Abstract
Due to the highly focused business strategies, it has become necessary for a manufacturing industry to have quick reaction times and high flexibility. In this manner, the presentation of flexible manufacturing systems (FMSs) has had in number positive effects on the manufacturing technology. FMS scheduling is the most influenced range by the evaluation factors; hence, choosing proper scheduling types or dispatching rules, concerning manufacturing system criteria, is a significant decision note for managers and professional experts. This paper presents a new multi-criteria decision-making approach with similarity to ideal solutions and interval-valued fuzzy sets based on possibilistic-statistical concepts to perfect arrangement group decision-making process. This model introduces new definitions for obtaining ideal solutions with values of possibilistic-interval mean and possibilistic-interval standard deviation, and novel separation measures along with a new interval-valued fuzzy ranking index of integrated relative-closeness coefficients to provide the final preference order of dispatching rule candidates under uncertain conditions. Furthermore, the steps of the proposed interval-valued fuzzy group decision model are implemented to an application case from the recent literature of the FMS scheduling. Finally, discussion of computational results for dispatching rules and comparative analysis are reported.
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Chan, F.T.S.; Chan, H.K.; Kazeroon, A.: A fuzzy multi-criteria decision-making technique for evaluation of scheduling rules. Int. J. Adv. Manuf. Technol. 20(2), 103–113 (2002)
Tavakkoli-Moghaddam, R.; Heydar, M.; Mousavi, S.M.: A hybrid genetic algorithm for a bi-objective scheduling problem in a flexible manufacturing cell. Int. J. Eng. Trans. A Basics 23(3&4), 235–252 (2010)
Jahromi, M.H.; Tavakkoli-Moghaddam, R.; Makui, A.; Saghaee, A.: A modified genetic algorithm for solving machine-tool selection and operation allocation problem in an FMS. J. UMP Soc. Sci. Technol. Manag. 3(1), 491–499 (2015)
Groover, M.P.: Automation: Production Systems and Computer-Integrated Manufacturing. Prentice Hall, US (2001). ISBN: 978-0132393218
El-Bouri, A.; Shah, P.: A neural network for dispatching rule selection in a job shop. Int. J. Adv. Manuf. Technol. 31(3–4), 342–349 (2006)
Gupta, Y.P.; Gupta, M.C.; Bector, C.R.: A review of scheduling rules in flexible manufacturing systems. Int. J. Comput. Integr. Manuf. 2(6), 356–377 (1989)
Smith, M.L.; Ramesh, R.; Dudeck, R.; Blair, E.: Characteristic of US flexible manufacturing systems. Comput. Ind. Eng. 7(3), 199–207 (1986)
Lo, J.J.; Lin, L.: An object-oriented FMS real-time and feedback control model. Int. J. Comput. Integr. Manuf. 12(6), 483–502 (1999)
Ishii, N.; Talavage, J.J.: A mixed dispatching rule approach in FMS scheduling. Int. J. Flex. Manuf. Syst. 6(1), 69–87 (1994)
Frazier, G.V.: An evaluation of group scheduling heuristics in a flow-line manufacturing cell. Int. J. Prod. Res. 34(4), 959–976 (1996)
Tung, L.F.; Lin, L.; Nagi, R.: Multiple-objective scheduling for the hierarchical control of flexible manufacturing systems. Int. J. Flex. Manuf. Syst. 11(4), 379–409 (1999)
Shih, H.M.; Sekiguchi, T.: Fuzzy inference-based multiple criteria FMS scheduling. Int. J. Prod. Res. 37(10), 2315–2333 (1999)
Lun, M.; Chen, F.F.: Holonic concept based methodology for part routeing on flexible manufacturing systems. Int. J. Adv. Manuf. Technol. 16(7), 483–490 (2000)
Chan, F.T.S.; Chung, S.H.; Chan, L.Y.: An introduction of dominant genes in genetic algorithm for FMS. Int. J. Prod. Res. 46(16), 4369–4389 (2008)
Tay, J.C.; Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput. Ind. Eng. 54(3), 453–473 (2008)
Lee, K.K.: Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment. Appl. Soft Comput. 8(4), 1295–1304 (2008)
Zhang, G.; Shao, X.; Li, P.; Gao, L.: An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput. Ind. Eng. 56(4), 1309–1318 (2009)
Chan, F.T.; Swarnkar, R.: Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robot. Comput. Integr. Manuf. 22(4), 353–362 (2006)
Subramaniam, V.; Ramesh, T.; Lee, G.K.; Wong, Y.S.; Hong, G.S.: Job shop scheduling with dynamic fuzzy selection of dispatching rules. Int. J. Adv. Manuf. Technol. 16(10), 759–764 (2000)
Yazgan, H.R.: Selection of dispatching rules with fuzzy ANP approach. Int. J. Adv. Manuf. Technol. 52(5–8), 651–667 (2011)
Kashfi, M.A.; Javadi, M.: A model for selecting suitable dispatching rule in FMS based on fuzzy multi attribute group decision making. Prod. Eng. Res. Dev. 9(2), 237–246 (2015)
Tavakkoli-Moghaddam, R.; Mousavi, S.M.; Heydar, M.: An integrated AHP-VIKOR methodology for plant location selection. Int. J. Eng. Trans. B Appl. 24(2), 127–137 (2011)
Vahdani, B.; Mousavi, S.M.; Tavakkoli-Moghaddam, R.: Group decision making based on novel fuzzy modified TOPSIS method. Appl. Math. Model. 35(9), 4257–4269 (2011)
Vahdani, B.; Mousavi, S.M.; Ebrahimnejad, S.: Soft computing-based preference selection index method for human resource management. J. Intell. Fuzzy Syst. 26(1), 393–403 (2014)
Mousavi, S.M.; Tavakkoli-Moghaddam, R.; Heydar, M.; Ebrahimnejad, S.: Multi-criteria decision making for plant location selection: an integrated Delphi-AHP-PROMETHEE methodology. Arab. J. Sci. Eng. 38(5), 1255–1268 (2013)
Mousavi, S.M.; Vahdani, B.; Tavakkoli-Moghaddam, R.; Ebrahimnejad, S.; Amiri, M.: A multi-stage decision-making process for multiple attributes analysis under an interval-valued fuzzy environment. Int. J. Adv. Manuf. Technol. 64(9–12), 1263–1273 (2013)
Mousavi, S.M.; Vahdani, B.; Sadigh Behzadi, S.: Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems. Iran. J. Fuzzy Syst. 13(1), 45–65 (2016)
Hashemi, H.; Bazargan, J.; Mousavi, S.M.; Vahdani, B.: An extended compromise ratio model with an application to reservoir flood control operation under an interval-valued intuitionistic fuzzy environment. Appl. Math. Model. 38(14), 3495–3511 (2014)
Zhou, B.; Pei, Z.; Ma, X.: An improvement method for selecting the best alternative in decision making. Int. J. Comput. Intell. Syst. 7(5), 882–895 (2014)
Gitinavard, H.; Mousavi, S.M.; Vahdani, B.; Siadat, A.: A distance-based decision model in interval-valued hesitant fuzzy setting for industrial selection problems. Sci. Iran. E 23(4), 1928–1940 (2016)
Gitinavard, H.; Mousavi, S.M.; Vahdani, B.: A new multi-criteria weighting and ranking model for group decision-making analysis based on interval-valued hesitant fuzzy sets to selection problems. Neural Comput. Appl. 27, 1593–1605 (2016)
Kannan, D.; Khodaverdi, R.; Olfat, L.; Jafarian, A.; Diabat, A.: Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. J. Clean. Prod. 47, 355–367 (2013)
Shen, L.; Olfat, L.; Govindan, K.; Khodaverdi, R.; Diabat, A.: A fuzzy multi criteria approach for evaluating green supplier’s performance in green supply chain with linguistic preferences. Resour. Conserv. Recycl. 74, 170–179 (2013)
Al-Refaie, A.; Diabat, A.: Optimizing convexity defect in a tile industry using fuzzy goal programming. Measurement 46(8), 2807–2815 (2013)
Al-Refaie, A.; Diabat, A.; Li, M.H.: Optimizing tablets’ quality with multiple responses using fuzzy goal programming. Proc. Inst. Mech. Eng. E J. Process Mech. Eng. 228(2), 115–126 (2014)
Govindan, K.; Diabat, A.; Shankar, K.M.: Analyzing the drivers of green manufacturing with fuzzy approach. J. Clean. Prod. 96, 182–193 (2015)
Mokhtarian, M.N.; Sadi-nezhad, S.; Makui, A.: A new flexible and reliable interval valued fuzzy VIKOR method based on uncertainty risk reduction in decision making process: An application for determining a suitable location for digging some pits for municipal wet waste landfill. Comput. Ind. Eng. 78, 213–233 (2014)
Ashtiani, B.; Haghighirad, F.; Makui, A.; Ali Montazer, G.: Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets. Appl. Soft Comput. 9(2), 457–461 (2009)
Zhang, W.G.; Wang, Y.L.; Chen, Z.P.; Nie, Z.K.: Possibilistic mean–variance models and efficient frontiers for portfolio selection problem. Inf. Sci. 177(13), 2787–2801 (2007)
Ye, F., Lin, Q.: Partner selection in a virtual enterprise: a group multiattribute decision model with weighted possibilistic mean values. Math. Probl. Eng. 2013 1–14 (2013). https://www.hindawi.com/journals/mpe/2013/519629
Deng, X.; Li, R.: Gradually tolerant constraint method for fuzzy portfolio based on possibility theory. Inf. Sci. 259, 16–24 (2014)
Chen, T.Y.: Comparative analysis of SAW and TOPSIS based on interval-valued fuzzy sets: Discussions on score functions and weight constraints. Expert Syst. Appl. 39(2), 1848–1861 (2012)
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Foroozesh, N., Tavakkoli-Moghaddam, R., Mousavi, S.M. et al. Dispatching Rule Evaluation in Flexible Manufacturing Systems by a New Fuzzy Decision Model with Possibilistic-Statistical Uncertainties. Arab J Sci Eng 42, 2947–2960 (2017). https://doi.org/10.1007/s13369-017-2448-8
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DOI: https://doi.org/10.1007/s13369-017-2448-8