Supply chain optimization modeling in uncertain environment with prediction mechanism

Purpose – The purpose of this paper is to explore new methods to improve supply chain management in uncertain environment, more specifically, to tackle the uncertain demand problem and the inventory optimization problem faced by most supply chain systems.Design/methodology/approach – The paper develops a multi‐objective inventory optimization model, which combines the classic grey prediction GM(1,1) model with the metaheuristic method. The former is applied to achieve the forecasting mechanism in supply chain operations, and the latter is applied to optimize the model solution.Findings – Results show that the grey‐based forecasting mechanism performs better than other prediction methods, such as the double exponential smoothing method used in this paper. The solution of the multi‐objective inventory optimization model is also improved with the integration of grey prediction method. These indicate the importance of a forecasting mechanism in supply chain management.Originality/value – The paper succeeds in...

[1]  Yuanjie He,et al.  Random yield risk sharing in a two-level supply chain , 2008 .

[2]  Tingjun Hou,et al.  Automated docking of peptides and proteins by using a genetic algorithm combined with a tabu search. , 1999, Protein engineering.

[3]  R. de Vivie-Riedle,et al.  Modified ant-colony-optimization algorithm as an alternative to genetic algorithms , 2009 .

[4]  Li-Wei Chen,et al.  Integration of the grey relational analysis with genetic algorithm for software effort estimation , 2008, Eur. J. Oper. Res..

[5]  D. K. Bowen,et al.  Characterization of structures from X-ray scattering data using genetic algorithms , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  Angappa Gunasekaran,et al.  Information systems in supply chain integration and management , 2004, Eur. J. Oper. Res..

[7]  Radivoj Petrovic,et al.  Supply chain modelling using fuzzy sets , 1999 .

[8]  Osamu Ueno,et al.  Introduction of time series data analysis using grey system theory , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[9]  Ron Unger The Genetic Algorithm Approach to Protein Structure Prediction , 2004 .

[10]  M Pavan,et al.  Total ranking models by the genetic algorithm variable subset selection (GA–VSS) approach for environmental priority settings , 2004, Analytical and bioanalytical chemistry.

[11]  Dobrila Petrovic,et al.  Simulation of supply chain behaviour and performance in an uncertain environment , 2001 .

[12]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[13]  Yi Lin,et al.  A SYSTEMIC ANALYSIS WITH DATA (II) , 2000 .

[14]  Christopher S. Tang Perspectives in supply chain risk management , 2006 .

[15]  William J. Kolarik,et al.  Real-time performance reliability prediction , 2001, IEEE Trans. Reliab..

[16]  Anna Nagurney,et al.  Supply chain networks, electronic commerce, and supply side and demand side risk , 2005, Eur. J. Oper. Res..

[17]  Young Hae Lee,et al.  Production-distribution planning in supply chain considering capacity constraints , 2002 .

[18]  C. Fu,et al.  Application of grey relational analysis for corrosion failure of oil tubes , 2001 .

[19]  Payam Hanafizadeh,et al.  Designing fuzzy-genetic learner model based on multi-agent systems in supply chain management , 2009, Expert Syst. Appl..

[20]  Yi Lin,et al.  Theory of grey systems: capturing uncertainties of grey information , 2004 .

[21]  GuoDong Li,et al.  New Proposal and Accuracy Evaluation of Grey Prediction GM , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[22]  Fariborz Rahimnia,et al.  Application of grey theory approach to evaluation of organizational vision , 2011, Grey Syst. Theory Appl..

[23]  D. Yamaguchi,et al.  A grey-based rough decision-making approach to supplier selection , 2008 .

[24]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

[25]  Kirstin Zimmer,et al.  Supply Chain Coordination with Uncertain Just-in-Time Delivery , 2002 .

[26]  S. S. Chaudhry *,et al.  Application of genetic algorithms in production and operations management: a review , 2005 .

[27]  Magne Setnes,et al.  GA-fuzzy modeling and classification: complexity and performance , 2000, IEEE Trans. Fuzzy Syst..

[28]  M. K. Darwish,et al.  A new control technique for active power filters using a combined genetic algorithm/conventional analysis , 2002, IEEE Trans. Ind. Electron..

[29]  Abdullah S. Al-Mudimigh,et al.  Extending the concept of supply chain:: The effective management of value chains , 2004 .

[30]  Seyed Reza Hejazi,et al.  Supply chain modeling in uncertain environment with bi-objective approach , 2009, Comput. Ind. Eng..

[31]  Gloria Pérez,et al.  An Approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming , 2003, J. Glob. Optim..

[32]  Armando Blanco,et al.  A real-coded genetic algorithm for training recurrent neural networks , 2001, Neural Networks.

[33]  Imad Alatiqi,et al.  Optimizing the Supply Chain of a Petrochemical Company under Uncertain Operating and Economic Conditions , 2004 .

[34]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[35]  Shiro Masuda,et al.  A New Reliability Prediction Model in Manufacturing Systems , 2010, IEEE Transactions on Reliability.

[36]  Jeffrey Forrest,et al.  Emergence and development of grey systems theory , 2009, Kybernetes.

[37]  Wu Shuai,et al.  Grey system model of medical diseases diagnosis and treatment , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[38]  Wang Xuemeng,et al.  China's population projections based on GM(1,1) metabolic model , 2009 .

[39]  Bo K. Wong,et al.  A Bibliography of Genetic Algorithm Business Application Research: 1988–June 1996 , 1998 .

[40]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[41]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[42]  Burkhard Rost,et al.  Using genetic algorithms to select most predictive protein features , 2009, Proteins.

[43]  Chia-Yon Chen,et al.  Applications of improved grey prediction model for power demand forecasting , 2003 .

[44]  Rob J. Hyndman,et al.  Exponential smoothing models: Means and variances for lead-time demand , 2004, Eur. J. Oper. Res..