A robust FMS control architecture with an embedded adaptive scheduling mechanism

Sinreich, David; Shnits, Boris

doi:10.1016/s0278-6125(06)80242-6

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…The control function is the first that should be mentioned. The common uses of simulation in the manufacturing system control applications are focused at the operational/shop floor level and are generally concerned with the ability to monitor the system on the short term basis (commonly in real-time) and the scheduling/rescheduling situations in response to uncertainties at the schedule execution levels (Jones, Yih, and Wallace 2001;Cowling and Johansson 2002;Aytug et al 2005;Son and Venkateswarana 2005;Cho, Son, and Jones 2006;Sinreich and Shints 2006;Pfeiffer et al 2008). SDDES has been designed such that it maintains the integrity of the SD and DES simulation methodologies.…”

Section: Scheduling and Controlmentioning

confidence: 99%

Enterprise scheduling: Hybrid and hierarchical issues

Pastrana¹,

Helal²,

Mendizabal³

2010

Proceedings of the 2010 Winter Simulation Conference

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We build a hybrid discrete-continuous simulation model of the manufacturing enterprise system. This model consists of an overall system dynamics model of the manufacturing enterprise and connected to it are a number of discrete event simulations for selected operational and tactical functions. System dynamics modeling best fits the macroscopic nature of activities at the higher management levels while the discrete models best fit the microscopic nature of the operational and tactical levels. An advanced mechanism based on information theory is used for the integration of the different simulation modeling modalities. In addition, the impact of the decisions at the factory level in scheduling are analyzed at the management level. The different models of control are discussed. INTRODUCTIONThe advances in information and computing technologies in addition to the unprecedented levels of competition and fast pace of changes in the business environment have created a more flattened enterprise system and changed the way enterprises should be managed. This is creating challenges to using simulation tools. The presence of manufacturing and nonmanufacturing functions in the manufacturing enterprise, the different types of behavior in the system, differences between management levels in the scope of planning, frequency of decision making, and levels of details they deal with, indicate that a single simulation approach cannot offer all that is needed in a simulation of such a complex system. The traditional use of discrete event simulation (DES) to simulate the manufacturing systems has narrowed the scope to detailed statistical analyses at the operational levels of the system. At that level, the main concerns have been the flow of materials and the levels of inventories and work in process, and the performance at the individual level of resources, units of products or processes (Smith 2003; Lee et al. The need to simulate the whole system (aggregate and detailed management level functions) has challenged DES, which was inadequate to approximate the continuous behavior in the system generally and particularly at the aggregate levels, or communicate appropriately the financial computations to higher management (Lee et al. 2002;Johnson and Eberlein 2002;Barton, Love, and Taylor 2001). DES works effectively with problems of narrow scopes, but LW LV ³« incompatible with a globDO SRLQW RI YLHZ´ /LQ HW al. 1998). And it does not address the stability of the system (Rabelo et al. 2005) that should be consi-3350 978-1-4244-9865-9/10/$26.00 ©2010 IEEE

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Section: Scheduling and Controlmentioning

confidence: 99%

Enterprise scheduling: Hybrid and hierarchical issues

Pastrana¹,

Helal²,

Mendizabal³

2010

Proceedings of the 2010 Winter Simulation Conference

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“…Dynamic scheduling is based on the successive selection of orders entered into production based on the current information about ongoing production processes [11,12]. This information may include, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…the time of completion of processing for each individual machine, the stock in station-side storages, tool changing times, equipment failures, delays in delivery of material and the results of interoperative control. Due to the very strict time limits, heuristic method and simple priority rules are most often used in this case [11].…”

Section: Introductionmentioning

confidence: 99%

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Scheduling Production Orders, Taking into Account Delays and Waste

Dylewski¹,

Jardzioch²

2014

Management and Production Engineering Review

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Accepted: 8 July 2014The article addresses the problem of determining the sequence of entering orders for production in a flexible manufacturing system implementing technological operations of cutting sheet metal. Adopting a specific ranking of production orders gives rise to the vector of delays and waste in the form of incompletely used sheets. A new method was postulated for determining the optimal sequence of orders in terms of two criteria: the total cost of delays and the amount of production waste. The examples illustrate the advantages of the proposed method compared with the popular heuristic principles.

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“…Smith [5] and Lee et al [6] provide a review of simulation-based research on manufacturing system design and operation problems. Hwang and Kim [7], Merchawi and ElMaraghy [8], Son et al [9], Sinreich and Shnits [10], and Um et al [11] adopt simulation for scheduling FMSs.…”

Section: Introductionmentioning

confidence: 99%