Compare Ordinal and Cardinal Utility
Information Flow and Decision-Making in Production Scheduling
Jeffrey W. Herrmann Department of Mechanical Engineering and Institute for Systems Research University of Maryland College Park, MD 20742
Abstract Although often studied as an isolated optimization problem, production scheduling in practice is a complex flow of information and decision-making. This paper discusses this perspective and presents ways to represent production scheduling systems. The paper uses a case study of a manufacturing facility to illustrate the concepts. KEYWORDS: Production scheduling, rescheduling, decision-making
1. Introduction
Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. In dynamic, stochastic manufacturing environments, managers, production planners, and supervisors must not only generate high-quality schedules but also react quickly to unexpected events and revise schedules in a cost-effective manner. These events, generally difficult to take into consideration while generating a schedule, disturb the system, generating considerable differences between the predetermined schedule and its actual realization on the shop floor. Rescheduling is then practically mandatory in order to minimize the effect of such disturbances in the performance of the system. In practice, production scheduling is part of the complex flow of information and decision-making that forms the manufacturing planning and control system. Such systems are typically divided into modules that perform different functions such as aggregate planning and material requirements planning [1, 2]. In this paper, production scheduling refers to the low-level, shop floor control function. A great deal of research effort has been spent developing methods to generate optimal production schedules, and countless papers discussing this topic have appeared in scholarly
Jeffrey W. Herrmann Department of Mechanical Engineering and Institute for Systems Research University of Maryland College Park, MD 20742
Abstract Although often studied as an isolated optimization problem, production scheduling in practice is a complex flow of information and decision-making. This paper discusses this perspective and presents ways to represent production scheduling systems. The paper uses a case study of a manufacturing facility to illustrate the concepts. KEYWORDS: Production scheduling, rescheduling, decision-making
1. Introduction
Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. In dynamic, stochastic manufacturing environments, managers, production planners, and supervisors must not only generate high-quality schedules but also react quickly to unexpected events and revise schedules in a cost-effective manner. These events, generally difficult to take into consideration while generating a schedule, disturb the system, generating considerable differences between the predetermined schedule and its actual realization on the shop floor. Rescheduling is then practically mandatory in order to minimize the effect of such disturbances in the performance of the system. In practice, production scheduling is part of the complex flow of information and decision-making that forms the manufacturing planning and control system. Such systems are typically divided into modules that perform different functions such as aggregate planning and material requirements planning [1, 2]. In this paper, production scheduling refers to the low-level, shop floor control function. A great deal of research effort has been spent developing methods to generate optimal production schedules, and countless papers discussing this topic have appeared in scholarly
References: 1. 2. 3. 4. 5. 6. 7. 8. Hopp, Wallace J., and Mark L. Spearman, Factory Physics, Irwin/McGraw-Hill, Boston, 1996. Vollmann, Thomas E., William L. Berry, and D. Clay Whybark, Manufacturing Planning and Control Systems, fourth edition, Irwin/McGraw-Hill, New York, 1997. Pinedo, Michael, and Xiuli Chao, Operations Scheduling with Applications in Manufacturing and Services, Irwin McGraw Hill, Boston, 1999. Pinedo, Michael, Scheduling: Theory, Algorithms, and Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1995. Vieira, Guilherme E., Jeffrey W. Herrmann, and Edward Lin, “Rescheduling manufacturing systems: a framework of strategies, policies, and methods,” Journal of Scheduling, Volume 6, Number 1, pages 35-58, 2003. McKay, Kenneth N., and Vincent C.S. Wiers, “Unifying the theory and practice of production scheduling,” Journal of Manufacturing Systems, Volume 18, Number 4, pages 241-255, 1999. McKay, K.N., F.R. Safayeni, and J.A. Buzacott, “An information systems based paradigm for decisions in rapidly changing industries,” Control Engineering Practice, Volume 3, Number 1, pages 77-88, 1995. Herrmann, Jeffrey W., and Linda C. Schmidt, “Viewing Product Development as a Decision Production System,” DETC2002/DTM-34030, Proceedings of the 14th International Conference on Design Theory and Methodology Conference, ASME 2002 Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Montreal, Canada, September 29 - October 2, 2002. Sharp, Alec, and Patrick McDermott, Workflow Modeling, Artech House, Boston, 2001. Beer, S., Brain of the Firm, Allen Lane, London, 1972. Checkland, Peter, Systems Thinking, Systems Practice, John Wiley & Sons, Ltd., West Sussex, 1999. 9. 10. 11.