T e c h n i o n - Israel Institute of Technology
The William Davidson Faculty of Industrial Engineering and Management

Computer Integrated Manufacturing - CIM

At the beginning of the 20th century, automation, at various production settings, played an important role in improving productivity and quality while reducing cost. However, this automation was fixed, rigid and tailored specifically to each product. Therefore, it could have been used only to produce a very limited product variety, as Henry Ford put it: we can offer any color car as long as it's black. Starting from the late 70's of the 20th century, there was an increase in the demand for product variety, fast deliveries, high quality and reasonable prices. This caused flexibility and efficiency to become essential requirements in any manufacturing systems. Today we take it for granted that we can purchase customized cars as well as other commodities such as computers clothes and even shoes of the highest quality, in affordable prices, delivered in a timely manner directly at our door steps.

Instrumental to these achievements is the Computer Integrated Manufacturing (CIM) technology. CIM enables the implementation of flexible design and manufacturing through the integration of the different available islands of automation such as:

  • Computer Automated Design (CAD) tools that enable the storage, retrieval, manipulation and presentation of geometric models.

  • Computer Automated Process Planning (CAPP) tools that enable the conversion of the geometric models into machine code which represents a machining sequence.

  • Computer Automated Manufacturing (CAM) which includes Computer Numerical Machines (CNC), Flexible Manufacturing Cells and Systems (FMC and FMS) that enable to modify substance according to a predetermined operation sequence.

  • Computerized vision systems that are used for quality assurance and control of the products that are produced by the manufacturing system.

  • Automated Material Handling Systems (AMHS) which includes robots, Automated Guided Vehicles (AGV), conveyors, and Automated Storage/Retrieval Systems (AS/RS) that enable to move and store items between the different operation stages.

Sinreich, D. and Nelkenbaum, B. (2006), "Managing Flexible Equipment", Research Executive Summaries, Edited by Candace Yano, Industrial Engineer, Vol. 38. No. 10, pp. 52.

Papers

  1. Sinriech, D. and Abekasis, M. (1998), "Process Selection and Tool Assignment in Cellular Manufacturing Using Genetic Algorithms", Annals of Operations Research, Focused Issue on Design and Planning Integration in Cellular Manufacturing Systems, Vol. 77, pp. 51-78.

  2. Sinriech, D., Rubinovitz, J., Milo, D. and Nakbily, G. (2001), "Sequencing, Scheduling and Tooling Single-Stage Multifunctional Machines in a small Batch Environment". IIE Transactions, Vol. 33, No. 10, pp. 897-911.

  3. Shnits, B., Rubinovitz, J. and Sinreich, D. (2004), "Multycriteria Dynamic Scheduling Methodology for Controlling a Flexible Manufacturing System", International Journal of Production Research, Vol. 42, No, 17, pp 3457-3472.

  4. Shnits, B. and Sinreich D. (2005), "Controlling Flexible Manufacturing Systems Based on a Dynamic Selection of an Appropriate Operational Criteria and Scheduling Policy", Forthcoming in Internationali Journal of Flexible Manufacturing Systems.

  5. Sinreich D. and Nelkenbaum D.B. (2006), " Determining Production Sequences For Single Stage Multifunctional Machining Systems Based on the Tradeoff between Fixture Cost, Re-Fixturing and Tool Replenishment",
    IIE Transactions Vol. 38, No. 10, pp. 813-828.

  6. Sinreich, D. and Shnits, B. (2006) "A Robust FMS Control Architecture with an Embedded Adaptive Scheduling Mechanism", Working Paper.


Funded Research

Embedding an Adaptive Scheduling Mechanism in a Robust FMS Control System Framework

Flexible Manufacturing Systems (FMSs) were developed to accommodate fast changing market demands. Flexibility in these systems was made possible largely due to the use of versatile and/or redundant machines these in turn enable alternative routing in the system. In cases of machine failure alternative routing made it possible to better balance machine work-load and improve system robustness. This in turn was instrumental in obtaining higher system productivity. As a result, it is clear that the performance of an FMS is highly dependent on the selection of the correct scheduling policy to control the system. However, since these systems usually operate in a highly dynamic environment, where product mix and overall system objectives are changing rapidly, developing an optimal dynamic scheduling and control scheme for an FMS is a very complex task.

In reality, contrary to these studies, especially due to practical limitations, FMS controllers are much simpler and usually use fixed scheduling rules to operate the system. In order to overcome this hurdle, we propose a new robust FMS control approach the will be described in the following sections.

The changes that the system needs to cope with can be classified into two major groups. The first are internal changes such as shop floor delays caused by machine failure, breakdowns or maintenance. These may affect part routing, dispatching rules, and other control decisions. The second group includes external changes such as changes in market demand, changes in organizational goals, or changes in order priority. These may affect the system objectives. There is a major difference in the time scope between the two groups. In the case the FMS scheduling period is very short e.g. a shift or in some cases even a day, it is safe to regard the FMSs external environment as constant. Based on this, instead of implementing a single highly complex controller (adaptive or reactive), as was studied in the past, a robust control framework is suggested, in which a new simple controller is developed automatically for every scheduling period (next shift or next day) or whenever internal conditions call for a major change that the current controller can not accommodate. The creation of a new controller can be achieved by automatically developing a simulation model which will serve, when operated in Real-Time as the system controller. As a result, the external source of variability is practically eliminated.

Unfortunately, the same can not be argued regarding the possible internal changes the FMS may encounter. In order to deal with these an adaptive type scheduling mechanism is embedded within the robust control architecture. This will expand the dynamic control approach of an FMS by using simulation to evaluate the possible internal changes in the system and adapt its objectives to multiple and changing system evaluation criteria, all in order to better exploit the system's flexibility through its alternative production routes. The suggested adaptive control framework is largely based on the use of ARENA simulation tool. This simulation tool was selected among others due to its ability to operate in conjunction with a Real Time (RT) package which is used as the real time shop floor control software. Moreover, VBA is an integral part of ARENA. This enables convenient access to databases, and the automated creation of Arena models.

The suggested control scheme is planned to be implemented and tested using the semi-industrial manufacturing system at the Robotics and CIM Center that belongs to the Industrial Engineering and management Faculty at the Technion and eventually replace entirely the old rigid PLC based controller.

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