Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Nagi, R.Subject: search.filters.subject.computer integrated manufacturing

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    A Class of Conflict Free Petri Nets Used for Controlling Manufacturing Systems
    (1992) Harhalakis, George; Levantopoulos, Marios M.; Lin, Chang-Pin; Nagi, R.; Proth, J.M.; ISR
    This paper is devoted to the behavior, evaluation and management of non-cyclic discrete systems in general and manufacturing systems in particular. We introduce a special type of Petri nets called CFIOs (Conflict-Free nets with Input and Output transitions). It is shown that CFIOs are live, reversible if consistent, and can be kept bounded under certain conditions. We also develop reduction rules which facilitate the computation of the t-invariants of CFIOs. We then take advantage of the qualitative properties of CFIOs to perform planning in manufacturing systems. Numerical examples illustrate these approaches
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    Hierarchical Modeling Approach for Production Planning
    (1992) Harhalakis, George; Nagi, R.; Proth, J.M.; ISR
    Production management problems are complex owing to large dimensionality, wide variety of decisions of varying scope, focus and time-horizon, and disturbances. A hierarchical approach to these problems is a way to address this complexity, wherein the global problem is decomposed into a series of top-down sub- problems. We advocate that a single planning architecture cannot be employed for all planning problems. We propose a multi-layer hierarchical decomposition which is dependent on the complexity of the problem, and identify the factors influencing complexity. A systematic stepwise design approach for the construction of the hierarchy and inputs required are presented. The subsequent operation of the hierarchy in an unreliable environment is also explained. Aggregation schemes for model reduction have been developed and blended with a time-scale decomposition of activities to provide the theoretical foundation of the architecture. It is also hoped that this methodology can be applied to other such large-scale complex decision making problems.
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    Design and Operation of Hierarchical Production Management Systems
    (1991) Nagi, R.; Harhalakis, G.; ISR
    Production Planning Management and Control of a production system subject to random events are challenging problems. A multi-layer hierarchical approach to tactical and aggregate production planning problems is proposed, wherein the architecture is strongly based on the specific physical system, applicable controls and the complexity of the decision making problem at hand. We address the design and operation of such Hierarchical production Management Systems (HPMS). Regarding the design aspect, we start by developing schemes for product, machine and temporal aggregation; consistency and controllability issues in the hierarchy have been addressed in the aggregation/disaggregation schemes. These three aggregation schemes for model reduction have been developed and incorporated to the time-scale decomposition of activities, in order to provide a solid theoretical foundation of the architecture. We then proceed to a systematic stepwise design approach for the construction of the hierarchy. It provides the appropriate number of layers and an associated Model as well as Decision Making Problem (DMP) at each level. A model is defined by entities, attributes, links and domains, while a DMP is defined by a set of possible controls (decisions), constraints, and optimality criteria to be optimized over a planning horizon. The operation of the hierarchy consists of a topdown computation of controls, which calls for the resolution of an optimization problem at each level of the hierarchy. The solution of any problem in sequence determines some parameters in the subsequent problem. We detail the mechanism for top-down constraint propagation, which is important in ensuring consistency of criteria and feasibility. The execution then involves the bottom-up feedbacks, and a revision in the plan is carried out if necessary. In particular, the rolling horizon mechanism, and the reaction of the hierarchy to random events has been detailed. A generic job-shop example has been employed to present the design and operation of the HPMS. It is hoped that this methodology can be applied to other types of large-scale complex decision making problems.