Computer-Aided Reconfiguration Planning: An Artificial Intelligence-Based Approach

The manufacturing industry today faces a highly volatile market in which manufacturing systems must be capable of responding rapidly to market changes while fully exploiting existing resources. Reconfigurable manufacturing systems (RMS) are designed for this purpose and are gradually being deployed by many mid-to-large volume manufacturers. The advent of RMS has given rise to a challenging problem, namely, how to economically and efficiently reconfigure a manufacturing system and the reconfigurable hardware within it so that the system can meet new requirements. This paper presents a solution to this problem that models the reconfigurability of a RMS as a network of potential activities and configurations to which a shortest path graph-searching strategy is applied. Two approaches using the A* algorithm and a genetic algorithm are employed to perform this search for the reconfiguration plan and reconfigured system that best satisfies the new performance goals. This search engine is implemented within an AI-based computer-aided reconfiguration planning (CARP) framework, which is designed to assist manufacturing engineers in making reconfiguration planning decisions. Two planning problems serve as examples to prove the effectiveness of the CARP framework.

[1]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[2]  Felix T.S. Chan,et al.  The Applications of Flexible Manufacturing Technologies in Business Process Reengineering , 2001 .

[3]  Derek Yip-Hoi,et al.  Design Principles for Machining System Configurations , 2002 .

[4]  F. Jovane,et al.  Reconfigurable Manufacturing Systems , 1999 .

[5]  Li Tang,,et al.  Concurrent Line-Balancing, Equipment Selection and Throughput Analysis for Multi-Part Optimal Line Design , 2004 .

[6]  Cheng Wu,et al.  Modeling and analysis of multi‐stage transfer lines with unreliable machines and finite buffers , 2000, Ann. Oper. Res..

[7]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

[8]  Alf Kimms,et al.  Minimal investment budgets for flow line configuration , 1998 .

[9]  John Bradford,et al.  A non-linear redesign methodology for manufacturing systems in SMEs , 2002, Comput. Ind..

[10]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[11]  Peter H. Aiken,et al.  Synergy Between Business Process and Systems Reengineering , 1998, Inf. Syst. Manag..

[12]  Rina Dechter,et al.  Generalized best-first search strategies and the optimality of A* , 1985, JACM.

[13]  Ming Zhou,et al.  Formation of independent flow-line cells based on operation requirements and machine capabilities , 1998 .

[14]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[15]  Kamal K. Gupta,et al.  Planning quasi-static fingertip manipulations for reconfiguring objects , 1999, IEEE Trans. Robotics Autom..

[16]  Grier C. I. Lin,et al.  Development of an automated flexible fixture for planar objects , 1998 .

[17]  Wallace J. Hopp,et al.  Optimal design of stochastic production lines: a dynamic programming approach , 2002 .