Dynamic scheduling system based on changes in job characteristics
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Dynamic Scheduling System (DSS) Based on Changes in Job Characteristics is a system that provides adjustability to a current schedule as a consequence of unpredictable or predictable changes. Changes in manufacturing systems are those that occur during production and cause the systems to behave unpredictably. The understanding of the relationship between these changes and their effects can be used to lessen such manufacturing problems. The main concept of this scheduling system is to continuously monitor and predict a manufacturing system's status so that as soon as a change is detected or able to be predicted, this scheduling system will react by offering new production schedules to lessen the effects of this change. This system will integrate several techniques (e.g., control chart, forecasting model, linear regression, and statistical analysis) to provide a scheduling system that can be used in a dynamic manufacturing system. This dissertation shows in detail how to develop and test a DSS prototype. Simulation modeling and statistical analysis are used as a basis to select appropriate variables in this prototype. A hypothetical sk-machine dynamic job shop is developed by using GPSS/H simulation language to compare three performance measures, which are weighted mean flow time, weighted mean tardiness, and weighted mean lateness obtained from DSS prototype versus four dispatching rules (SPT, S/OPN, FIFO, and EDD). By comparing results from 300 random test cases, it is found that generally DSS can produce resuhs as good as the best results obtained among SPT, S/OPN, FIFO, and EDD 84% ofthe time. However, in weighted mean flow time and weighted mean lateness performance measures, DSS has matched up to 95% ofthe best results. Thus, based on these sunulations, this prototype of DSS has shown that by incorporating the abilities to monitor, forecast, and adjust the current schedules in a dynamic manufacturing system, undesirable results can be avoided.