Abstract

We combine genetic algorithms and case-based reasoning principles to find optimally directed solutions to open shop scheduling and open shop re-scheduling problems. Appropriate solutions to open shop scheduling problems are injected into the genetic algorithm's population to speed up and augment genetic search on a related open shop re-scheduling problem. Preliminary results indicate that the combined genetic algorithm -- case-based reasoning system quickly finds better solutions than the genetic algorithm alone. Keywords: Genetic Algorithms, Case-Based Reasoning, Open Shop Scheduling. 1 Introduction Genetic algorithms (GAs) are stochastic, parallel search algorithms based on the mechanics of natural selection, the process of evolution [3, 2]. GAs were designed to efficiently search large, non-linear search spaces where expert knowledge is lacking or difficult to encode and where traditional optimization techniques fail. They are flexible and robust, exhibiting the adaptiveness and g...

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