Perturbation analysis for continuous and discrete flow models: a study of the delivery time impact on the optimal buffer level

Turki, Sadok; Hennequin, Sophie; Sauer, Nathalie

doi:10.1080/00207543.2013.765996

Cited by 32 publications

(11 citation statements)

References 16 publications

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“…The proof of this theorem is similar to the proof of theorem 1 and theorem 2 in [13]. 4 Theorem 2 shows that the remanufacturing inventory level of the perturbed path is equal to nominal path minus the lag Ijf(t) when t E ] t pi' t;, [ and the nominal path and of the perturbed path are equal whentE [t;"t P (i+I J ] .…”

Section: Ill Perturbation Analysismentioning

confidence: 72%

Optimization of stochastic fluid model using perturbation analysis: A manufacturingremanufacturing system with stochastic demand and stochastic returned products

Turki

Bistorin

Rezg

2014

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control

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In this paper, a stochastic fluid model is used to study a manufacturing/ remanufacturing system composed by two parallel machines, a serviceable inventory, a remanufacturing inventory and customers who demand a stochastic quantity of product. Stochastic fluid model is adopted to describe the system and to take into account machine failure, stochastic demand, stochastic returned products and remanufacturing products. The goal of this paper is to evaluate the optimal serviceable inventory level which allows minimizing the sum of inventory and lost sales costs. Perturbation analysis is applied to the stochastic fluid model to optimize the considered system. The trajectories of the serviceable inventory level are studied and the perturbation analysis estimates are evaluated. The unbiasedness of these estimates is proved and then they are implemented in an optimization algorithm which determines the optimal serviceable inventory in the presence of stochastic returned products and stochastic demand.

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Section: Ill Perturbation Analysismentioning

confidence: 72%

Optimization of stochastic fluid model using perturbation analysis: A manufacturingremanufacturing system with stochastic demand and stochastic returned products

Turki

Bistorin

Rezg

2014

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control

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“…The planned delivery time τ pl is equal to 2, the delivery time τ(t) is generated according to a truncated normal distribution, the normal distribution gives same value out the interval [1,3], the truncated normal distribution gives bounded value between 1 and 3.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Stochastic fluid model [1][2][3][4] is widely used to control, analyze, and improve the performance of manufacturing supply chain systems. This model has been long adopted as a modeling technique of queuing theory, for analysis and synthesis of discrete event systems [5,6].…”

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confidence: 99%

Optimization of Manufacturing Supply Chain with Stochastic Demand and Planned Delivery Time

Turki¹,

Bistorin²,

Rezg

2017

JTTE

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Abstract:In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into account stochastic delivery times. The objective of this paper is to evaluate the optimal buffer level used in hedging point policy taken into account planned delivery times, machine failures and random demands. This optimal buffer allows minimizing the sum of inventory, transportation, lost sales and late delivery costs. Infinitesimal perturbation analysis method is used for optimizing the proposed system. Using the stochastic fluid model, the trajectories of buffer level are studied and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm, which determines the optimal buffer level in the presence of planned delivery time. Also in this work, we discuss the advantage of the use of the infinitesimal perturbation analysis method comparing to classical simulation methods.

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“…The existing BM system mainly follows the practice established by Goldratt (1997) for production operations (Yuan, Chang, and Li 2003;Wu et al 2010;Turki, Hennequin, and Sauer 2013;Büchmann-Slorup 2014;Papadopoulos, O'Kelly, and Tsadiras 2013). That is, the buffer is equally divided into three regions and explicit action levels are set in terms of the buffer consumption (BC) (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective expediting to improve project due date and cost performance through buffer management

Cui

Demeulemeester

2014

International Journal of Production Research

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The rapidly changing marketplace together with the increasing complexity of contemporary projects makes it more likely that project activities will have uncertain durations, incurring a generally low probability of on-time delivery. Thus, project control that aims to track the project performance and to expedite relevant activities when necessary has become the main aspect within the scope of project management in order to ensure a successful scheduling outcome. The Critical Chain Scheduling and Buffer Management (CC/BM) has shown to provide a popular approach to build robust project schedules and to offer a valuable control tool for coping with schedule variability. However, the most current buffer management (BM) practice faces a problem of neglecting the cost information when taking expediting actions. In view of this defect, we introduce a new control procedure on the basis of CC/BM that evaluates the probability of successful project completion relative to the cost of crashing and that determines when to expedite which activities in a cost-effective manner. Results of an experimental application of the proposed method present its relative dominance over the currently widely adopted BM approach with respect to project time and cost performance.

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Perturbation analysis for continuous and discrete flow models: a study of the delivery time impact on the optimal buffer level

Cited by 32 publications

References 16 publications

Optimization of stochastic fluid model using perturbation analysis: A manufacturingremanufacturing system with stochastic demand and stochastic returned products

Optimization of stochastic fluid model using perturbation analysis: A manufacturingremanufacturing system with stochastic demand and stochastic returned products

Optimization of Manufacturing Supply Chain with Stochastic Demand and Planned Delivery Time

Effective expediting to improve project due date and cost performance through buffer management

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