Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Most of the past studies regarding machining optimization were based on machining science and economic considerations without the environmental dimension. Machining with higher cutting speed is usually desirable considering productivity, but requires high power load peak. In Taiwan, electricity price goes up sharply if the instantaneous power demand is over the contract capacity. Production scheduling problems have been widely studied for decades. However, majority of these studies consider jobs are known and processing times are certain. Besides, traditional sequencing and scheduling models deals with the economic objectives. There is still a severe lack of environmental considerations for production scheduling problems. In this study, we deal with a production scheduling problem for a manufacturing system with a bounded power demand peak. It is necessary to simultaneously determine proper cutting conditions for jobs and assign them to machines for processing without exceeding the electricity load limit at any point of time. A two-stage heuristic approach is proposed to solve the parallel machine scheduling problem with the goal of minimizing makespan. An illustrated instance with 3 machines and 20 jobs, each job in details with four possible cutting parameter settings for selection, is studied and employed to investigate the performance of the proposed heuristi