Investigation of dynamic job allocation methods for computerized manufacturing systems with priority and due date considerations
Abstract
Global economical developments and market competition have altered the fundamentals of the manufacturing industry. The technological advances in computers, both hardware and software developments, and in the machine tool industry have enabled academic researchers, as well as practitioners, to explore, and experiment with, various novel approaches to utilize the new technology more effectively. Among the several critical issues that are being studied are the choices of control structures and the subsequent control schemes intended to best facilitate the production operations. They have great impact on system performance and evaluation. This research investigates the dynamic job allocation methods in a computerized manufacturing system environment, in which decision authority is distributed among all system entities. Each system entity can receive, store, process, and transmit information to any other entities in the system, and can act to execute commands contained in the information. In particular, we want to examine the case when systems become saturated by parts (jobs) with different priority and urgency levels. Negotiation schemes, based on a special bidding scheme, are proposed to collect updated status information from all system entities (parts, machines, material handling devices, etc.). Job assignments are then determined according to the real-time information about production requirements and facility constraints. Job urgency index is introduced to identify part urgency levels and is utilized to facilitate resource allocation decisions. Negotiations among entities of the same type are carried out to ensure global optimization, while local optimization is accomplished by negotiations among entities of different types. The simulation results of the bidding schemes are also compared with the results of applying dispatching rules in the same production constraints.
Degree
Ph.D.
Advisors
Barash, Purdue University.
Subject Area
Industrial engineering
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