FMS scheduling with knowledge based genetic algorithm approach

In this paper a complex scheduling problem in flexible manufacturing system (FMS) has been addressed with a novel approach called knowledge based genetic algorithm (KBGA). The literature review indicates that meta-heuristics may be used for combinatorial decision-making problem in FMS and simple genetic algorithm (SGA) is one of the meta-heuristics that has attracted many researchers. This novel approach combines KB (which uses the power of tacit and implicit expert knowledge) and inherent quality of SGA for searching the optima simultaneously. In this novel approach, the knowledge has been used on four different stages of SGA: initialization, selection, crossover, and mutation. Two objective functions known as throughput and mean flow time, have been taken to measure the performance of the FMS. The usefulness of the algorithm has been measured on the basis of number of generations used for achieving better results than SGA. To show the efficacy of the proposed algorithm, a numerical example of scheduling data set has been tested. The KBGA was also tested on 10 different moderate size of data set to show its robustness for large sized problems involving flexibility (that offers multiple options) in FMS.

[1]  H. D. Thomas,et al.  SUCCESSFUL KNOWLEDGE MANAGEMENT PROJECTS , 1998 .

[2]  Mieczysław Wodecki,et al.  A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion , 2004, Comput. Oper. Res..

[3]  I. Moon,et al.  Genetic algorithms for job shop scheduling problems with alternative routings , 2008 .

[4]  John M. Wilson,et al.  A genetic algorithm for flow shop scheduling problems , 2004, J. Oper. Res. Soc..

[5]  F. Pezzella,et al.  A genetic algorithm for the Flexible Job-shop Scheduling Problem , 2008, Comput. Oper. Res..

[6]  Chao-Ton Su,et al.  Intelligent scheduling controller for shop floor control systems: A hybrid genetic algorithm/decision tree learning approach , 2003 .

[7]  Carla O'Dell,et al.  If Only We Knew What We Know: Identification and Transfer of Internal Best Practices , 1998 .

[8]  Rubén Ruiz,et al.  TWO NEW ROBUST GENETIC ALGORITHMS FOR THE FLOWSHOP SCHEDULING PROBLEM , 2006 .

[9]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[10]  Mitsuo Gen,et al.  Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey , 2009, Comput. Ind. Eng..

[11]  Ruey-Shiang Guh,et al.  GA-based learning bias selection mechanism for real-time scheduling systems , 2009, Expert Syst. Appl..

[12]  I. Nonaka,et al.  How Japanese Companies Create the Dynamics of Innovation , 1995 .

[13]  Amrit Tiwana,et al.  A design knowledge management system to support collaborative information product evolution , 2001, Decis. Support Syst..

[14]  Fatima Ghedjati,et al.  Genetic algorithms for the job-shop scheduling problem with unrelated parallel constraints: heuristic mixing method machines and precedence , 1999 .

[15]  M. H. Hamza.  Proceedings of the IASTED International Conference, artificial intelligence and soft computing , 1997 .

[16]  Hong Zhou,et al.  The hybrid heuristic genetic algorithm for job shop scheduling , 2001 .

[17]  Jim Hutchison,et al.  Scheduling approaches for random job shop flexible manufacturing systems , 1991 .

[18]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[19]  I. Nonaka.  A Dynamic Theory of Organizational Knowledge Creation , 1994 .

[20]  L. N. Van Wassenhove,et al.  Analysis of Scheduling Rules for an FMS , 1990 .

[21]  Manoj Kumar Tiwari,et al.  Scheduling of flexible manufacturing systems: An ant colony optimization approach , 2003 .

[22]  Thomas H. Davenport,et al.  Book review:Working knowledge: How organizations manage what they know. Thomas H. Davenport and Laurence Prusak. Harvard Business School Press, 1998. $29.95US. ISBN 0‐87584‐655‐6 , 1998 .

[23]  Bor-Wen Cheng,et al.  A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm , 2007, Expert Syst. Appl..

[24]  Pei-Chann Chang,et al.  Two-phase sub population genetic algorithm for parallel machine-scheduling problem , 2005, Expert Syst. Appl..

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

[26]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[27]  S. Wadhwa,et al.  Modeling FMS with decision Petri nets , 1989 .

[28]  Ming Liang,et al.  A mixed-integer programming approach to the machine loading and process planning problem in a process layout environment , 1990 .

[29]  D. Upton.  The Management of Manufacturing Flexibility , 1994 .

[30]  Kathryn E. Stecke,et al.  Formulation and Solution of Nonlinear Integer Production Planning Problems for Flexible Manufacturing Systems , 1983 .

[31]  Alok R. Chaturvedi.  FMS scheduling and control: Learning to achieve multiple goals , 1993 .

[32]  M. Nieto.  From R&D management to knowledge management , 2003 .

[33]  Ji-Hyung Park,et al.  A Genetic Algorithm Approach to Job Shop Scheduling Considering Alternative Process Plans , 1998 .

[34]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[35]  A. Mileham,et al.  Applications of particle swarm optimisationin integrated process planning and scheduling , 2009 .

[36]  Subhash Wadhwa,et al.  Impact of Routing Flexibility on the Performance of an FMS—A Simulation Study , 1997 .

[37]  Chandrasekharan Rajendran,et al.  Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs , 2004, Eur. J. Oper. Res..

[38]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[39]  MengChu Zhou,et al.  MODELING FMS WITH PETRI NETS , 1999 .

[40]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[41]  Subhash Wadhwa,et al.  Flexibility: an emerging meta-competence for managing high technology , 2000, Int. J. Technol. Manag..

[42]  K. S. Rao,et al.  Flexibility-enabled lead-time reduction in flexible systems , 2005 .

[43]  Frank DiCesare,et al.  Scheduling flexible manufacturing systems using Petri nets and heuristic search , 1994, IEEE Trans. Robotics Autom..

[44]  Yueh-Min Huang,et al.  Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm , 2008, Expert Syst. Appl..