Application of genetic algorithms and visual simulation in a real-case production optimization

Authors:
Davorin Kofjač;Miroljub Kljajic
Affiliations:
University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia;University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia
Venue:
WSEAS Transactions on Systems and Control
Year:
2008

Citing 5
Cited 1

A genetic algorithm for flowshop sequencing

Computers and Operations Research - Special issue on genetic algorithms
Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
Applying the clonal selection principle to find flexible job-shop schedules

ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Parameter control in evolutionary algorithms

IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution

IEEE Transactions on Evolutionary Computation

Using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning

SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a real case customized flexible furniture production optimization. Such a make-to-order production must be flexible to meet the customer's needs, which are changing frequently. Hence, a frequent review of the production process is needed to ensure near-optimal production schedule to meet the minimal makespan constraint. In such a case we are confronted with a tradeoff between makespan and optimization runtime to ensure efficient real-time scheduling. The genetic algorithm production scheduling optimization is presented to solve a job shop scheduling problem with recirculation. Several initial population generators, selection methods, and crossover and mutation methods are discussed and tested. The visual model of the furniture production process was developed during the research. Such a model is closer to end-user perception and is used to clarify the results of numerical optimization. It also enables the implementation of end-user's expert knowledge into the optimizer to reduce the GA search space with a goal of reducing the runtime to a minimum.