Optimal production control policy in unreliable batch processing manufacturing systems with transportation delay

Bouslah, Bassem; Gharbi, Ali; Pellerin, Robert; Hajji, Adnène

doi:10.1080/00207543.2012.676217

Cited by 19 publications

(7 citation statements)

References 35 publications

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“…ETC is convex in Q and Z. In fact, the sum of the inventory, backlog and transportation costs is a convex function in Q and Z as shown in Bouslah et al (2012), while 100% inspection, rectification and replacement costs are linear with respect to Q. In addition, when the sampling cost is assumed to be linear or strictly convex function in the sample size n, the existence of a global optimum sampling plan (n * , c * ) which minimizes the sum of all quality related costs was proved by Moskowitz and Berry (1976) and Moskowitz et al (1979).…”

Section: Optimization Problem Formulationmentioning

confidence: 99%

“…For unreliable batch manufacturing systems with delays which cannot be considered as continuous-flow systems, Bouslah et al (2012) showed that the optimal feedback control policy can be closely approximated by a base-stock policy expressed by a modified HPP. When the batches produced need to be transported for a non-negligible delay to the serviceable stock, the authors assumed that the feedback inventory control is based on the concept of the inventory position which includes the on-hand inventory in the final stock and the total pending quantities in transportation as in Mourani et al (2008) and Li et al (2009).…”

Section: Production Control Policymentioning

confidence: 99%

“…Our choice to use the HPP for the production-inventory control is motivated by its flexibility, feedback and optimality properties ( (Bielecki and Kumar, 1988), (Bouslah et al, 2012)). The single sampling plan by attributes is the most commonly applied sampling procedure in industry because of its simplicity compared to double, multiple and sequential sampling (Wetherill and Chiu, 1975).…”

Section: Introductionmentioning

confidence: 99%

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Joint production and quality control of unreliable batch manufacturing systems with rectifying inspection

Bouslah

Gharbi

Pellerin

2013

International Journal of Production Research

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Production control policy and economic sampling plan design problems have been studied separately in previous research. This paper considers a joint production control policy and economic single sampling plan design for an unreliable batch manufacturing system. The production is controlled by a modified hedging point policy which consists in building and maintaining a safety stock of finished product to avoid shortages during corrective maintenance. The main objective of this paper is to determine simultaneously the economic production quantity, the optimal safety stock level and the economic sampling plan design which minimize the expected overall cost. A stochastic mathematical model is developed and solved using a simulation optimization approach based on the response surface methodology. Simulation is used to imitate the complex dynamic and stochastic behaviour of processes as in the real-life industrial systems. The obtained results show clearly strong interactions between production quantity, inventory state and sampling plan design which confirm the necessity of jointly considering production and quality control parameters in an integrated model. Moreover, it is shown a significant impact of production system reliability on the economic sampling plan design and therefore on the quality of finished product delivered to consumers. Numerical example and sensitivity analyses are presented for illustrative purposes.

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Section: Optimization Problem Formulationmentioning

confidence: 99%

Section: Production Control Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint production and quality control of unreliable batch manufacturing systems with rectifying inspection

Bouslah

Gharbi

Pellerin

2013

International Journal of Production Research

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“…Recently, the research shows some methods describing the process of parts manufacturing, which are the objectoriented method [1,2], the petri net-based modeling method [3][4][5], discrete event dynamic simulation method [6,7], and so forth. These methods put the emphasis on describing and definition of the elements in manufacturing process, while lacking description of the correlation among the elements.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Process Route Based on Weighted Network

Xiang

Chang

et al. 2015

Mathematical Problems in Engineering

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Aiming at the production stability of complex parts, a method based on weighted network was proposed to analyze the stability of the process routes of complex parts in order to improve the production stability. The weighted network of the process routes of complex parts was constructed by using the concept of machining cell which can transform the production cost and manufacturing time to the weights of network to do the decision-making of the process routes. Based on the production stability, the brittleness risk entropy of subsystem of weighted network was constructed by analyzing the probability of the brittle events that may lead to the collapse of the weighted network in machining cells. As the indicator of analyzing the vulnerability of weighted network node, the brittleness risk entropy can predict the easily failed subsystem in the entire network. Meanwhile, the brittle event, which may lead to the machining cell failure, was retrospect for the greater stability of the process routes. Finally, the correctness and effectiveness of this method were verified by using the manufacturing process of an aero-engine blade.

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“…For unreliable batch manufacturing systems, some authors, e.g., and Sana and Chaudhuri (2010), used an optimal safety stock in inventory to protect against possible stock-out during system repair and to enhance customer service levels. In a prior paper, Bouslah et al (2012) focused on the problem of simultaneously determining the optimal lot sizing and the optimal production control policy of unreliable batch manufacturing systems. Assuming that the system is perfect, they were capable of writing the HJB equations and solving them numerically.…”

Section: Heuristic Control Policymentioning

confidence: 99%

Joint optimal lot sizing and production control policy in an unreliable and imperfect manufacturing system

Bouslah

Gharbi

Pellerin

2013

International Journal of Production Economics

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This paper deals with the problem of the joint determination of the optimal lot sizing and optimal production control policy for an unreliable and imperfect manufacturing system, where the quality control of lots produced is performed using an acceptance sampling plan. The proportion of defective items, the time between failures and the time to repair are generally distributed. The incurred total cost includes manufacturing cost, transportation cost, inspection costs, rejection cost of defective items, replacement cost for returned defective items from customers, and holding and backlog costs. The associated cost minimization problem is formulated with a stochastic dynamic programming model where the lot sizing and production rate are considered as decision variables. Given the difficulties in solving such a highly stochastic model analytically or numerically, we adopted a modified hedging point policy (HPP) to control the production rate, as well as an economic lot sizing policy for batch processing control; we also relied on a simulation-based experimental approach to determine a close approximation of the optimal control parameters. It is shown that production should be accelerated at the maximum production rate, not only when building the safety stock, as in the classical HPP, but also after rejecting a lot, in order to recuperate the loss in inventory and to maintain the on-hand safety stock. Numerical experiments and thorough sensitivity analyses are provided to illustrate the effectiveness of the proposed control policy and the robustness of the resolution approach. Some interesting behaviours regarding the impact of different parameters on the optimal decision variables are observed and discussed.

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Optimal production control policy in unreliable batch processing manufacturing systems with transportation delay

Cited by 19 publications

References 35 publications

Joint production and quality control of unreliable batch manufacturing systems with rectifying inspection

Joint production and quality control of unreliable batch manufacturing systems with rectifying inspection

Stability Analysis of Process Route Based on Weighted Network

Joint optimal lot sizing and production control policy in an unreliable and imperfect manufacturing system

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