On a volume flexible inventory model for items with an imperfect production system
01 Jan 2007-International Journal of Operational Research (Inderscience Publishers)-Vol. 2, Iss: 1, pp 64-80
TL;DR: A mathematical model is developed for volume flexible production system taking into account the effect of imperfect quality products and demand rate of some defective items which are suitable for sale at reduced price is considered as a function of reduction rate of selling price.
Abstract: A mathematical model is developed for volume flexible production system taking into account the effect of imperfect quality products. At the start of the production of a lot, the whole system is adjusted so that the process is 'in-control' state and items produced are of acceptable quality. During high rate of production, the process shifts from the 'in-control' state to the 'out-of-control 'state. In the 'out-of-control' state, the quality of products deteriorates resulting in defective items. The demand rate of some defective items which are suitable for sale at reduced price is considered as a function of reduction rate of selling price. The proposed model is graphically illustrated with numerical examples and optimised by Mathematica Software. Sensitivity analysis is undertaken and various issues regarding the sale of imperfect quality products are discussed.
Citations
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TL;DR: An EPQ type inventory model with planned backorders for determining the economic production quantity for a single product, which is manufactured in a single-stage manufacturing system that generates imperfect quality products, and all these defective products are reworked in the same cycle is developed.
229 citations
TL;DR: An EPL (Economic Production Lotsize) model in an imperfect production system in which the production facility may shift from an 'in- control' state to an 'out-of-control' state at any random time is investigated.
200 citations
TL;DR: A model to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process is developed and the Euler-Lagrange method is used to obtain optimal solutions for product reliability parameter and dynamic production rate.
182 citations
TL;DR: In this article, the optimal buffer inventory for stochastic demand in the market during preventive maintenance or repair of a manufacturing facility with an economic production quantity model in an imperfect production system was developed.
93 citations
TL;DR: This paper is concerned with the joint determination of optimal production lot size, safety stock and reliability parameter under the realistic assumptions that the production facility is subject to random breakdown of machinery system and also to change in the variable reliability parameter.
Abstract: This paper is concerned with the joint determination of optimal production lot size, safety stock and reliability parameter under the realistic assumptions that the production facility is subject to random breakdown of machinery system and also to change in the variable reliability parameter. Reliability of a machinery system is a decision variable that can be increased by more investment in production technology. In this model, preventive and corrective maintenance, safety stock for repair times and shortages are adopted to generalise the model. Except machinery breakdown, the manufacturing system may shift from in-control state to out-of-control state. During out-of-control state, a certain percentage of total production consists of defective items which can be reworked immediately at a cost to make as good as perfect quality items. The cost function is maximised by Khun-Tucker method. Numerical results are provided to illustrate both the study of the optimal solutions and sensitivity of different changes in key parameters.
93 citations
References
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TL;DR: It is demonstrated that lower setup costs can benefit production systems by improving quality control by introducing a simple model that captures a significant relationship between quality and lot size.
Abstract: This paper seeks to demonstrate that lower setup costs can benefit production systems by improving quality control. It does so by introducing a simple model that captures a significant relationship between quality and lot size: while producing a lot, the process can go "out of control" with a given probability each time it produces another item. Once out of control, the process produces defective units throughout its production of the current lot. The system incurs an extra cost for rework and related operations for each defective piece that it produces. Thus, there is an incentive to produce smaller lots, and have a smaller fraction of defective units. The paper also introduces three options for investing in quality improvements: i reducing the probability that the process moves out of control which yields fewer defects, larger lot sizes, fewer setups, and larger holding costs; ii reducing setup costs which yields smaller lot sizes, lower holding costs, and fewer defects; and iii simultaneously using the two previous options. By assuming a specific form of the investment cost function for each option, we explicitly obtain the optimal investment strategy. We also briefly discuss the sensitivity of these solutions to changes in underlying parameter values. A numerical example illustrates the results.
1,133 citations
TL;DR: In this paper, the authors study the effects of an imperfect production process on the optimal production cycle time and derive the optimal cycle time for the case where the defective rate is a function of the set-up cost.
Abstract: In this paper, we study the effects of an imperfect production process on the optimal production cycle time. The system is assumed to deteriorate during the production process and produce some proportion of defective items. The optimal production cycle is derived, and is shown to be shorter than that of the classical Economic Manufacturing Quantity model. The analysis is extended to the case where the defective rate is a function of the set-up cost, for which the set-up cost level and the production cycle time are jointly optimized. Finally, we also consider the case where the deterioration process is dynamic in its nature, i.e., the proportion of defective items is not constant. Both linear, exponential, and multi-state deteriorating processes are studied. Numerical examples are provided to illustrate the derivation of the optimal production cycle time in these situations.
1,021 citations
Book•
01 Mar 1996
TL;DR: This title is a substantial revision of one of the leading textbooks designed for the statistical quality control course taught in departments of industrial engineering, operations research and statistics and has incorporated key organizational changes in order to reflect recent trends in the field.
Abstract: This title is a substantial revision of one of the leading textbooks designed for the statistical quality control course taught in departments of industrial engineering, operations research and statistics . While maintaining its already successful writing style and pedagogy, this title has also incorporated key organizational changes in order to reflect recent trends in the field. The text features large quantity of examples and student problems and a strong introduction to the proper use and misuse of control charts. In this edition several chapters were streamlined, and consolidations and profitability were brought forward in the text. There is new material on experimental design, a reduced emphasis on acceptance sampling, and enhanced attention to the managerial and organizational aspects of quality control. Free SPC expert software is packaged with the text for use as a statistical and graphical tool. Text plus 3.5 diskette.
995 citations
TL;DR: In this article, the authors extended the traditional economic production quantity model by accounting for imperfect quality items when using the EPQ/EOQ formulae, and considered the issue that poor-quality items are sold as a single batch by the end of the 100% screening process.
943 citations