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Edward M. Sitarski

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AAAI Conference 1997 Conference Paper

Beyond Contention: Extending Texture-Based Scheduling Heuristics

J. Christopher Beck
Edward M. Sitarski

In order to apply texture measurement based heuristic commitment techniques beyond the unary capacity resource constraints of job shop scheduling, we extend the contention texture measurement to a measure of the probability that a constraint will be broken. We define three methods for the estimation of this probability and show that they perform as well or better than existing heuristics on job shop scheduling problems. Empirical insight into the performance is provided and we sketch how we have extended probability-based heuristics to more complicated scheduling constraints. the “probability of breakage” of a constraint will allow us to directly compare the criticality of different types of constraints. We present three new techniques for estimating the probability of breakage of the resource constraint in job shop scheduling and compare the performance of heuristics based on these estimations against earlier texture-based and non-texture-based heuristics for scheduling. We also describe how our estimation techniques can be extended to more complicated scheduling domains.

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AAAI Conference 1997 Conference Paper

Texture-Based Heuristics for Scheduling Revisited

J. Christopher Beck
Edward M. Sitarski

Recent scheduling work has challenged the need for sophisticated heuristics such as those based on texture measurements. This paper examines these claims in the light of advances in scheduling technology. We compare a number of current heuristic commitment techniques against a texture-based heuristic. Our results demonstrate that texture-based heuristics can outperform these widely-used heuristic commitment techniques. tics with the same consistency techniques (Nuijten, 1994). In this paper we re-evaluate texture-based heuristics in light of recent advances in scheduling technology and show that on two job shop scheduling problem sets (a widely used set of Operations Research benchmark problems and a set of randomly generated, hard problems) a texture-based heuristic outperforms heuristic commitment techniques found in the literature.

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