Optimization of Failure-Prone Continuous-Flow Transfer Lines with Delays and Echelon Base Stock Policy using IPA

Mourani, Iyad; Hennequin, Sophie; Xie, Xiaolan

doi:10.1109/cdc.2006.377659

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abou-Kandkil et al 19 discussed the problem of computing optimal production rates for a failure-prone manufacturing system with multi-machine and multi-product. Mourani et al 20 explored the optimization of failure-prone transfer lines with important delays for material transfer, constant demand and echelon base stock policy for production control. Kenne and Gharbi 21 presented the optimal flow control method for a one-machine, two-product manufacturing system subject to random failures and repairs based on the combination of the analytical model, simulation experiments, experimental design and response surface methodology.…”

Section: Introductionmentioning

confidence: 99%

Hedging-point control policy for a failure-prone manufacturing system

Yan

Jiang

Meng

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

The production control of failure-prone manufacturing systems is notoriously difficult because such systems are uncertain and non-linear. Since the introduction of hedging-point policies, many researches have been done in this field. However, there are few literatures that consider the production control problem of tree-structured manufacturing systems. In this article, a hedging-point production control policy is proposed for a multi-machine, tree-structured failure-prone manufacturing system. To obtain the optimal hedging points, an iterative learning algorithm is developed by considering the system’s characteristics. A simulation method is embedded in the iterative learning algorithm to calculate the system cost. To estimate the performance of the proposed algorithm, comparisons are made between our algorithm, genetic algorithm and particle swarm optimization algorithm. The experimental results show that our algorithm works better than others in reducing the computation time and minimizing the production cost.

show abstract

Section: Introductionmentioning

confidence: 99%

Hedging-point control policy for a failure-prone manufacturing system

Yan

Jiang

Meng

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…(see also Glasserman, 1990). IPA technique has been used successfully in several works, like (Yu & Cassandras 2004;Mourani, et al, 2006, Yu & Cassandras 2006Melamed, et al, 2007;Turki, et al, 2009;Cassandras, et al, 2010).…”

Section: Introductionmentioning

confidence: 99%