Darwin J. Davis

Countering forgetting through training and deployment

Abstract Although worker flexibility has several advantages, it is costly to obtain and maintain given the productivity losses that arise from worker learning and forgetting effects. In this study, we review factors that influence worker forgetting in industrial settings, and analyze the degree to which existing mathematical models conform to observed human forgetting behavior. We find that the learn–forget curve model (LFCM) satisfies many characteristics of forgetting. In the context of worker flexibility, we use LFCM to understand the extent to which cross training and deployment become important in helping reduce forgetting effects. Finally, we enhance LFCM by augmenting it to incorporate the job similarity factor. Sensitivity analysis reveals that the importance of training and deployment policies is reduced as task similarity increases.

A search heuristic for the sequence-dependent economic lot scheduling problem

Abstract Almost all of the research on the economic lot scheduling problem (ELSP) has assumed that setup times are sequence-independent even though sequence-dependent problems are common in practice. Furthermore, most of the solution approaches that have been developed solve for a single optimal schedule when in practice it is more important to provide managers with a range of schedules of different length and complexity. In this paper, we develop a heuristic procedure to solve the ELSP problem with sequence-dependent setups. The heuristic provides a range of solutions from which a manager can choose, which should prove useful in an actual stochastic production environment. We show that our heuristic can outperform Dobsons heuristic when the utilization is high and the sequence-dependent setup times and costs are significant.

The production of several items in a single facility with linearly changing demand rates

In this paper we extend the ELSP model to allow for linearly changing demand rates over a fixed planning horizon. This extension of the ELSP research provides a model that can be used in coordinating the production and marketing planning activities in a firm. The model allows the user to evaluate the impact of changes in product demand on production costs and customer service. We solve the model using a standard nonlinear programming package (MINOS) and show through examples based on actual production data how the model can be used to support coordinated production and marketing planning.

Economic control chart policies for monitoring variables

In this paper, we compare the costs of an economically designed CUSUM control chart and a common Shewhart control chart, the X–bar chart for many configurations of parameters. Our results indicate that there are identifiable regions where there is an overwhelming cost advantage to using CUSUM charts. Additionally, we find that there are identifiable regions where an X–bar chart can be employed without any substantial economic disadvantage. Finally, we identify regions where a regular search policy is less costly than a policy of using either a CUSUM or X–bar chart.

Economic-Statistical Design of X̄ and R or X̄ and S Charts

In this paper we present a comprehensive FORTRAN program that can be used to jointly determine the parameters of X̄ charts used in combination with either R or S charts. The parameters are determined using economic, statistical, or economic- statistical design criteria. The general cost model due to Lorenzen and Vance (1986) is used in economic and economic-statistical designs.

Detecting improvement using Shewhart attribute control charts when the lower control limit is zero

In this paper, we present a method to monitor count data so as to be able to detect improvement when the counts are low enough to cause the lower limit to be zero. The method, which is proposed as an add-on to the conventional Shewhart control chart, consists in counting the number of samples in which zero defectives or zero defects per unit occur and signaling an increase in quality if k-in-a-row or 2-in-t samples have zero counts of defectives or zero defects per unit. This method enjoys some similarities to the very popular Shewhart control chart in that it is easy to design, understand and use. It is flexible, robust, and, like the Shewhart chart, yields detection frequencies that are optimal for very large shifts and good for other shifts. Some comparisons with traditional CUSUM charts are provided. Figures enabling Shewhart control chart users to easily design low-side add-on control charts are given for c and np charts.

Order Dispatching and Labor Assignment in Cellular Manufacturing Systems

Although order and labor dispatching in the job shop manufacturing setting have been investigated extensively over the last three decades, its representation of actual processes found in practice today is limited due to the move to cellular manufacturing (CM). Manufacturing cells have become an important approach to batch manufacturing in the last two decades, and their layout structure provides a dominant flow structure for the part routings. The flow shop nature of manufacturing cells adds a simplifying structure to the problem of planning worker assignments and order releases, which makes it more amenable to the use of optimization techniques. In this paper we exploit this characteristic and present two mathematical modeling approaches for making order dispatching and labor assignment/reassignment decisions in two different CM settings. The two formulations are evaluated in a dynamic simulation setting and compared to a heuristic procedure using tardiness as the primary performance measure. The formulations are superior to the heuristic approach and can be incorporated into detail scheduling systems that are being implemented by corporations employing enterprise resource planning (ERP) systems today.

On the Design of Single Sample Acceptance Sampling Plans

Abstract In this paper we provide a method of finding exact designs for single sample acceptance sampling plans. The method uses an algorithm that allows determination of plans based on the hypergeometric, binomial or Poisson distributions. Designs can be found for specified OC curves of either Type A or Type B.

Discrete Sequential Search with Group Activities

Equipment failures can have significant implications in terms of cost and customer satisfaction. Reducing the time required to find the cause of a failure can provide large cost savings and help preserve customer goodwill. Single-item discrete sequential search models can be used to sequence the tasks in diagnostic search to minimize the expected time required to find the cause of the failure. We increase the utility of the single-item discrete sequential search model by developing a formulation that includes simple precedence relationships as well as sequence dependent relationships defined by group activities. This formulation can be applied to a number of other problems including determining the sequence for multiple quality control tests on an item, scheduling oil well workovers to maximize the expected increase in oil production, and sequencing tasks in a research project where there is a technological risk associated with each task.

Economic Advantages of CUSUM Control Charts for Variables

CUSUM charts are usually recommended to be used to monitor the quality of a stable process when the expected shift is small. Here, a number of authors have shown that the average run length (ARL) performance of the CUSUM chart is better than that of the standard Shewhart chart. In this paper we address this question from an economic perspective. Specifically we consider the case where one is monitoring a stable process where the quality measurement is a variable and the underlying distribution is normal. We compare the economic performance of CUSUM and \( \bar X\) charts for a wide range of cost and system parameters in a large experiment using examples from the literature. We find that there are several situations in which CUSUM control charts have an economic advantage over \( \bar X\) charts. These situations are: 1. when there are high costs of false alarms and high costs of repairing a process; 2. when there are restrictions on sample size and sampling interval; 3. when there are several components of variance, and; 4. when there are statistical constraints on ARL.