Flow Time Performance Research Articles

The effect of push and pull lot splitting approaches on lot traceability and material handling costs in stochastic flow shop environments

This study evaluates how the procedures followed in splitting job lots on the shop floor influence both material handling and lot integrity in stochastic flow shop environments. Former work has shown that lot splitting improves flow time and customer service performance, but with an increased number of transfers along with a corresponding increase in material handling costs. Physical lot integrity, which is important to lot tracing, has not been considered in prior literature. In order to overcome these limitations and address material handling concerns better, we define a new measure of performance called the lot integrity loss exposure (LILE). We use this and other measures to show that the traditional 'push' approach that has been followed in modelling lot splitting complicates lot traceability in the shop and leads to an excessive number of transfers. As an alternative, we propose a 'pull' approach in which an order is split between any successive pair of machines only on an as-needed basis. The push and pull approaches for lot splitting are compared under a range of conditions characterized by factors such as machine utilization levels within the shop, setup-to-processing time ratios, and number of splits created in job lots. Our results indicate that relative to the push approach, pull lot splitting helps significantly in reducing the number of transfers incurred and in maintaining a greater degree of physical lot integrity. At the same time, there is no significant difference in the two approaches with respect to improvements in flow time and customer service measures. Both the degree to which lot integrity is preserved, and the extent of savings in material handling that result from using the pull approach, are shown to be most pronounced under conditions characterized by high machine utilizations rates and setup-to-processing time ratios. Managerial implications of our study along with further research directions in this area are also discussed.

Using an empirical queueing approach to predict future flow times

The traditional method used to describe a steady-state manufacturing system is a queueing model; whereas the common tool used to predict the future performance of a dynamic manufacturing system is a simulation model. This study proposes an empirical queueing approach to obtain the flow time performance measures of a complex dynamic manufacturing system, such as semiconductor wafer fabrication. This approach is easier to implement than a simulation model and more straightforward than a queueing model. Initially, the empirical queueing curve of each work station, which defines the relationship between the utilization rate and the expected waiting time of a small time period, is obtained from the historical database. Then, an iterative scheme is used to predict the future system behavior. Several latest researches have reported that the prediction of future flow times is important in the operation management of a complex manufacturing system. The approach proposed in this study can be easily implemented for such purposes, and the experimental results show that this approach can accurately predict the future flow times.

A control framework to support responsive manufacturing

This paper is concerned with reducing the barriers imposed on the flexibility and responsiveness of automated manufacturing systems by current control software technology. The general question that is addressed by this research is, how can insights be gained from the manufacturing system that can assist the control system in meeting this goal of responsive behavior? The approach that is taken is to investigate appropriate means of integrating available manufacturing system information into the control system. A framework for integrating status information to control an automated assembly line is introduced that combines both transient information (e.g. station queue length) and steady‐state information (e.g. station gradient estimates) obtained by observing the operation of the assembly line. It is shown that, through the use of an appropriately designed fuzzy‐logic controller (FLC), the combined information results in flow time performance superior to that achieved using the transient or steady‐state measures individually.

Cellular vs process layouts: an analytic investigation of the impact of learning on shop performance

Evidence from the literature on cellular manufacturing suggests that shops configured as manufacturing cells perform poorly compared to job shops. However, cellular shops, being conducive to the use of teams in the assignment of production activities, have the potential to yield higher productivity than a job shop. Productivity differentials, and in particular, differences in the rates at which processing times can be reduced, have been largely overlooked in prior comparisons of cellular and job shops. This paper uses queuing theory to illustrate the relationship between processing time learning rates and flow time performance in cellular and job shops. Models are developed that make it possible to estimate the learning rate required in a cellular shop in order for it to yield performance comparable to that of a job shop. Simulation is used to validate the models under dynamic conditions as opposed to the steady state conditions assumed by queuing theory. Results indicate that a cellular shop need only achieve a marginally higher learning rate than a job shop in order to perform at a comparable level.

Stochastically minimizing total flowtime in flowshops with no waiting space

The problem of scheduling n jobs in a two-machine flowshop with constant and known processing times is considered with the total flowtime performance measure. The machines are subject to random breakdowns and there is no waiting space between them. The problem is formulated and an expression for the completion time of the jobs is obtained in terms of the processing times and the breakdown elements. Provided that the counting processes associated with both machines have stationary increments property, a sequence that stochastically minimizes the performance criterion is established for the cases when only the first or the second machine suffers breakdowns.

The Effects of Scheduling Rules and Routing Flexibility on the Performance of a Random Flexible Manufacturing System

The increased use of flexible manufacturing systems to efficiently provide customers with diversified products has created a significant set of operational challenges for managers. Many issues concerning procedures and policies for the day-to-day operation of these systems still are unresolved. Previous studies in this area have concentrated on various problems by isolating or simplifying the systems under study. The primary objective of this study is to extend previous research by examining the effects of scheduling rules and routing flexibility on the performance of a constrained, random flexible manufacturing system (FMS). Other experimental factors considered are shop load, shop configuration, and system breakdowns. Within the bounds of this experiment, the results indicate that, in the presence of total routing flexibility, the effects of shop load, system breakdowns, and scheduling rules are significantly dampened. In particular, when total routing flexibility exists, the choice of scheduling rules is not critical. We also show that the behavior of scheduling rules in a more constrained FMS environment (i.e., where system breakdowns occur and material handling capability is limited) is consistent with the findings of previous research conducted under less constrained environments. Finally, results indicate that the shop configuration factor has little or no impact on a system's flow-time performance.

An analytical approach for using simulation in real-time decision making in FMSs

Abstract This paper presents an analytical approach to determine the suitability of and to adapt discrete-event simulation for real-time decision making in flexible manufacturing systems (FMSs). First, a formula is developed to predict the simulation CPU run time for a given manufacturing system, a planning horizon, and a computer system. It is shown that there are only three main factors that affect simulation run time: the planning horizon, the overall system average interarrival time, and the average number of workstations per part routing. In light of this, an approach to reduce simulation run time is presented. It is based on aggregating workstations to reduce the average number of workstations per part routing. The validity of the approach is justified by comparing the performance measures of example systems with and without aggregation. A theoretical approximation of the error incurred as a result of aggregations is derived. The results show that time savings of up to 400% can be achieved at the expense of only a few percentage points loss in accuracy of the average flowtime performance measure. The developed concepts have been integrated in an algorithm to serve as a front end for discrete-event simulation to adapt simulation models to real-time decision-making requirements. The application of this algorithm is illustrated with an example.

Impactof maintenance policies onthe performance of manufacturing cells

With increases in maintenance, equipment, and labour costs, the need for an effective maintenance program is increasing. Previous research has shown that the use of preventive maintenance procedures may allow for a simpler and less expensive way of maintaining and operating equipment. Many studies have dealt with various characteristics of cellular manufacturing. However, almost all of these studies have assumed machines do not break down. The objective of this study is to compare the performance of three maintenance policies in conjunction with four two-stagegroup scheduling heuristics, under eight experimental conditions. Approaches which induce shop efficiency by performing preventive maintenance and machine setups simultaneously are also considered. The results of this study extend the findings of previous research by demonstrating that utilizing preventive maintenance procedures generally results in better average flowtime and percent tardy performance while deteriorating the average tardiness performance in some cases. Furthermore, it was shown that conducting preventive maintenance and machine setups simultaneously improves the average tardiness performance while maintaining the same average flowtime performance compared to the case where these activities are performed separately. Finally, it was demonstrated that if the primary concern is average flowtime or other efficiency oriented measures, the choice of group scheduling heuristic is critical. However, if the primary concern is due date attainment, the choice of maintenance policy becomes more critical.

Flow-time performance of modified scheduling heuristics in a dynamic rescheduling environment

A generic, modular simulation model was constructed using the SLAM II discrete event modeling language to investigate the flowtime performance of a job shop. The findings of earlier research using conventional scheduling heuristics were confirmed and used to validate the design of revised AWINQ and MST heuristics. These heuristics were reformulated to take into account the state of the entire job shop at each rescheduling opportunity. Simulation results indicate that the revised heuristics perform better than the conventional SPT heuristic when job load is moderate. At high loads, the SPT rule was the preferred heuristic.

Lot Splitting in Stochastic Flow Shop and Job Shop Environments

In recent years many firms have been implementing small lot size production. Lot splitting breaks large orders into smaller transfer lots and offers the ability to move parts more quickly through the production process. This paper extends the deterministic studies by investigating various lot splitting policies in both stochastic job shop and stochastic flow shop settings using performance measures of mean flow time and the standard deviation of flow time. Using a computer simulation experiment, we found that in stochastic dynamic job shops, the number of lot splits is more important than the exact form of splitting. However, when optimal job sizes are determined for each scenario, we found a few circumstances where the implementation of a small initial split, called a “flag,” can provide measurable improvement in flow time performance. Interestingly, the vast majority of previous research indicates that methods other than equal lot splitting typically improves makespan performance. The earlier research, however, has been set in the static, deterministic flow shop environment. Thus, our results are of practical interest since they show that the specific method of lot splitting is important in only a small set of realistic environments while the choice of an appropriate number of splits is typically more important.

Batching and Scheduling in FMS Hubs: Flow Time Considerations

Many flexible manufacturing systems (FMS) involve complex queueing networks in the processing of lots (jobs) in a production facility. In particular, the feedback of lots to a common, multipurpose workstation, or hub, processing different tasks for the same lot at various points in the production process, can create serious scheduling problems. This paper models the hub structure and presents some results for dual measures of flow time in the facility, namely, total flow time required for a fixed number of lots (or makespan), and average flow time for all lots (or average completion time). The flow time performance is a function of different batch sizes and priority scheduling rules for lots arriving at the hub. The results can be used to establish strategies for batching and scheduling lots according to adjustments in the importance of the cycle time versus the work-in-process (WIP) tradeoff. Extensions of the results to more general production facilities are also discussed.

Product mix selection and closed manufacturing cell flow time performance

This research addresses performance issues for Just-In-Time types of “manufacturing cells”. Prior research ignores the pragmatic reality that the formation of manufacturing cells does not always totally eliminate stochastic job arrivals and setup requirements between successive jobs. These issues are addressed in this research through an analysis of the queueing consequences of product mix decisions. Specifically, this research is focused on the linkage between competitive advantage, traditional cell performance measures, and product mix selections. A “closed” shop is considered (i.e., a manufacturing cell that produces to stock rather than to order). Managerial insights are derived for product mix selection. Performance of a manufacturing cell in terms of mean and variance of job flow time is shown to improve with increasing product homogeneity (i.e., reduced variability among setup time requirements and unit processing time requirements). Optimal product batch sizes are also shown to decrease with increasing product homogeneity. Finally, the relationships between product mix characteristics and cell performance measures are related to the shop's competitive advantage.

An evaluation of order releasing and due date assignment heuristics in a cellular manufacturing system

AbstractThe presence of an order release function, where dispatchers can release or hold jobs from the first work center, has yielded beneficial results in practice but inconsistent results in research. Previous research results contradict the notion that shop floor information should be used to trigger the release mechanism. At the same time, such information has been effectively used to assign realistic due dates to released jobs.This study extends previous work on order releasing and due date assignment by providing a controlled comparison of three order releasing and two due date assignment heuristics in conjunction with six scheduling heuristics in a cellular manufacturing environment. Results show that controlled release deteriorates flow time, lateness, and tardiness performance and is inferior to both immediate and interval release. Controlled release appears to work best in situations of low load and tight due dates. Analysis of different dispatching rules shows the relative performance remains unchanged by the presence of different order release mechanisms.Comparisons of internally and externally set due date mechanisms indicate simpler, non‐due date oriented heuristics demonstrate as good a performance as the more complex due date oriented heuristics when shop information is utilized to assign job due dates. Finally, results indicate that in a manufacturing cell, the use of shop floor information is effective for due date assignment, but is not worthwhile for order releasing.