Route‐Independent Analysis of Available Capacity in Flexible Manufacturing Systems

In a job shop, because of large setup times, each operation is assigned to only one machine. There is no alternative routing. In a flexible manufacturing system, each manufacturing operation can often be performed on several machines. Therefore, with automated equipment, the capacity of a machine to perform certain operations is not independent of the capacity of other machines. Often, however, operations managers can use a route‐independent answer to production planning questions. For example, how much can be produced of a certain part type and when are important capacity questions in business negotiations, when the detailed routing and scheduling are not yet of interest or cannot be known. This paper provides a mathematical model for the route‐independent analysis of the capacity of flexible manufacturing systems based on a concept of operation types. An example is provided both to illustrate the use of operation types and to highlight the differences between the traditional route‐dependent and the proposed route‐independent formulations of capacity constraints. Some computational results are also given. Finally, a sensitivity analysis is developed to analyze the feasibility of production plans when production requirements and machine capacities can change.     

Economic Production Lot Sizing with Periodic Costs and Overtime*

Traditional approaches for modeling economic production lot‐sizing problems assume that a single, fixed equipment setup cost is incurred each time a product is run, regardless of the quantity manufactured. This permits multiple days of production from one production setup. In this paper, we extend the model to consider additional fixed charges, such as cleanup or inspection costs, that are associated with each time period's production. This manufacturing cost structure is common in the food, chemical, and pharmaceutical industries, where process equipment must be sanitized between item changeovers and at the end of each day's production. We propose two mathematical problem formulations and optimization algorithms. The models' unique features include regular time production constraints, a fixed charge for each time period's production, and the availability of overtime production capacity. Experimental results indicate the conditions under which our algorithms' performance is superior to traditional approaches. We also test the procedures on a set of lot‐sizing problems facing a national food processor and document their potential economic benefit.     

A Multitude of Voices: Pushcart Prize Winning Publications 1976–2006

The Pushcart Prize, established in 1976, has a well-deserved reputation for highlighting the best in small press publication. The authors examined the first thirty volumes, 1976/1977 through 2006, to identify attributes of the items included in each volume and placed the volumes into five time periods of six volumes each to facilitate trend analysis. In order to identify the most productive publications, titles that had fewer than four selections in the thirty volumes and did not appear in at least two time periods were eliminated. The authors examined: press status as independent or affiliated, state and region where published, and type of work (poetry or other). Finally, highly productive titles were reviewed in WorldCat to determine how frequently these were held in the United States.California, Massachusetts, New York, and Ohio have a continuing, substantial presence in the Prize volumes. Most of the publications included were still active and were affiliated with a larger institution. The three small press titles appearing most frequently were Ploughshares, Paris Review, and American Poetry Review. The Pushcart Prize selections most frequently listed in WorldCat were the Hudson Review, the Paris Review, and the American Poetry Review. Each is held by more than eight hundred U.S. libraries.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

The effects of child support guidelines: An analysis of the evidence in court records

Court records from four Ohio counties were used to compare child support orders before and after promulgation of federally mandated guidelines in 1987. There was no significant change in probability of Court Ordered Child Support. Amount Ordered in constant dollars increased between 1985 and 1987–88. Variables influencing Court Ordered Child Support differed post-guidelines. Years Married, Attorney for Both and Attorney for Wife were significant in both years. Number of Children, Rural, and Wife's Motions were significant in 1987–88. Variables affecting amount ordered also differed after adoption of the guidelines. Wife Contested, Husband's Motions, Defendant and Plaintiff no longer were significant in 1987–88. Years Married and Payment by Father were significantly related to Amount Ordered in 1985 but not in 1987–88. Number of Children, Expenses Paid, and Number of Assets were significantly related to Amount Ordered in both years.Salaries and research support were provided by state and federal appropriations to the Ohio Agricultural Research and Development Center, and by the Department of Family Resource Management, and The Office of Research and Graduate Studies, The Ohio State University.Kathryn Stafford is Associate Professor, Department of Family Resource Management, 1787 Neil Avenue, Columbus, OH 43210-1295. Her research interests include household management and time use. She received her Ph.D. from Cornell University.Golden Jackson is Assistant Professor, Department of Family Resource Management, 1787 Neil Avenue, Columbus, OH 43210-1295. Her research is primarily in the area of the economic effects of divorce. She received her Ph.D. from The Ohio State University.Sharon Seiling is Assistant Professor and Extension Specialist in Family Resource Management, Department of Family Resource Management, 1787 Neil Avenue, Columbus, OH 43210-1295. Her research is on the impact of family change, especially divorce, on access to housing and overall financial well-being. She received her Ph.D. from Cornell University.     

Approximate optimal allocation in repeated sampling from a finite population

Samples of size n are drawn from a finite population on each of two occasions. On the first occasion a variate x is measured, and on the second a variate y. In estimating the population mean of y, the variance of the best linear unbiased combination of means for matched and unmatched samples is itself minimized, with respect to the sampling design on the second occasion, by a certain degree of matching. This optimal allocation depends on the population correlation coefficient, which previous authors have assumed known. We estimate the correlation from an initial matched sample, then an approximately optimal allocation is completed and an estimator formed which, under a bivariate normal superpopulation model, has model expected mean square error equal, apart from an error of order n^-2, to the minimum enjoyed by any linear, unbiased estimator.