Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms

• Published in: International journal of production research Vol. 48; no. 10; pp. 2887 - 2912
• Main Authors: Koulouriotis, D.E., Xanthopoulos, A.S., Tourassis, V.D.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 15.05.2010; Taylor & Francis; Taylor & Francis LLC
• Subjects: Applied sciences; artificial intelligence; Assembly; Assembly lines; automated manufacturing systems; Computer science; control theory; systems; Computer simulation; computer vision; data mining; Data processing. List processing. Character string processing; decision support systems; Decision theory. Utility theory; evolutionary computation; Exact sciences and technology; Genetic algorithms; Inventory control, production control. Distribution; Just in time; Kanbans; knowledge engineering; Manufacturing; Mathematical models; Mathematical programming; Memory organisation. Data processing; Operational research and scientific management; Operational research. Management science; Optimization algorithms; Policies; Production control; Production controls; robotics; Serials; Simulation; Software; Studies; time series analysis; Knowledge engineering; time series analysis; Evolutionary algorithm; In-process inventory; Data mining; Robotics; automated manufacturing systems; Kanban; Base stock; Assembly; Discrete event system; Production system; Assembly line; Script; Computer vision; Data analysis; Lean production; Decision support system; Process control; decision support systems; Production management; assembly lines; evolutionary computation; Genetic algorithm; Just in time; Time-series analysis; Objective function; Artificial intelligence
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207540802603759

Several efficient pull production control policies for serial lines implementing the lean/JIT manufacturing philosophy can be found in the production management literature. A recent development that is less well-studied than the serial line case is the application of pull-type policies to assembly systems where manufacturing operations take place both sequentially and in parallel. Systems of this type contain assembly stations where two or more parts from lower hierarchical manufacturing stations merge in order to produce a single part of the subsequent stage. In this paper we extend the application of the Base Stock, Kanban, CONWIP, CONWIP/Kanban Hybrid and Extended Kanban production control policies to assembly systems that produce final products of a single type. Discrete-event simulation is utilised in order to evaluate the performance of serial lines and assembly systems. It is essential to determine the best control parameters for each policy when operating in the same environment. The approach that we propose and probe for the problem of control parameter selection is that of a genetic algorithm with resampling, a technique used for the optimisation of stochastic objective functions. Finally, we report our findings from numerical experiments conducted for two serial line simulation scenarios and two assembly system simulation scenarios.