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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Item Reconfigurable Control in Discrete Event Dynamic Systems Applied to Manufacturing Systems(1993) Dhingra, Jastej S.; Blankenship, Gilmer L.; ISRProduction management in an automated manufacturing system entails the implementation of the following two decision functions: Operational Planning and Resource Allocation, and Production Control. In this work we present scheduling and production control algorithms for manufacturing systems. We present a general manufacturing system model and formalize the concept of a schedule as a single sequence of operations. Using a general performance measure we formulate the operations scheduling problem as a combinatorial optimization problem. A simulated annealing based optimization algorithm is developed for this job shop scheduling problem. We present a three level hierarchical on-line reconfigurable control scheme. For the "process" level control, we present a reactive operations scheduling scheme. Based on the control specification at the process level, the lower "operation" level parameters are defined. We present operation control algorithms for both continuous and batch mode processing. Using dynamic programming principles, we present a Quasi-Variational Inequality based impulse control algorithm for online control of processing rates for a single batch. Perturbation Analysis, in conjunction with stochastic approximation techniques, is used for continuous mode, online processing rate control. Algorithm extensions to other discrete event systems are also discussed.Item Reactive Operations Scheduling for Flexible Manufacturing Systems(1993) Dhingra, Jastej S.; Musser, K.L.; Blankenehip, Gilmer L.; ISRIn this paper we present a reactive operations scheduling methodology for online control of manufacturing systems. We present description of the Texas Instruments electronic circuit board assembly unit at Johnson City, TN, which was used as a model for the development of the reactive operations scheduling scheme. Based on the manufacturing system model, we present the operations scheduling problem as a combinatorial optimization problem. We present a simulated annealing based predictive scheduling algorithm for feed-forward control of the manufacturing system. For reactive operations scheduling, the closed loop configuration consists of the simulated annealing based predictive scheduler and the manufacturing system model in the feed forward path, and a reactive re-scheduler in the feedback path. Deviations of the observed sequenced operation timings from the scheduled timings, quantified by a cost functional, are used to trigger the re-scheduling loop. The capabilities of the scheme are demonstrated using a discrete event simulation of the Texas Instruments manufacturing unit.Item Online Parameter Optimization for a Multi-Product, Multi-Machine Manufacturing System(1992) Dhingra, Jastej S.; Blankenehip, Gilmer L.; ISRWe develop an algorithm based on Infinitesimal Perturbation Analysis for online optimization of a multi-product service facility composed of a network of multi-server machines, modeled using multi-class M/M/m queues. starting from the Robbins-Monro stochastic approximation method, we first develop an online, local optimization algorithm for a single multi-server machine. for the special case of Poisson arrivals and exponentially distributed service time in a multi-machine network, local optimization at individual machines leads to global optimization of the overall network. Simulation results for a single machine are compared to the exact analytical results. Application of the methodology for the optimization of a simple flexible manufacturing system is also presented.Item Annealing Based Experiment in Scheduling(1991) Dhingra, Jastej S.; Musser, K.L.; Blankenehip, Gilmer L.; Ferguson, L.; ISRA scheduling software was developed for general job shop scheduling. It was implemented at a Texas Instruments electronic circuit board assembly facility. The assembly line scheduling problem is presented as an optimization problem. A variation of simulated annealing technique was used to find a solution to the optimization problem. A detailed description of the software is presented and various assembly line features which were incorporated in the software are discussed. Finally, an onsite evaluation is presented.Item Optimization Based Job Shop Scheduling(1991) Musser, K.L.; Dhingra, Jastej S.; Blankenehip, Gilmer L.; ISRA generalized job shop scheduling problem is defined in detail. The proposed factory description is sufficiently realistic to model the routing and sequencing decisions made in a real manufacturing plant. An optimization problem is posed, permitting the use of very general cost functions. A variation of the method of simulated annealing is proposed as a tool for the solution of the optimization problem. A novel technique for embedding the space of feasible schedules into a permutation group is used to define a neighborhood structure for the simulated annealing process. This technique has algorithmic advantages over working directly in the space of schedules. These ideas were used to construct a scheduling software system which is in use at a Texas Instruments Custom Manufacturing Unit. We give a brief description of the software system, called ABES for Annealing Based Experiment in Scheduling, and comment on its effectiveness.