Simultaneous determination of production lot size and process parameters under process deterioration and process breakdown

Jeang, Angus

doi:10.1016/j.omega.2011.12.005

Cited by 42 publications

(24 citation statements)

References 49 publications

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“…Let X denote the screening characteristic and Y denote the product characteristic. Hence, the expected total product cost, including the quality loss of conforming products and the rework cost of non-conforming products, is (8) where k is the quality loss coefficient; m0 is the target value of product characteristic; A is the scrap cost of product; δ1 is the lower screening limit coefficient, δ1>0; δ2 is the upper screening limit coefficient, δ2>0; ; …”

Section: Modified Tsai's [19] Modelmentioning

confidence: 99%

“…Taguchi's [5] quadratic quality loss function can connect on-line and off-line quality control methods and can be applied to process control and quality design processes. Many studies have adopted this quality loss function to determine the economic parameters of control chart, variable sampling inspection plans and specification limit settings, e.g., Darwish [6], Jeang [7,8,9], Jeang and Lin [10], Darwish and Duffuaa [11], Darwish and Abdulmalek [12], Duffuaa and El-Ga'aly [13,14], Darwish et al [15], and Chen et al [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Economic Design of Process Mean, Standard Deviation and Screening Limits Based on Burr Distribution

Chen

2017

AUSMT

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Abstract:The traditional quality screening problem considers the correlated and normal quality characteristics between product and screening characteristics. However, non-normal characteristics may exist in many practical production and inspection processes. In this paper, the author applies the bivariate Burr's density function in the quality level setting and screening problem to obtain the optimal process mean, standard deviation, and screening limits based on the minimization of the expected total product cost including quality loss for conforming products and rework costs for non-conforming products. A numerical example is given for illustration and a sensitivity analysis is conducted to assess the effects of model parameters on the optimal problem solution.

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Section: Modified Tsai's [19] Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic Design of Process Mean, Standard Deviation and Screening Limits Based on Burr Distribution

Chen

2017

AUSMT

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“…There are also studies in the literature that consider the lot sizing problem with production rate decisions [41] or with quadratic quality loss functions [12]. However, they consider the problem under an infinite horizon assumption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…and replace γ t ðk t Þ with κ t r t in the objective function (12). As b 4 0, we can rewrite (13) as k a t r r b t ; t ¼ 1; …; T: Therefore, we could reformulate the problem as follows:…”

Section: Reformulationsmentioning

confidence: 99%

Stochastic lot sizing problem with controllable processing times

Koca

Yaman

Aktürk

2015

Omega

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a b s t r a c tIn this study, we consider the stochastic capacitated lot sizing problem with controllable processing times where processing times can be reduced in return for extra compression cost. We assume that the compression cost function is a convex function as it may reflect increasing marginal costs of larger reductions and may be more appropriate when the resource life, energy consumption or carbon emission are taken into consideration. We consider this problem under static uncertainty strategy and α service level constraints. We first introduce a nonlinear mixed integer programming formulation of the problem, and use the recent advances in second order cone programming to strengthen it and then solve by a commercial solver. Our computational experiments show that taking the processing times as constant may lead to more costly production plans, and the value of controllable processing times becomes more evident for a stochastic environment with a limited capacity. Moreover, we observe that controllable processing times increase the solution flexibility and provide a better solution in most of the problem instances, although the largest improvements are obtained when setup costs are high and the system has medium sized capacities.

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“…As a result, future failures of the system will be prevented, leading to greater customer satisfaction. Several authors have extended the HPP policy in order to consider practical aspects such as multi-state machines, random demand, preventive maintenance, quality problems, and simultaneous breakdown and quality failures [11,12,13,14,15,16].…”

Section: Bureau Of Labor Statistics (Bls) Classifies Pharmaceutical Cmentioning

confidence: 99%

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

Assid

Gharbi

Dhouib

2015

Omega

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This article addresses the problem of joint optimization of production and subcontracting of unreliable production systems. The production system considered presents a common problem in the pharmaceutical industry. It is composed of multiple production facilities with different capacities, each of which is capable of producing two different classes of medications (brand name and generic). The resort to subcontracting is double: first, it involves the quantity of products received on a regular basis in order to compensate for insufficient production capacity in existing facilities, second, when needed, urgent orders are also launched in order to reduce the risk of shortages caused by breakdowns of manufacturing facilities. Failure, repair and urgent delivery times may be represented by any probability distributions.The objective is to propose a general control policy for the system under consideration, and to obtain, in the case of two facilities, optimal control parameters that minimize the total incurred cost for a specific level of the customer service provided. Given the complexity of the problem considered, an experimental optimization approach is chosen in order to determine the optimal control parameters. This approach includes experimental design, analysis of variance, response surface methodology and simulation modeling. It allows the accurate representation of the dynamic and stochastic behaviours of the production system and the assessment of optimal control parameters. Other control parameters which represent the subcontracting are introduced and three joint production / subcontracting control policies (general, urgent, regular) This policy offers not only cost savings, but is also easier to manage, as compared to that proposed by Dror et al. [1]. Numerical examples and a sensitivity analysis are also performed to illustrate the robustness of the proposed control policy and the solution approach.

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Simultaneous determination of production lot size and process parameters under process deterioration and process breakdown

Cited by 42 publications

References 49 publications

Economic Design of Process Mean, Standard Deviation and Screening Limits Based on Burr Distribution

Economic Design of Process Mean, Standard Deviation and Screening Limits Based on Burr Distribution

Stochastic lot sizing problem with controllable processing times

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

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