Authors:
M. Umlauft
;
M. Gojkovic
;
K. Harshina
and
M. Schranz
Affiliation:
Lakeside Labs GmbH, Klagenfurt, Austria
Keyword(s):
Swarm Intelligence, Bio-Inspired Algorithm, Bee Algorithm, Bat Algorithm, Flexible Job-Shop Scheduling, Agent-Based Modeling.
Abstract:
Scheduling in a production plant with a high product diversity is an NP-hard problem. In large plants, traditional optimization methods reach their limits in terms of computational time. In this paper, we use inspiration from two bio-inspired optimization algorithms, namely, the artificial bee colony (ABC) algorithm and the bat algorithm and apply them to the job shop scheduling problem. Unlike previous work using these algorithms for global optimization, we do not apply them to solutions in the solution space, though, but rather choose a bottom-up approach and apply them as literal swarm intelligence algorithms. We use the example of a semiconductor production plant and map the bees and bats to actual entities in the plant (lots, machines) using agent-based modeling using the NetLogo simulation platform. These agents then interact with each other and the environment using local rules from which the global behavior – the optimization of the industrial plant – emerges. We measure perf
ormance in comparison to a baseline algorithm using an engineered heuristics (FIFO, fill fullest batches first). Our results show that these types of algorithms, employed in a bottom-up manner, show promise of performance improvements using only low-effort local calculations.
(More)