z. A. Khan, “Selection of E-learning websites using a novel Proximity Indexed Value (PIV) MCDM method,” J. Comput. Educ., vol. 6, no. 2, pp. 241–256, Jun. 2019, doi: 10.1007/s40692-019-00135-7.

S. Wakeel, S. Bingol, M. N. Bashir, and S. Ahmad, “Selection of sustainable material for the manufacturing of complex automotive products using a new hybrid Goal Programming Model for Best Worst Method–Proximity Indexed Value method,” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, p. 1464420720966347, Oct. 2020, doi: 10.1177/1464420720966347.

D. Pamučar, Ž. Stević, and E. K. Zavadskas, “Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages,” Applied Soft Computing, vol. 67, pp. 141–163, 2018.

D. I. Božanić, D. S. Pamučar, and S. M. Karović, “Application the MABAC method in support of decision-making on the use of force in a defensive operation,” Tehnika, vol. 71, no. 1, pp. 129–136, 2016.

D. Bozanic, D. Tešić, and J. Kočić, “Multi-criteria FUCOM – Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge,” Decision Making: Applications in Management and Engineering, vol. 2, no. 1, Art. no. 1, Mar. 2019.

M. Keshavarz Ghorabaee, M. Amiri, E. K. Zavadskas, Z. Turskis, and J. Antucheviciene, “Stochastic EDAS method for multi-criteria decision-making with normally distributed data,” Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1627–1638, Jan. 2017, doi: 10.3233/JIFS-17184.

M. K. Kikomba, R. M. Mabela, and D. I. Ntantu, “Applying EDAS method to solve air traffic problems,” International Journal of Scientific and Innovative Mathematical Research (IJSIMR), vol. 4, no. 8, pp. 15–23, 2016.

D. Stanujkic, G. Popovic, and M. Brzakovic, “An approach to personnel selection in the IT industry based on the EDAS method,” Transformations in Business & Economics, vol. 17, no. 2, p. 44, 2018.

M. Behzadian, S. Khanmohammadi Otaghsara, M. Yazdani, and J. Ignatius, “A state-of the-art survey of TOPSIS applications,” Expert Systems with Applications, vol. 39, no. 17, pp. 13051–13069, Dec. 2012, doi: 10.1016/j.eswa.2012.05.056.

C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey. Berlin Heidelberg: Springer-Verlag, 1981.

Y.-J. Lai, T.-Y. Liu, and C.-L. Hwang, “TOPSIS for MODM,” European Journal of Operational Research, vol. 76, no. 3, pp. 486–500, Aug. 1994, doi: 10.1016/0377-2217(94)90282-8.

W.-C. Yang, S.-H. Chon, C.-M. Choe, and K. Un-Ha, “Materials Selection Method Combined with Different MADM Methods,” Journal of Artificial Intelligence, vol. 1, no. 2, p. 89, 2019.

G. A. Chang and W. R. Peterson, “Modeling and analysis of flexible manufacturing systems: a simulation study,” in Proceedings of the 2015 ASEE Annual Conference & Exposition, Seattle, WA, USA, 2015, pp. 14–17.

R. Rangsaritratsamee, W. G. Ferrell Jr, and M. B. Kurz, “Dynamic rescheduling that simultaneously considers efficiency and stability,” Computers & Industrial Engineering, vol. 46, no. 1, pp. 1–15, 2004.




DOI: https://doi.org/10.31449/inf.v47i4.3480

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