Joint production and setup control policies: an extensive study addressing implementation issues via quantitative and qualitative criteria

• Published in: International journal of advanced manufacturing technology 2014-05, Vol.72 (5-8), p.809-826
• Main Authors: Assid, M., Gharbi, A., Hajji, A.
• Format: Article
• Language: English
• Subjects: CAE) and Design; Comparative studies; Complexity; Computer simulation; Computer-Aided Engineering (CAD; Control systems; Criteria; Customer satisfaction; Design of experiments; Design optimization; Engineering; Industrial and Production Engineering; Mechanical Engineering; Media Management; Optimal control; Original Article; Parameters; Policies; Response surface methodology; Simulation; Variance analysis
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-014-5721-y

This paper addresses the production and setup control problem in unreliable multiproducts manufacturing system. Several decision criteria are considered in order to conduct an exhaustive comparative study of the two most complete control policies in the literature. The objective is to propose a production and setup control policy for the system under review. The first part of this work consists in analyzing the effect of the system parameters variation on the difference between the total costs of the two control policies studied. The best control policy in terms of cost will then be determined using a numerical example in the case of two identical product types and without the loss of generality of the problem. In the second part, two key performance indicators (KPIs): the cost and the customer satisfaction rate are simultaneously considered. The goal is to optimize the parameters of the policies studied, which minimize the total cost incurred while respecting customer satisfaction constraint. A discussion on the best control policy is conducted based on cost and customer satisfaction. An experimental resolution approach is used. It integrates combined discrete-continuous simulation models with statistical techniques of optimization such as design of experiments, analysis of variance, and response surface methodology. Finally, a discussion is conducted on the effects of other quantitative and qualitative criteria in order to determine the best control policy and to reach the best concerns of the company’s decision makers. These decision criteria are generally related to the storage space required constraint, the setups complexity, the implementation issue, and the complexity of the optimal control problem.