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Online scheduling on multiple resources under stochastic conditions

Thomas M., Szczerbicka H.

Systems Science

2007

Vol. 33, no 2

23-30

Online scheduling (also known as dynamic scheduling) refers to a real-time coordination of operation processing while new operations are continuously arriving. Despite the attention this area received over last years, little effort has been spent on cases where multiple resources are required for an operation processed. The objective of this paper is the study of different online scheduling techniques specifically in a dynamic, stochastic job shop scheduling with multiple resources. We adopt algorithms for dispatching and optimizing schedulers to this case and provide a quantitative study of their performance under varying stochastic conditions. We also discuss the regret-based algorithm from Bent and Van Hentenryck. While this approach is superior in the case of scheduling single resource, it showed inferior performance in our experiments compared to its underlying optimizing scheduler.

Optimisation of buffer size in manufacturing systems using ant algorithms

Becker M., Szczerbicka H.

Foundations of Control and Management Sciences

2005

No. 3

9-23

In this article we use the Ant Colony Optimisation (AGO) algorithm in order to find optimal Kanban allocations in Kanban systems represented by Stochastic Petri Net (SPN) models. Like other optimisation algorithms inspired by nature, such as Simulated Annealing/Genetic Algorithms, the AGO algorithm contains a large number of adjustable parameters. Thus we study the influence of the parameters on performance of AGO on the Kanban allocation problem, and identify the most important parameters.