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Group decision-making and grey programming approaches to optimal product mix in manufacturing supply chains

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Abstract

Group decision-making is a significant task that involves trade-offs, risks and the interplay of various factors. Optimization models developed from group decision-making are often built on uncertainties and negotiated parameters and may not yield factual solutions. Manufacturing decision-making environment is subject to several uncertainties, and grey theory is an effective tool to improve the exactitude of decision-making in such situations. An application of grey programming in manufacturing decision-making environment has been implemented in this research. Grey programming models can potentially avoid the loss of data while formulating optimization models from group decisions. A grey programming model for optimizing profits has been constructed and solved for an indeterminate product mix problem of a case electronics manufacturing industry. For the case, the optimization models were constructed and solved for the ideal model, critical model and the most typical model. And the results favor the decision on the introduction of a new product variant in the smartphone segment. In the most favorable and unfavorable conditions, it is seen that the introduction of new variant in the smartphone section is meritorious when compared to the tablet or laptop segments. The solution to the case endorses flexibility of grey programming in uncertain decision-making environments. As the decisions from the group can be signified in intervals using grey numbers, managers are recommended to comprise the benefits of grey programming models into their optimization problems for increasing flexibility and reliability of solutions.

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References

  1. Pettigrew AM (2014) The politics of organizational decision-making. Routledge, Abingdon

    Book  Google Scholar 

  2. Hwang CL, Yoon K (2012) Multiple attribute decision making: methods and applications a state-of-the-art survey, vol 186. Springer Science & Business Media, Berlin

    MATH  Google Scholar 

  3. Ross DF (2013) Competing through supply chain management: creating market-winning strategies through supply chain partnerships. Springer Science & Business Media, Berlin

    Google Scholar 

  4. Blanco-Mesa F, Gil-Lafuente AM, Merigó JM (2017) New aggregation operators for decision-making under uncertainty: an applications in selection of entrepreneurial opportunities. Technol Econ Dev Econ. https://doi.org/10.3846/20294913.2016.1212744

    Article  Google Scholar 

  5. Cunha A, Morais D (2017) Problem structuring methods in group decision making: a comparative study of their application. Oper Res Int J. https://doi.org/10.1007/s12351-017-0310-0

    Article  Google Scholar 

  6. Yang W, Chen Z, Zhang F (2015) New group decision making method in intuitionistic fuzzy setting based on TOPSIS. Technol Econ Dev Econ. https://doi.org/10.3846/20294913.2015.1072754

    Article  Google Scholar 

  7. Hwang CL, Lin MJ (2012) Group decision making under multiple criteria: methods and applications, vol 281. Springer Science & Business Media, Berlin

    Google Scholar 

  8. Rajesh R (2017) Pseudo resilient supply chains: concept, traits, and practices. J Risk Res. https://doi.org/10.1080/13669877.2017.1304977

    Article  Google Scholar 

  9. Rajesh R (2018) On sustainability, resilience, and the sustainable–resilient supply networks. Sustain Product Consum 15:74–88

    Article  Google Scholar 

  10. Rajesh R (2017) Technological capabilities and supply chain resilience of firms: a relational analysis using total interpretive structural modeling (TISM). Technol Forecast Soc Chang 118:161–169

    Article  Google Scholar 

  11. Rajesh R (2018) Measuring the barriers to resilience in manufacturing supply chains using Grey clustering and VIKOR approaches. Measurement 126:259–273

    Article  Google Scholar 

  12. Xu Z, Wang H (2016) Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul Comput 1(1):21–35

    Article  Google Scholar 

  13. Joshi D, Kumar S (2016) Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making. Eur J Oper Res 248(1):183–191

    Article  MathSciNet  MATH  Google Scholar 

  14. Peng JJ, Wang JQ, Wang J, Zhang HY, Chen XH (2016) Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. Int J Syst Sci 47(10):2342–2358

    Article  MATH  Google Scholar 

  15. Biswas P, Pramanik S, Giri BC (2016) TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Comput Appl 27(3):727–737

    Article  Google Scholar 

  16. Aguarón J, Escobar MT, Moreno-Jiménez JM (2016) The precise consistency consensus matrix in a local AHP-group decision making context. Ann Oper Res 245(1–2):245–259

    Article  MathSciNet  MATH  Google Scholar 

  17. Wu J, Xiong R, Chiclana F (2016) Uninorm trust propagation and aggregation methods for group decision making in social network with four tuple information. Knowl Based Syst 96:29–39

    Article  Google Scholar 

  18. Liao H, Xu Z, Zeng XJ, Xu DL (2016) An enhanced consensus reaching process in group decision making with intuitionistic fuzzy preference relations. Inf Sci 329:274–286

    Article  Google Scholar 

  19. Xu Y, Chen L, Rodríguez RM, Herrera F, Wang H (2016) Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making. Knowl Based Syst 99:71–78

    Article  Google Scholar 

  20. Chai J, Liu JN, Ngai EW (2013) Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Syst Appl 40(10):3872–3885

    Article  Google Scholar 

  21. Govindan K, Palaniappan M, Zhu Q, Kannan D (2012) Analysis of third party reverse logistics provider using interpretive structural modeling. Int J Prod Econ 140(1):204–211

    Article  Google Scholar 

  22. Jindal A, Sangwan KS (2013) An integrated fuzzy multi-criteria evaluation of sustainable reverse logistics network models. In: 2013 IEEE international conference on fuzzy systems (FUZZ). IEEE, pp 1–7

  23. Kilger C, Reuter B, Stadtler H (2015) Collaborative planning (Chap. 14). In: Stadtler H, Kilger C, Meyr H (eds) Supply chain management and advanced planning. Springer Texts in Business and Economics. Springer, Berlin, pp 257–277

    Chapter  Google Scholar 

  24. Muralidharan C, Anantharaman N, Deshmukh SG (2002) A multi-criteria group decision making model for supplier rating. J Supply Chain Manag 38(4):22

    Article  Google Scholar 

  25. Salas-Molina F, Rodríguez-Aguilar JA, Pla-Santamaria D (2017) Characterizing compromise solutions for investors with uncertain risk preferences. Oper Res. https://doi.org/10.1007/s12351-017-0309-6

    Article  Google Scholar 

  26. Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36(8):11363–11368

    Article  Google Scholar 

  27. Kar AK (2015) A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. J Comput Sci 6:23–33

    Article  Google Scholar 

  28. Khatwani G, Singh SP, Trivedi A, Chauhan A (2015) Fuzzy-TISM: a fuzzy extension of TISM for group decision making. Glob J Flex Syst Manag 16(1):97–112

    Article  Google Scholar 

  29. Hernández JE, Mula J, Poler R, Lyons AC (2014) Collaborative planning in multi-tier supply chains supported by a negotiation-based mechanism and multi-agent system. Group Decis Negot 23(2):235–269

    Article  Google Scholar 

  30. Sivaprakasam R, Selladurai V, Sasikumar P (2015) Implementation of interpretive structural modelling methodology as a strategic decision making tool in a green supply chain context. Ann Oper Res 233(1):423–448

    Article  MATH  Google Scholar 

  31. Zhang X, Xu Z (2015) Soft computing based on maximizing consensus and fuzzy TOPSIS approach to interval-valued intuitionistic fuzzy group decision making. Appl Soft Comput 26:42–56

    Article  Google Scholar 

  32. Van den Honert RC (2001) Decisional power in group decision making: a note on the allocation of group members’ weights in the multiplicative AHP and SMART. Group Decis Negot 10(3):275–286

    Article  Google Scholar 

  33. Escobar MT, Moreno-Jiménez JM (2007) Aggregation of individual preference structures in AHP-group decision making. Group Decis Negot 16(4):287–301

    Article  Google Scholar 

  34. Arias-Nicolás JP, Pérez CJ, Martín J (2008) A logistic regression-based pairwise comparison method to aggregate preferences. Group Decis Negot 17(3):237–247

    Article  Google Scholar 

  35. Huang YS, Liao JT, Lin ZL (2009) A study on aggregation of group decisions. Syst Res Behav Sci 26(4):445–454

    Article  Google Scholar 

  36. Xu Z, Yager RR (2010) Power-geometric operators and their use in group decision making. IEEE Trans Fuzzy Syst 18(1):94–105

    Article  Google Scholar 

  37. Pedrycz W, Song M (2011) Analytic hierarchy process (AHP) in group decision making and its optimization with an allocation of information granularity. IEEE Trans Fuzzy Syst 19(3):527–539

    Article  Google Scholar 

  38. Scala NM, Rajgopal J, Vargas LG, Needy KL (2015) Group decision making with dispersion in the analytic hierarchy process. Group Decis Negot 25(2):355–372

    Article  Google Scholar 

  39. Deng JL (1982) Control problems of grey systems. Syst Control Lett 1(5):288–294

    Article  MathSciNet  MATH  Google Scholar 

  40. Deng JL (1989) Introduction to grey system theory. J Grey Syst 1(1):1–24

    MathSciNet  MATH  Google Scholar 

  41. Chang CJ, Yu L, Jin P (2016) A mega-trend-diffusion grey forecasting model for short-term manufacturing demand. J Oper Res Soc 67(12):1439–1445

    Article  Google Scholar 

  42. Hsin PH, Chen CI (2017) Application of trembling-hand perfect equilibrium to Nash nonlinear Grey Bernoulli model: an example of BRIC’s GDP forecasting. Neural Comput Appl 28(1):269–274

    Article  Google Scholar 

  43. Liu P, Chu Y, Li Y (2015) The multi-attribute group decision-making method based on the interval grey uncertain linguistic generalized hybrid averaging operator. Neural Comput Appl 26(6):1395–1405

    Article  Google Scholar 

  44. Liu S, Lin JYF (2010) Grey systems: theory and applications. Springer, Berlin

    Book  MATH  Google Scholar 

  45. Nguyen NT, Tran TT (2017) Optimizing mathematical parameters of Grey system theory: an empirical forecasting case of Vietnamese tourism. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3058-9

    Article  Google Scholar 

  46. Rajesh R (2016) Forecasting supply chain resilience performance using grey prediction. Electron Commer Res Appl 20:42–58

    Article  Google Scholar 

  47. Wu LF, Liu SF, Cui W, Liu DL, Yao TX (2014) Non-homogenous discrete grey model with fractional-order accumulation. Neural Comput Appl 25(5):1215–1221

    Article  Google Scholar 

  48. Lijuan WANG (2009) Grey-game in the perishable agricultural products supply chain under competition. J Wuhan Univ (Nat Sci Ed) 2:4

    MathSciNet  Google Scholar 

  49. Wang SJ, Wang WL, Huang CT, Chen SC (2011) Improving inventory effectiveness in RFID-enabled global supply chain with Grey forecasting model. J Strateg Inf Syst 20(3):307–322

    Article  Google Scholar 

  50. Hajiagha SHR, Zavadskas EK, Hashemi SS (2013) Application of stepwise data envelopment analysis and grey incidence analysis to evaluate the effectiveness of export promotion programs. J Bus Econ Manag 14(3):638–650

    Article  Google Scholar 

  51. Tseng ML, Chiu AS (2013) Evaluating firm’s green supply chain management in linguistic preferences. J Clean Prod 40:22–31

    Article  Google Scholar 

  52. Rajesh R, Ravi V (2017) Analyzing drivers of risks in electronic supply chains: a grey–DEMATEL approach. Int J Adv Manuf Technol 92(1–4):1127–1145

    Article  Google Scholar 

  53. Sanayei A, Mousavi SF, Yazdankhah A (2010) Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Syst Appl 37(1):24–30

    Article  Google Scholar 

  54. You XY, You JX, Liu HC, Zhen L (2015) Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information. Expert Syst Appl 42(4):1906–1916

    Article  Google Scholar 

  55. Rief D, Van Dinther C (2010) Negotiation for cooperation in logistics networks: an experimental study. Group Decis Negot 19(3):211–226

    Article  Google Scholar 

  56. Hernández JE, Poler R, Mula J, Lario FC (2011) The reverse logistic process of an automobile supply chain network supported by a collaborative decision-making model. Group Decis Negot 20(1):79–114

    Article  Google Scholar 

  57. Wong CW, Lai KH, Cheng TCE, Lun YV (2015) The role of IT-enabled collaborative decision making in inter-organizational information integration to improve customer service performance. Int J Prod Econ 159:56–65

    Article  Google Scholar 

  58. Xia M, Chen J (2015) Multi-criteria group decision making based on bilateral agreements. Eur J Oper Res 240(3):756–764

    Article  MathSciNet  MATH  Google Scholar 

  59. Wan SP, Wang F, Dong JY (2016) A novel group decision making method with intuitionistic fuzzy preference relations for RFID technology selection. Appl Soft Comput 38:405–422

    Article  Google Scholar 

  60. Tian ZP, Wang J, Wang JQ, Zhang HY (2017) Simplified neutrosophic linguistic multi-criteria group decision-making approach to green product development. Group Decis Negot 26(3):597–627

    Article  Google Scholar 

  61. del Moral MJ, Chiclana F, Tapia JM, Herrera-Viedma E (2018) A comparative study on consensus measures in group decision making. Int J Intell Syst. https://doi.org/10.1002/int.21954

    Article  Google Scholar 

  62. Banaeian N, Mobli H, Fahimnia B, Nielsen IE, Omid M (2018) Green supplier selection using fuzzy group decision making methods: a case study from the agri-food industry. Comput Oper Res 89:337–347

    Article  MathSciNet  MATH  Google Scholar 

  63. Xu Z, Cai X (2015) Group decision making with incomplete interval-valued intuitionistic preference relations. Group Decis Negot 24(2):193–215

    Article  Google Scholar 

  64. Qin J, Liu X, Pedrycz W (2017) An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. Eur J Oper Res 258(2):626–638

    Article  MathSciNet  MATH  Google Scholar 

  65. Wu J, Chiclana F, Fujita H, Herrera-Viedma E (2017) A visual interaction consensus model for social network group decision making with trust propagation. Knowl Based Syst 122:39–50

    Article  Google Scholar 

  66. Capuano N, Chiclana F, Fujita H, Herrera-Viedma E, Loia V (2018) Fuzzy group decision making with incomplete information guided by social influence. IEEE Trans Fuzzy Syst 26(3):1704–1718

    Article  Google Scholar 

  67. Changbo Z (2009) The study of improved Grey–Markov model and its application in independent demand forecasts in supply chain. J Intell 7:44

    Google Scholar 

  68. Zhang C, Yu ZY, Liao YR (2011) Analysis on grey control system of regional logistics capability and industrial economy in Jiangxi province. East China Econ Manag 7(25):23–26

    Google Scholar 

  69. Dou Y, Zhu Q, Sarkis J (2014) Evaluating green supplier development programs with a grey-analytical network process-based methodology. Eur J Oper Res 233(2):420–431

    Article  MATH  Google Scholar 

  70. Xia X, Govindan K, Zhu Q (2015) Analyzing internal barriers for automotive parts remanufacturers in China using grey-DEMATEL approach. J Clean Prod 87:811–825

    Article  Google Scholar 

  71. Huang GH, Baetz BW, Patry GG (1995) Grey integer programming: an application to waste management planning under uncertainty. Eur J Oper Res 83(3):594–620

    Article  MATH  Google Scholar 

  72. Lin JJ, Li CN (2008) A grey programming model for regional transit-oriented development planning. Pap Reg Sci 87(1):119–138

    Article  Google Scholar 

  73. Tsai MS, Hsu FY (2010) Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration. IEEE Trans Power Syst 25(2):1126–1133

    Article  Google Scholar 

  74. Chang NB, Makkeasorn A (2010) Optimal site selection of watershed hydrological monitoring stations using remote sensing and grey integer programming. Environ Model Assess 15(6):469–486

    Article  Google Scholar 

  75. Han Y, Huang YF, Wang GQ, Maqsood I (2011) A multi-objective linear programming model with interval parameters for water resources allocation in Dalian city. Water Resour Manag 25(2):449–463

    Article  Google Scholar 

  76. Liu BB, Guo YJ (2011) A grey linear bilevel programming problem with multiple independent followers and its algorithm. J Jilin Univ (Sci Ed) 4:10

    Google Scholar 

  77. Lee YS, Tong LI (2011) Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers Manag 52(1):147–152

    Article  Google Scholar 

  78. Sadeghieh A, Dehghanbaghi M, Dabbaghi A, Barak S (2012) A genetic algorithm based grey goal programming (G3) approach for parts supplier evaluation and selection. Int J Prod Res 50(16):4612–4630

    Article  Google Scholar 

  79. Sadeghi M, Hajiagha SHR, Hashemi SS (2013) A fuzzy grey goal programming approach for aggregate production planning. Int J Adv Manuf Technol 64(9–12):1715–1727

    Article  Google Scholar 

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  1. Department of Management Studies (DoMS), Indian Institute of Technology Madras, Chennai, 600 036, India

    R. Rajesh

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Rajesh, R. Group decision-making and grey programming approaches to optimal product mix in manufacturing supply chains. Neural Comput & Applic 32, 2635–2649 (2020). https://doi.org/10.1007/s00521-018-3675-y

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