Intelligent Model of Scheduling Rfacs - Part I: Methodology and Strategy

Production scheduling of advanced manufacturing systems has attracted significant attention of both researchers and industrial practitioners in recent years. Due to the complexity in these systems, the generation of production schedules requires an intelligent technique. Many artificial intelligence techniques such as fuzzy logic, genetic algorithms and neural networks have been successfully applied to the scheduling of advanced manufacturing systems. One such system is robotic flexible assembly cells (RFACs). Few studies have been done on the problem of scheduling RFACs. The major limitation is that these studies are limited to the assembly of only one product type. The objective of this chapter is to propose a new intelligent model of scheduling RFACs in a multi-product assembly environment, using fuzzy logic.

[1]  S. Balakrishnan,et al.  Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell , 2008, J. Intell. Manuf..

[2]  Shimon Y. Nof,et al.  Assembly and Disassembly: An Overview and Framework for Cooperation Requirement Planning with Conflict Resolution , 2003, J. Intell. Robotic Syst..

[3]  Nikos A. Aspragathos,et al.  Time-Optimal Task Scheduling for Two Robotic Manipulators Operating in a Three-Dimensional Environment , 2010 .

[4]  Emin Gundogar,et al.  Fuzzy priority rule for job shop scheduling , 2004, J. Intell. Manuf..

[5]  Tae-Eog Lee,et al.  Automata-based supervisory control logic design for a multi-robot assembly cell , 2002, Int. J. Comput. Integr. Manuf..

[6]  Roman Buil,et al.  Improvement of Lagrangian Relaxation Convergence for Production Scheduling , 2012, IEEE Transactions on Automation Science and Engineering.

[7]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[8]  Manoj Kumar Tiwari,et al.  A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model , 2004 .

[9]  Yoke San Wong,et al.  Job Shop Scheduling with Dynamic Fuzzy Selection of Dispatching Rules , 2000 .

[10]  S. Swegles Business Process Modeling With Simprocess , 1997, Winter Simulation Conference Proceedings,.

[11]  Ebrahim Shayan,et al.  A fuzzy logic modelling of dynamic scheduling in FMS , 2006 .

[12]  Tadeusz Sawik Production Planning and Scheduling in Flexible Assembly Systems , 1998 .

[13]  Manoj Kumar Tiwari,et al.  Machine loading problem of FMS: A fuzzy-based heuristic approach , 2001 .

[14]  Ranga V. Ramasesh Dynamic job shop scheduling: A survey of simulation research , 1990 .

[15]  Chandrasekharan Rajendran,et al.  New dispatching rules for shop scheduling: A step forward , 2000 .

[16]  Kazem Abhary,et al.  Intelligent Model of Scheduling Rfacs - Part Ii: Application , 2013 .

[17]  I. Mahdavi,et al.  Applying fuzzy rule based to flexible routing problem in a flexible manufacturing system , 2009, 2009 IEEE International Conference on Industrial Engineering and Engineering Management.

[18]  Hing Kai Chan,et al.  A comprehensive survey and future trend of simulation study on FMS scheduling , 2004, J. Intell. Manuf..

[19]  Lin Danping,et al.  A review of the research methodology for the re-entrant scheduling problem , 2011 .

[20]  Mani S. Manivannan Robotic collision avoidance in a flexible assembly cell using a dynamic knowledge base , 1993, IEEE Trans. Syst. Man Cybern..

[21]  Ye Li,et al.  An effective TPA-based algorithm for job-shop scheduling problem , 2011, Expert Syst. Appl..

[22]  Peng Wang,et al.  A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems , 2010, Appl. Soft Comput..