Parallel machines (mill-turn machining centers) provide a powerful and efficient machining alternative to the traditional sequential machining process. The underutilization of parallel machines due to their operational complexity | Lee Yuan-Shin et al Soft Computing for Optimal Planning and Sequencing of Parallel Machining Operations Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton CRC Press LLC 2001 8 Soft Computing for Optimal Planning and Sequencing of Parallel Machining Operations Introduction A Mixed Integer Program A Genetic-Based Algorithm Yuan-Shin Lee Tabu Search for Sequencing Parallel Machining Operations North Carolina State University Two Reported Examples Solved Nan-Chieh Chiu by the Proposed GA North Carolina State University Two Reported Examples Solved by the Proposed Tabu Search Shu-Cherng Fang Random Problem Generator and Further Tests North Carolina State University Conclusion Abstract Parallel machines mill-turn machining centers provide a powerful and efficient machining alternative to the traditional sequential machining process. The underutilization of parallel machines due to their operational complexity has raised interests in developing efficient methodologies for sequencing the parallel machining operations. This chapter presents a mixed integer programming model for the problems. Both the genetic algorithms and tabu search methods are used to find an optimal solution. Testing problems are randomly generated and computational results are reported for comparison purposes. Introduction Process planning transforms design specifications into manufacturing processes and computer-aided process planning CAPP uses computers to automate the tasks of process planning. The recent introduction of parallel machines mill-turn machining centers can greatly reduce the total machining cycle time required by the conventional sequential machining centers in manufacturing a large batch of millturn parts 13 14 . In this chapter we consider the CAPP for this new machine tool. Dr. Lee s work was partially supported by the National Science Foundation NSF CAREER Award DMI9702374 . E-mail yslee@ 2001 CRC
