Intelligent multi-objective decision-making model with RFID technology for production planning

A multi-objective production planning problem in the labor-intensive manufacturing industry is investigated. An intelligent and real-time multi-objective decision-making model is developed to provide timely and effective solutions for this problem by integrating RFID technology with intelligent optimization techniques, in which RFID technology is used to collect real-time production data, a novel (μ/ρ+λ)-evolution strategy process with self-adaptive population size and novel recombination operation is proposed and integrated with effective non-dominated sorting and pruning techniques to generate Pareto optimal solutions for real-world production. Experiments based on industrial data were conducted to evaluate the effectiveness of the proposed model. Experimental results show that the proposed model can effectively solve the investigated problem by providing production planning solutions superior to industrial solutions.

[1]  Mir-Bahador Aryanezhad,et al.  A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty , 2011 .

[2]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[3]  K. Chandra Sekaran,et al.  Improved NSGA-II Based on a Novel Ranking Scheme , 2010, ArXiv.

[4]  Evan J. Hughes,et al.  Evolutionary many-objective optimisation: many once or one many? , 2005, 2005 IEEE Congress on Evolutionary Computation.

[5]  Haiying Ren,et al.  An efficient priority rule for scheduling job shops to minimize mean tardiness , 2006 .

[6]  E. Powell Robinson,et al.  Master production scheduling policy and rolling schedules in a two-stage make-to-order supply chain , 2008 .

[7]  Alexandre Dolgui,et al.  Supply planning under uncertainties in MRP environments: A state of the art , 2007, Annu. Rev. Control..

[8]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[9]  Francisco Luna,et al.  Evolutionary algorithms for solving the automatic cell planning problem: a survey , 2010 .

[10]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[11]  D G Bobrow,et al.  Applications of Artificial Intelligence , 1999 .

[12]  Lihui Wang,et al.  Assembly process planning and its future in collaborative manufacturing: a review , 2009 .

[13]  Wing-Keung Wong,et al.  Applications of artificial intelligence in the apparel industry: a review , 2011 .

[14]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[15]  Hisao Ishibuchi,et al.  Multi-objective genetic local search algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[16]  Elena Marchiori,et al.  Evolutionary Algorithms with On-the-Fly Population Size Adjustment , 2004, PPSN.

[17]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[18]  M. A. Wazed,et al.  A Review of Manufacturing Resources Planning Models Under Different Uncertainties: State-of-The-Art and Future Directions , 2010 .

[19]  Shunji Tanaka,et al.  A branch-and-bound algorithm with Lagrangian relaxation to minimize total tardiness on identical parallel machines , 2008 .

[20]  Sven Axsäter.  Planning order releases for an assembly system with random operation times , 2005, OR Spectr..

[21]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[22]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  Zhaoxia Guo.  Intelligent production control decision-making for apparel manufacturing process , 2008 .

[25]  C. L. Valenzuela.  A simple evolutionary algorithm for multi-objective optimization (SEAMO) , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[26]  Reha Uzsoy,et al.  Scheduling and order release in a single-stage production system , 1995 .

[27]  Zhi-Long Chen,et al.  Order Assignment and Scheduling in a Supply Chain , 2006, Oper. Res..

[28]  Wai Keung Wong,et al.  A Genetic-Algorithm-Based Optimization Model for Solving the Flexible Assembly Line Balancing Problem With Work Sharing and Workstation Revisiting , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[29]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[30]  Heidi A. Taboada,et al.  Multi-objective scheduling problems: Determination of pruned Pareto sets , 2008 .

[31]  A. E. Eiben,et al.  Genetic algorithms with multi-parent recombination , 1994, PPSN.

[32]  Hing Kai Chan,et al.  The State of the Art in Simulation Study on FMS Scheduling: A Comprehensive Survey , 2002 .

[33]  Ali Tozkapan,et al.  A branch and bound algorithm to minimize the total weighted flowtime for the two-stage assembly scheduling problem , 2003, Comput. Oper. Res..

[34]  William A. Martin,et al.  Applications of Artificial Intelligence , 1977, IJCAI.

[35]  David Wyld,et al.  RFID 101: The Next Big Thing for Management , 2006, IEEE Engineering Management Review.

[36]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .