Ronald F. Perry

Flexible kanban system

Just‐in‐time (JIT) systems were originally designed for deterministic production environments such as constant processing times and smooth and stable demand. However, once implemented, JIT is fraught with numerous types of uncertainties, including variations in processing time and demand, planned interruptions such as preventive maintenance and unplanned interruptions such as equipment failure. These uncertainties lead to lowered production throughput, decreased machine utilization, increased order completion time and greater backlogs and overtime requirements. In this paper, we introduce a newly developed system, which we refer to as the flexible kanban system (FKS), to cope with uncertainties and planned/unplanned interruptions. We demonstrate the superiority of the new system by considering four case examples covering various uncertainties, conducting numerous studies and comparing the overall performances of the FKS with that of the traditional JIT system. In all the cases considered, the performance of the FKS was, indeed, superior to that of the traditional JIT system.

Interactive simulation modeling of automated storage retrieval systems

Owing to the complexity and enormous cost involved in Automated Material Handling Systems, there is a growing need to use computer simulation in both the physical system and control software design of such systems. Simulation models can be developed to test not only the final system configuration, but also each installation phase. This paper describes the development of an interactive and flexible simulation software for Automated Storage/Retrieval Systems( AS/RS) of the Miniload variety. A modular approach is taken in the development of the simulation software so that the user, through an interactive capability to model an menu, has the AS/RS by selecting a combination of modules that define the AS/RS. The user then enters the values of the system variables specified for each module. This user-defined simulation model is translated into a simulation language source code and then executed.

Predicting the Likelihood of Matriculation for College Applicants.

This paper reports on the collection and analysis of survey data directed toward eliciting those characteristics of the institution, applicant, and applicants family which affect the matriculation decision. Survey data were collected from freshman students accepted for 1981 and 1982. Discriminant analysis of both a parsimonious and an inclusive set of questions items was conducted, demonstrating that a small subset of the items could predict matriculation with an overall accuracy of 70% correct. Also, there was marked consistency in the effective set of predictor variables over the two year period. Using factor analysis it was shown that all of the predictor variables could be grouped into four composite variables reflecting institutional characteristics, admissions office activities, family attributes, and the cooperative education plan. Finally, we suggest ways in which the results may influence recruiting activities so that limited resources may yield more matriculants.

A Multi-attribute Simulation Paradigm for the Justification of FMS

Abstract The flexible manufacturing System (FMS) is the most promising solution to the problem of producing high quality products efficiently while still being able to adapt quickly to changes in demand. Due to the high cost of such systems, they require careful justification before implementation. Most justification approaches take a long-term view that heavily emphasizes capital investment. However, proper decisions in the operation of an FMS can greatly improve its performance and contribute to its justification. This paper develops a multi-attribute simulation model of an FMS which permits assessment of short and intermediate-term operation decisions. Measures of performance include not only the traditional ones, such as production rate, utilization and delay times, but heretofore neglected measures of quality and reliability. Experimentation with the model demonstrates that variables which define flexibility properties have a significant effect on quality measures. If one is concerned with efficiency and high quality in an FMS, the model must include input variables and output measures that explicitly define quality and reliability relationships.