Optimization of quality costs

Oppermann, Martin; Sauer, W.; Wohlrabe, Heinz

doi:10.1016/s0736-5845(02)00070-4

Cited by 16 publications

(6 citation statements)

References 1 publication

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“…This framework was also employed in a building project that focuses on the impact of failures (Hall and Tomkins, 2001). Considering only inspection (i.e., appraisal) strategies, Oppermann et al (2003) used dynamic programming to determine an inspection strategy for minimizing quality costs. There also exist some studies concerning the optimization of quality costs for construction project management endeavors (Rahimi and Iranmanesh, 2008;Ning and Wang, 2009), where the trade-off between cost and quality is modeled as a multi-objective optimization problem, which is then solved heuristically.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost-of-Quality Optimization via Zero-One Polynomial Programming

Lim¹,

Sherali

Glickman

2014

IIE Transactions

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This article considers a Cost-of-Quality (CoQ) optimization problem that finds an optimal allocation of prevention and inspection resources to minimize the expected total quality costs under a prevention-appraisal-failure framework, where the quality costs in the proposed model are involved with prevention, inspection, and correction of internal and external failures. Commencing with a simple structure of the problem, the complexity of the problem is progressively increased by accommodating realistic scenarios regarding preventive, appraisal, and corrective actions. The resulting problem is formulated as a zero-one polynomial program, which can be solved either directly using a mixed-integer nonlinear programming solver such as BARON or using a more conventional Mixed-Integer Linear Programming (MILP) solver such as CPLEX after performing an appropriate linearization step. Two case studies from the literature (related to a lamp manufacturing context and an order entry process) are examined to illustrate how the proposed model can be utilized to find optimal inspection and prevention strategies, as well as to analyze sensitivity with respect to different cost parameters. A comparative numerical study of using the aforementioned solvers to optimize the respective model formulations is provided. The results provide insights into the use of such quantitative methods for optimizing the CoQ and indicate the efficacy of using the linearized MILP model for this purpose.

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Section: Literature Reviewmentioning

confidence: 99%

Cost-of-Quality Optimization via Zero-One Polynomial Programming

Lim¹,

Sherali

Glickman

2014

IIE Transactions

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“…Methods to solve the inspection allocation problem could be divided into two categories: exact and heuristic methods. Among exact methods, dynamic programming (Oppermann et al, 2003), non-linear programming and integer programming techniques can be mentioned. Using these techniques is very computationally expensive.…”

Section: Literature Review and Problem Formulationmentioning

confidence: 99%

A genetic algorithm with tournament selection for optimising inspection allocation in multiproduct multistage production systems

Korytkowski

2011

IJSPM

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Multiproduct multistage production systems have been widely implemented in recent years. One of the important considerations in such systems is identifying the location of inspection stations. This paper considers a multiproduct setting where part types compete with each other for common production resources. In this environment, it is important to consider factors such as throughput time variability and to include the corresponding queuing aspects into the model. Each workstation is modelled as a GI/G/c queue. The optimal allocation is determined by using a genetic algorithm with tournament selection, one-point crossover and uniform mutation.

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“…Some authors (e.g. Oppermann et al, 2003;Abdul-Kader et al, 2010;Lim C, 2015;Lim T. K et al, 2015) gave proposals for quality cost optimization in different process environments.…”

Section: Introductionmentioning

confidence: 99%

A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

Glogovac

Filipović

Zivkovic

et al. 2019

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In this paper, the PAF (preventive-appraisal-failure) quality costs model is used on the process level, in order to identify those processes which are critical as a top-priority for improvement. The elements of quality costs are considered in relation to the outputs of the processes, where the principle that one process affects the quality of another process by its outputs is taken into account. That is why the failure costs of the affected processes should also be assigned to the quality costs of the observed process. Using the factors proposed in the Process cost model, costs are identified and calculated for each process for two moments: the current (before improvements) and desired (after improvements). Improvements are implemented on the basis of the causes of nonconformity analysis and according to the PAF model assumption that preventive and appraisal activities reduce failure costs and the total costs of quality. The difference between the total costs of quality in two observed moments is seen as the greatest possible quality cost reduction for the observed process. This value is used as a criterion for ranking the processes according to their priority for improvements, taking into account the assumption of the PAF model that greater costs mean lower quality and hence a higher priority for improvement.

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Optimization of quality costs

Cited by 16 publications

References 1 publication

Cost-of-Quality Optimization via Zero-One Polynomial Programming

Cost-of-Quality Optimization via Zero-One Polynomial Programming

A genetic algorithm with tournament selection for optimising inspection allocation in multiproduct multistage production systems

A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

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