Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 7/2020

06-09-2018 | Original Article

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

Author: R. Rajesh

Published in: Neural Computing and Applications | Issue 7/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Auto-MeDiSine: an auto-tunable medical decision support engine using an automated class outlier detection method and AutoMLP
next article Robust exponential stabilization for uncertain neutral neural networks with interval time-varying delays by periodically intermittent control

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Pettigrew AM (2014) The politics of organizational decision-making. Routledge, AbingdonCrossRef
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, BerlinMATH
3.
Ross DF (2013) Competing through supply chain management: creating market-winning strategies through supply chain partnerships. Springer Science & Business Media, Berlin
4.
5.
6.
7.
Hwang CL, Lin MJ (2012) Group decision making under multiple criteria: methods and applications, vol 281. Springer Science & Business Media, Berlin
8.
9.
Rajesh R (2018) On sustainability, resilience, and the sustainable–resilient supply networks. Sustain Product Consum 15:74–88CrossRef
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 118:161–169CrossRef
11.
Rajesh R (2018) Measuring the barriers to resilience in manufacturing supply chains using Grey clustering and VIKOR approaches. Measurement 126:259–273CrossRef
12.
Xu Z, Wang H (2016) Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul Comput 1(1):21–35CrossRef
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):183–191CrossRefMathSciNetMATH
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):2342–2358CrossRefMATH
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):727–737CrossRef
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):245–259CrossRefMathSciNetMATH
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 96:29–39CrossRef
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 329:274–286CrossRef
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 99:71–78CrossRef
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):3872–3885CrossRef
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):204–211CrossRef
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 257–277CrossRef
24.
Muralidharan C, Anantharaman N, Deshmukh SG (2002) A multi-criteria group decision making model for supplier rating. J Supply Chain Manag 38(4):22CrossRef
25.
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):11363–11368CrossRef
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 6:23–33CrossRef
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):97–112CrossRef
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):235–269CrossRef
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):423–448CrossRefMATH
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 26:42–56CrossRef
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):275–286CrossRef
33.
Escobar MT, Moreno-Jiménez JM (2007) Aggregation of individual preference structures in AHP-group decision making. Group Decis Negot 16(4):287–301CrossRef
34.
Arias-Nicolás JP, Pérez CJ, Martín J (2008) A logistic regression-based pairwise comparison method to aggregate preferences. Group Decis Negot 17(3):237–247CrossRef
35.
Huang YS, Liao JT, Lin ZL (2009) A study on aggregation of group decisions. Syst Res Behav Sci 26(4):445–454CrossRef
36.
Xu Z, Yager RR (2010) Power-geometric operators and their use in group decision making. IEEE Trans Fuzzy Syst 18(1):94–105CrossRef
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):527–539CrossRef
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):355–372CrossRef
39.
40.
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):1439–1445CrossRef
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):269–274CrossRef
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):1395–1405CrossRef
44.
45.
46.
Rajesh R (2016) Forecasting supply chain resilience performance using grey prediction. Electron Commer Res Appl 20:42–58CrossRef
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):1215–1221CrossRef
48.
Lijuan WANG (2009) Grey-game in the perishable agricultural products supply chain under competition. J Wuhan Univ (Nat Sci Ed) 2:4MathSciNet
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):307–322CrossRef
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):638–650CrossRef
51.
Tseng ML, Chiu AS (2013) Evaluating firm’s green supply chain management in linguistic preferences. J Clean Prod 40:22–31CrossRef
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):1127–1145CrossRef
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):24–30CrossRef
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):1906–1916CrossRef
55.
Rief D, Van Dinther C (2010) Negotiation for cooperation in logistics networks: an experimental study. Group Decis Negot 19(3):211–226CrossRef
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):79–114CrossRef
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 159:56–65CrossRef
58.
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 38:405–422CrossRef
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):597–627CrossRef
61.
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 89:337–347CrossRefMathSciNetMATH
63.
Xu Z, Cai X (2015) Group decision making with incomplete interval-valued intuitionistic preference relations. Group Decis Negot 24(2):193–215CrossRef
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):626–638CrossRefMathSciNetMATH
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 122:39–50CrossRef
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):1704–1718CrossRef
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
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
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):420–431CrossRefMATH
70.
Xia X, Govindan K, Zhu Q (2015) Analyzing internal barriers for automotive parts remanufacturers in China using grey-DEMATEL approach. J Clean Prod 87:811–825CrossRef
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):594–620CrossRefMATH
72.
Lin JJ, Li CN (2008) A grey programming model for regional transit-oriented development planning. Pap Reg Sci 87(1):119–138CrossRef
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):1126–1133CrossRef
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):469–486CrossRef
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):449–463CrossRef
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
77.
Lee YS, Tong LI (2011) Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers Manag 52(1):147–152CrossRef
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):4612–4630CrossRef
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):1715–1727CrossRef
Metadata
Title
Group decision-making and grey programming approaches to optimal product mix in manufacturing supply chains
Author
R. Rajesh
Publication date
06-09-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3675-y

Other articles of this Issue 7/2020

Neural Computing and Applications 7/2020 Go to the issue

Deep Learning & Neural Computing for Intelligent Sensing and Control

Content semantic image analysis and storage method based on intelligent computing of machine learning annotation

Original Article

An optimum forceful generation scheduling and unit commitment of thermal power system using sine cosine algorithm

Original Article

Integrating mutation scheme into monarch butterfly algorithm for global numerical optimization

Original Article

Improved word-level handwritten Indic script identification by integrating small convolutional neural networks

Original Article

Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier

Deep Learning & Neural Computing for Intelligent Sensing and Control

Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm

Premium Partner

    Image Credits