Adaptive dispatching using genetic algorithms for multiple resources

Title:	Adaptive dispatching using genetic algorithms for multiple resources
Author:	Wongsavengwate, Pisamai
Description:	Genetic Algorithms (GAs) have been applied to a variety of engineering problems with varying degrees of success. With two essential operators, crossover and mutation, genetic algorithms are able to find satisfying solutions in dynamic environments. This research investigates the utilization of genetic algorithms to improve the selection of parameters (e.g., sequencing and selection rules, load sizes, release strategies, resource allocation policies) for industrial scheduling systems based on discrete event simulators. The number of these parameters is very large and leads to combinatorial explosions. This research was limited to the dispatching rules that drive the selection of job orders in each manufacturing resource. Specific software and algorithms were developed and an industrial grade scheduler was utilized to define and implement a research prototype. The different genetic algorithms variations and the emphasis of the genetic operators were examined using this research prototype. The selection of parameters using genetic algorithms is further considered using the information from an existing manufacturing environment to demonstrate its feasibility.
Permanent Link:	http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1184598551 http://hdl.handle.net/2374.OX/14101
Date:	1997

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