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Determining optimum sample sizes with respect to cost for multiple acceptance quality characteristics.

机译:确定针对多个接受质量特征的最佳样本量。

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摘要

There has been little and limited research done to determine the optimum sample size n for highway construction acceptance plans containing pay adjustment provisions. To optimize the sample size of Acceptance Quality Characteristics (AQCs) with respect to the cost to the agency, an understanding is required of costs of testing and of the cost consequences associated with making a wrong acceptance decision. This study developed an optimization model to determine the sample size. Actual state agency's data were used not only to check the validity of model's assumptions but also draw the conclusions. Among the key conclusions were: (1) The optimum sample size is generally small (n=3) for Percent Within Limits acceptance plans; (2) The degree of correlation among AQCs has a negligible effect on the optimum sample size; and (3) The highway agency tends to underpay contractors through use of its pay adjustment provisions. Some cautions are presented for state highway agencies planning to use small sample sizes. Agencies are encouraged to perform their own optimization calculations. By doing so, they will gain a better understanding of their acceptance plan systems and associated costs, and have greater confidence in applying economic decision analysis principles to minimize expected costs and optimize statistical acceptance risks.
机译:为确定包含薪酬调整条款的公路建设验收计划确定最佳样本量n的研究很少,而且研究也很有限。为了相对于代理商的成本来优化验收质量特征(AQC)的样本量,需要了解测试成本以及与做出错误验收决策相关的成本后果。这项研究开发了一种优化模型来确定样本量。国家机构的实际数据不仅用于检验模型假设的有效性,而且可以得出结论。关键结论包括:(1)百分比内限接受计划的最佳样本量通常很小(n = 3); (2)AQC之间的相关程度对最佳样本量的影响可忽略不计; (3)公路代理机构倾向于通过使用其薪酬调整条款来少付承包商费用。对于计划使用小样本量的州高速公路机构,提出了一些警告。鼓励代理商执行自己的优化计算。这样,他们将更好地了解验收计划系统和相关成本,并且对应用经济决策分析原则以最大程度地降低预期成本和优化统计验收风险具有更大的信心。

著录项

  • 作者

    Cho, Dooyong.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 95 p.
  • 总页数 95
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:36:59

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