Roberta S. Russell

A comparative analysis of the COVERT job sequencing rule using various shop performance measures

The COVERT job shop dispatching rule was tested extensively twenty years ago with impressive results, however, since then it has been included in only one comparative analysis with other sequencing rules, and, reported instances of its application have been infrequent. In this paper, the COVERT rule is examined in detail relative to its applicability, its sensitivity to various operating parameters and performance measures, and its performance compared to several other sequencing rules including truncated SPT rules, dynamic slack rules and modified duedate rules. The performance of COVERT is examined for a variety of tardiness measures. The examination is conducted within the context of a simulation model of a machine-constrained job shop with serial jobs and random routings. The results indicate that COVERT performs well as a sequencing rule and in most instances was superior to the other sequencing rules tested both directly and across varying degrees of due-date tightness.

EVALUATING MIXED-MODEL ASSEMBLY LINE SEQUENCING HEURISTICS FOR JUST-IN-TIME PRODUCTION SYSTEMS

Abstract When mixed-model assembly lines use components fabricated in-house, the demand for these components is not uniform over time and is affected by the sequence of models on the assembly line. Thus, without proper mixed-model sequencing and the subsequent smoothing of component demand, the effectiveness of a just-in-time (JIT) production system is limited. This paper focuses on making component usage uniform. Five sequencing methods are reviewed, two suggested by Monden (1983) and used at Toyota (GC1 and GC2), and three proposed by Miltenburg (1989) (M-A1, MA3H1, and M-A3H2). Their performance is evaluated for the special case when all models use the same components. For all of the sequencing methods tested the mean absolute deviation of model usage varies directly with the number of models produced. There is no clear relationship between the mean absolute deviation of model usage and demand pattern or length of production sequence. Method M-A3H2 produces the highest quality feasible solutions under all conditions tested. The relative performance of the methods does not appear to be related to the number of models, demand type, or length of production sequence. To compare methods for the more general case of different models requiring different components, a mixed integer programming (MIP) model is presented as a way to find an optimal sequence. The M1P creates optimal solutions but is too slow to be used in practice. The two goal chasing heuristics used by Toyota were considered on the basis of their ability to schedule production to use components linearly over time. It is shown that these methods differ widely in their ability to generate good sequences. The performance of both is best when the products assembled have simple product structures. When models require more than one of a given component or when models require many different components, the performance worsens. The difference in performance is very small for GC1, but significant for GC2.

A preliminary investigation of multi-attribute based sequencing rules for assembly shops

This paper updates the current literature on assembly shop scheduling to include multi-attribute based sequencing rules that have recently been introduced and measures of shop performance that more accurately reflect managements view toward tardiness and inventory. In addition, a new set of sequencing rules, called importance ratio (IR), is proposed. The operation of a hypothetical assembly shop is simulated to test the performance of eight sequencing rules on three distinct sets of product structures. Four measures of system inventory and four measures of job tardiness are used to evaluate sequencing rule performance. The study shows that sequencing rules which incorporate attributes of both job shop and assembly shop scheduling do not necessarily produce the best performance. Moreover, multiple measures of inventory and tardiness performance do provide important insight into the operation and particular benefits of different sequencing rules. Finally, the importance ratio rules introduced are viable ca...

An evaluation of scheduling policies in a dual resource constrained assembly shop

Abstract The purpose of this paper is to evaluate scheduling policies for the production of assembled products in an assembly shop. The scheduling policies examined include duedate assignment, labor assignment and item sequencing rules. The sensitivity of these rules to product structure is also considered. The data for analysis is generated by a SLAM II simulation model of a hypothetical dual constrained assembly shop operation. The 2·3·3·3 complete factorial experiment is analyzed by an ANOVA procedure to statistically determine whether job structure, duedate assignment rule, labor assignment rule and item sequencing rule or their interaction significantly affect the root mean square of tardiness of jobs completed by the assembly shop. Further analysis to identify where significant differences in performance occurs is conducted via Tukey multiple comparison tests and general linear contrasts.

Using beam search techniques for sequencing mixed-model assembly lines

This paper introduces a beam search approach to sequencing mixed-model assembly lines and compares that approach to existing sequencing heuristics. The comparison study consists of over 400 test problems that vary in terms of number of product models, quantity of assembly, and degree of component commonality. The results show that beam search techniques are clearly superior to both the goal chasing algorithm (GCA) and Miltenburg and Sinnamons look ahead heuristic. The second half of this paper extends the beam search approach to allow two scheduling objectives: (1) minimizing parts consumption variation, and (2) minimizing workload variation. Termed filtered beam, this variation uses a filter to eliminate alternatives that exceed a predetermined threshold according to one objective, and then proceeds with the beam search for the second objective. As in the first case, optimization is not guaranteed; however, the filtered beam search provides a frontier of good trade-off solutions from which the decision maker can choose an acceptable sequence.

Workload vs scheduling policies in a dual-resource constrained job shop

Abstract The purpose of this paper is to report on research conducted to examine the effectiveness of different scheduling policies in a dual-constrained job shop under various workload conditions. The standard assumption in most job shop scheduling research has been that a 90% utilization of the shop is achieved. However, since shop utilization levels vary widely, it was hypothesized that scheduling policies that are optimum under one load condition might not be as effective under other load conditions. The model for this simulation experiment represented a job shop constrained by both labor and machines. The shop contains four machine centers with random routing of jobs through the shop. Shop workload was defined at three levels: 70, 85 and 99% utilization. Four machine scheduling rules and three labor assignment rules were tested for each of the shop workload levels, with mean job flow time as. the performance criterion. The results of the 3 × 4 × 3 factorial experiment showed that the advantage of the SPT (shortest processing time) machine scheduling rule over other rules is diminished dramatically when shop utilization is reduced from 99 to 85% or below. This same observation holds for other rules considered. The LNQ (longest queue length) labor assignment rule outperformed other rules at the 99% utilization level, but yielded no significant difference in performance at the 85% and below workload levels.

Using Cognitive Fit Theory to Evaluate the Effectiveness of Information Visualizations: An Example Using Quality Assurance Data

Cognitive fit theory, along with the proximity compatibility principle, is investigated as a basis to evaluate the effectiveness of information visualizations to support a decision-making task. The task used in this study manipulates varying levels of task complexity for quality control decisions in a high-volume discrete manufacturing environment. The volume of process monitoring and quality control data produced in this type of environment can be daunting. Todays managers need effective decision support tools to sort through the morass of data in a timely fashion to make critical decisions on product and process quality.

Patient perceptions of quality: analyzing patient satisfaction surveys

Purpose – Healthcare facilities are entering an era of increased oversight and heightened expectations concerning both reduced costs and measureable quality. The US Affordable Care Act requires healthcare organizations to collect certain metrics, including patient assessments of quality, in order to monitor and improve the quality of healthcare. These metrics are used as a basis for graduated insurance reimbursements, and are available to consumers as an aid in selecting healthcare providers and insurance plans. The purpose of this paper is to provide healthcare providers with the analytic capabilities to better understand quality of care from the patient’s point of view. Design/methodology/approach – This research examines patient satisfaction data from a multi-specialty Medical Practice Group, and uses regression analysis and paired comparisons to provide insight into patient perceptions of care quality. Findings – Results show that variables related to Access, Moving Through the Visit, Nurse/Assistant,...

Perceptions of care quality and the effect on patient satisfaction

Purpose This research models the impact of patient perceptions of care quality on overall patient satisfaction in a rural healthcare organization over a three-year time period. The purpose of this paper is to determine if the factors that influence perceptions of service quality change over time and if the change affects overall patient satisfaction. Design/methodology/approach Data were collected for three fiscal years (2012-2014) using a 36-question, Likert-scaled attitudinal survey. Multiple regression analysis was performed to identify which constructs of five different service quality dimensions were statistically significant in predicting overall patient satisfaction. Paired comparison of means and ANOVA F-tests highlighted significant differences across years and demographics. Findings Multiple regression models of overall patient satisfaction over a three-year time period had significant repeat variables, indicating salience of the dimensions and constructs of service quality that predict patient satisfaction. However, some dimensions of service quality did not remain significant from one year to another, indicating there may be a gap in the patient service cycle over an extended time frame. Originality/value This paper explored the sequential relationship between patient satisfaction survey data and perceptions of service quality over a multi-year time frame. The research focussed on outpatient medical clinics, while the majority of previous studies have focussed on acute care or inpatient stays. A longitudinal study is especially relevant for outpatient clinics where continuity of care is important.

SEM of Service Quality to Predict Overall Patient Satisfaction in Medical Clinics: A Case Study

This study analyzed patient satisfaction surveys to determine which process attributes affect overall patient satisfaction. The authors conclude that the strongest impact is the care providers interaction with the patient. Healthcare providers/practitioners value business analytics as a means to focus and redirect their continuous improvement efforts and to encourage appropriate provider behavior. The data for this research were gathered from patient satisfaction surveys from 18 diverse medical clinics in a rural, Midwestern U.S. healthcare system. Overall patient satisfaction was measured by four separate variables. Quality service was measured by five dimensions and related constructs. All were measured through the use of a five-point Likert-scaled survey. Confirmatory factor analysis and structural equation modeling were applied to identify the factors that impact overall patient satisfaction at the clinics. This research, and the surveys from which the data were derived, focus on process dimensions of service quality, rather than the technical aspects of the healthcare delivery system.