Authors:
K. Youssefi
;
M. Gojkovic
and
M. Schranz
Affiliation:
Lakeside Labs GmbH, Klagenfurt, Austria
Keyword(s):
Swarm Intelligence, Bio-Inspired Algorithm, Bee Algorithm, Job-Shop Scheduling, Agent-Based Modeling.
Abstract:
The optimization of a job-shop scheduling problem, e.g., in the semiconductor industry, is an NP-hard problem. Various research work have shown us that agent-based modeling of such a production plant allows to efficiently plan tasks, maximize productivity (utilization and tardiness) and thus, minimize production delays. The optimization from the bottom-up especially overcomes computational barriers associated with traditional, typically centrally calculated optimization methods. Specifically, we consider a dynamic semiconductor production plant where we model machines and products as agents and propose two variants of the artificial bee colony algorithm for scheduling from the bottom-up. Variant (1) prioritizes decentralization and batch processing to boost production speed, while Variant (2) aims to predict production times to minimize queue delays. Both algorithmic variants are evaluated in the framework SwarmFabSim, designed in NetLogo, focusing on the job-shop scheduling problem
in the semiconductor industry. With the evaluation we analyze the effectiveness of the bottom-up algorithms, which rely on low-effort local calculations.
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