Developing a New Integrated Bi-Objective Model for Buffer and Process Time Optimization Problem using Optimization via Simulation Approach

The cost of semi-finished products storage and processing time are two issues affecting the rate of manufacturing in all production systems. The acceleration in production time is usually associated with the increase of costs and lack of appropriate allocation of the production line buffers imposing...

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Veröffentlicht in:Mathematical models and computer simulations Jg. 10; H. 3; S. 373 - 386
Hauptverfasser: Azimi, P., Farhadi, N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.05.2018
Springer Nature B.V
Schlagworte:
Buffers
Computer simulation
Design of experiments
Genetic algorithms
Industrial engineering
Manufacturing engineering
Mathematical Modeling and Industrial Mathematics
Mathematical programming
Mathematics
Mathematics and Statistics
Multiple objective analysis
Optimization
Production costs
Regression models
Simulation
Simulation and Modeling
optimal processing time
optimization via simulation
buffer optimization problem
NSGA_II
ISSN:2070-0482, 2070-0490
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Zusammenfassung:The cost of semi-finished products storage and processing time are two issues affecting the rate of manufacturing in all production systems. The acceleration in production time is usually associated with the increase of costs and lack of appropriate allocation of the production line buffers imposing production costs and increase in delivery time and decrease in productivity. Many previous studies have focused on deterministic production systems using mathematical programming models. Moreover, the optimization of buffers and processing times with stochastic events has not been considered simultaneously. In this study, a new simulation-based optimization model has been proposed to address the modeling and solving of buffer and processing time optimization problem in a stochastic environment. The real world problem has been modeled by using the simulation technique and the model replicated by a design of experiment method. The results were used to build a meta-model of regression for the objective functions and finally, a new mathematical model was solved by a multiobjective Genetic Algorithm. The results of this research show that the proposed approach has efficient solutions and could be easily applied to real world problems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2070-0482
2070-0490
DOI:10.1134/S207004821803002X