The design of due date assignment model and the determination of flow time control parameters for the wafer fabrication factories

1997 ◽  
Vol 20 (4) ◽  
pp. 278-287 ◽  
Author(s):  
Shu-Hsing Chung ◽  
Ming-Hsien Yang ◽  
Cho-Ming Cheng
Keyword(s):  
Flow Time ◽  
Wafer Fabrication ◽  
Control Parameters ◽  
Due Date ◽  
Due Date Assignment ◽  
Time Control ◽  
Assignment Model

Simulation Modelling and Analysis of Due Date Assignment Methods and Scheduling Decision Rules in a Flexible Manufacturing System

2021 ◽  
Vol 106 ◽  
pp. 99-108
Author(s):  
V.V. Muhammed Anees ◽  
K.P. Abdul Nazar ◽  
R. Sridharan
Keyword(s):  
Standard Deviation ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Decision Rules ◽  
Flow Time ◽  
Mean Flow ◽  
Due Date ◽  
Due Date Assignment ◽  
Tardy Jobs

This paper presents the salient details of a simulation-based study conducted to analyze the effect of due date assignment methods and scheduling decision rules on the performance of a flexible manufacturing system. A typical FMS is considered for investigation in the present study. Three endogenous due date setting methods and one exogenous method are examined in the present study. The scheduling rules considered for experimentation include processing time based rules and due date based rules in addition to the unbiased first-in-first-out rule. The performance measures evaluated in the present study are mean flow time, standard deviation of flow time, mean tardiness, standard deviation of tardiness, percentage of tardy jobs and average flow allowance. Analysis of the simulation results reveal that the dynamic due date setting methods provide better system performance.

A Computer-Aided System for Job-Shop Flow Time and Due–Date Prediction

2011 ◽  
Vol 367 ◽  
pp. 639-645
Author(s):  
B.O. Akinnuli ◽  
S.A. Oluwadare ◽  
Adeyemi A. Aderoba
Keyword(s):  
Job Shop ◽  
Real Life ◽  
Flow Time ◽  
Production Data ◽  
Due Date ◽  
Computer Aided ◽  
Customer Dissatisfaction ◽  
Cost Inefficiency ◽  
Holding Cost

: One of the key challenges confronting a production system is the accurate prediction of flow time (completion time) and due date of jobs coming into the job-shop. The inaccuracy in the determination of flow time and due date could lead to problems of dead line misses, high holding cost for waiting jobs and/or completed jobs, technical and cost inefficiency; and customer dissatisfaction, among others. In this paper, a Computer-Aided System which is based on an empirical model for predicting job-shop flow time and due-date was developed. The Computer-Aided System makes use of historical production data and other peculiar in-house factors of an organization. The Computer-Aided System was tested with real life job-shop data in order to test its efficiency and it was found to be efficient in predicting flow time and due date of jobs.

Dynamic due date assignment method

2019 ◽  
Vol 30 (6) ◽  
pp. 987-1003 ◽  
Author(s):  
Vinod K.T. ◽  
S. Prabagaran ◽  
O.A. Joseph
Keyword(s):  
Regression Models ◽  
Job Shop ◽  
Setup Time ◽  
Flow Time ◽  
Analytical Models ◽  
Due Date ◽  
Due Date Assignment ◽  
Content Type ◽  
Mean Tardiness ◽  
Dynamic Job Shop

Purpose The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness. Design/methodology/approach A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation. Findings The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop. Research limitations/implications Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines. Practical implications The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error. Originality/value Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

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