Finite Schedule Monitoring and Filtering in a Computer Integrated Manufacturing Environment

R. G. Wilhelm’ (21, R. ChandrashekaP, R. Sun3, M. Hegedus4, B. Chu5, W. J. Tolone5, J. Long5 ’ Department of Mechanical Engineering UNC Charlotte, Charlotte, USA

zi2 Corporation, Dallas, TX, USA 30racle Corporation, Redwood City, USA

41BM, Charlotte 5Department of Computer Science, UNC Charlotte

Received on January 7,2000

Abstract An efficient and effective method of filtering is described for reactive scheduling in an integrated manufacturing environment. The filter evaluates the significance of schedule deviations in near-real-time and gives a statistical estimate of the importance of each schedule deviation: Highly important deviations being those that prevent schedule objectives from being achieved. A typical result from STC filtering would be an indication that there is a 95% probability of failing to meet the schedule if the resource were to continue working at the same rate. The technique is well suited for bottle-neck resources but may be applied to all resources in an enterprise. The filter, based on statistical throughput control (STC) is compared to past work in DEDS and reactive scheduling. Model formulation is presented to show extensions from previous applications of STC for short-term production control in a CONWIP (Constant WIP) production system. Data requirements are detailed with reference to Business Object Documents (BODS) defined by the Open Applications Group (OAG). A summary of the CllMPLEX framework is presented to show how the filter is applied.

Keywords: Integrated, Scheduling, Monitoring

1 INTRODUCTION Production scheduling systems are used to produce feasible schedules that solve short and medium term production scheduling requirements of manufacturing systems. Scheduling is a dynamic process and must react to changes on the production floor. An initial feasible schedule is often rendered infeasible due to perturbations in schedule execution. Typically, perturbations take the form of changes to the due dates of orders, machine breakdown, late arrival of raw material, new orders, change in priority of existing orders, ‘hot jobs’ and unavailability of resources. In several cases, the shop floor/line operators change the order of jobs to be worked on based on the immediate needs on the production floor. These disruptions in production schedules require rescheduling, as the initial predictive schedule is rendered infeasible. Rescheduling can, however, induce considerable nervousness in the manufacturing system that is characterized by increased uncertainty in production schedules, re-alignment of jobs on the shop floor, and disruption in planned orders. For example, rescheduling may cause re-assignment of a large number of jobs to different resources in a pattern that is totally different from that of the initial schedule. To avoid nervousness and rescheduling, a number of techniques to monitor and ‘repair’ existing schedules have been proposed and implemented [1,2,3,4,5,6]. Sun reviews these techniques in depth in [7]. Reactive scheduling techniques, including schedule repair, are used when catastrophic events occurring on the production floor necessitate significant changes to the initial schedule such as re-arrangement and re- assignment of jobs among available resources while maintaining temporal constraints and work in process (WIP) status [5,6]. This paper proposes a real time ‘filter’ that evaluates the impact of all disturbances at a work center level, and identifies those perturbations that are significant from a schedule compliance standpoint. Filtering means that all

disturbances in schedule execution are evaluated to determine if they influence the quality of the current schedule. Those disturbances that have low influence on schedule compliance are automatically ‘filtered‘ out. By effectively ‘filtering’ non-significant disturbances, the quality of the current schedule may be evaluated without triggering a rescheduling operation every time a disturbance is encountered in execution. These perturbations might be early job completion or tardy jobs since this approach evaluates both ‘good‘ and ‘bad‘ perturbations that arrive at a work center. Good perturbations are early job completion that imply that the work center is ahead of schedule and could potentially take on additional jobs. It could also indicate that the process rates are higher than estimated in the manufacturing model, triggering a change in the manufacturing model and hence the entire schedule. Bad perturbations are jobs that are delayed in processing at the work center. Delayed arrival of jobs at the work center leading to idle time also constitutes a ‘bad perturbation as it indicates a slowing down of job completion at work centers preceding this one. A number of small disturbances may add up to create a big disturbance and a combination of good and bad perturbations may balance out over time. The filtering approach provides information about the process rates that are attained and, if necessary, provides information to be fed back into the manufacturing model for rescheduling. By ensuring effective rate monitoring, the filter provides the human scheduler with real time information on the status of execution in the manufacturing system. Statistical throughput control (STC) is used to develop this filter that feeds off the information recorded in a manufacturing execution system (MES) database to perform a fast assessment of the impact of any disturbances on the production floor.

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2 STATISTICAL THROUGHPUT CONTROL STC is similar to Statistical Process Control (SPC) in the analysis of production/shop floor data. Hopp and Spearman [8] use STC for short-term production control in a CONWIP (Constant WIP) production system. Their system measures throughput against a planned production schedule. A STC control chart for a production line is maintained which tells the production controller the shortage or overage of production at that instant in time, and compares it with the planned production in that period. The probability of being able to meet the targeted production by the end of the planned production period is indicated on the STC charts. This technique is useful for monitoring discrete output of a CONWIP type line. It lends well to a WIP level monitoring scheme, where a pull type of production framework is in use. Their implementation of STC is restricted to a single or at the most two production lines and is not shown for larger, more complex production systems. This system is static, and does not react dynamically to perturbations as they occur. The decision support information provided by their implementation is limited to WIP control. The approach in this paper is to implement STC as a resource-monitoring filter, to detect variations in resource usage as a function of scheduled resource usage over the scheduling horizon. Let, Nt = actual cumulative machine hours used in the time interval [O,t] S, = scheduled cumulative machine hours used in the time interval [O,t] S, , the scheduled production function, is obtained from the dispatch list for the work center from the FCS. Nt , the actual production function, is obtained from the MES data that records the start and finish time of jobs on the work center. The STC chart is a plot of the probabilities of failing/exceeding the targeted machine usage over the scheduling horizon. By laying an STC probability curve of targeted machine usage alongside a plot of the actual machine usage, an estimate of the current state of the shop floor could be obtained. This gives the planner/scheduler an indication of whether the work center is behind schedule or ahead of schedule. A decision may then be taken to 1). Reschedule, 2). Deploy additional resources, 3). Add additional jobs to take up the 'bonus' capacity available. The STC chart is a plot of the variable x defined as:

x = -(p - Q)(R - t)/R + zoJ(R - t ) / R Where, p = Mean of machine hours used in the scheduling period 0 = Standard deviation of machine hours used in the scheduling period Q =targeted machine usage in that period R = number of production time intervals in the scheduling period - hours in the shift or number of days in the scheduling horizon. t = instance in time in that production period. z, =@( z, ) = a where a = Probability of failing to meet the targeted machine usage in the given time period @( z,) = area under the standard normal distribution curve corresponding to the probability a.

All the above information is obtained from the predictive schedule. The actual production track is the variable x, where x = N, - St the difference between actual machine usage and predictive machine usage obtained from the dispatch list. Consider an STC chart set up for a work center. The dispatch list for a set of jobs to be executed on the resource is in Table 1 and the corresponding job completion data in Table 2. The STC chart for the resource is in Figure 1.

Table 1. Dispatch List for Work Center

Table 2. MES Report for Work Center

I STC Chart for large perturbation

w - *

I

I

Scheduling Horizon (days)

~ __- - _ _ _ _ _ _ ~ ~ -1

Figure 1. STC Chart for Tables 1,2 The STC chart can be constructed from Tables 1 and 2 in the following manner. Consider Table 1 with the initial schedule. The scheduling horizon is for 30 days.

336

x = -(/A - Q)(R - t) / R + ZOJ-

Hence in the equation above, R=30. p=0.777023. This is the standard deviation of resource usage over the 30-day horizon. p=Q since we are operating on a schedule basis and not on a production quota. Hence the first term in the equation above is reduced to zero. Z,=1.645 for 95% confidence that the schedule will be adhered to at the present production rate. Hence, computing the STC chart data points for each day in the scheduling horizon, we get data points as: 0.173997, 0.170434, 0.166821 for the first three days; the last three data points are 0.041 771, 0.0291 79 and 0. The data points for 5% confidence may be computed in a similar manner using a Z,, value of -1.645. For different statistical confidence levels, different values of Z, might be used corresponding to the statistical confidence level desired. x = N, - S, is computed from the cumulative difference in daily resource usage between Table 1 - the predictive production schedule, and Table 2 - the MES production report. The data point at which the STC confidence limit is first breached is the I l l h data point with x=-0.14583. The production system does not recover, and schedule compliance progressively declines with x=-0.11875 for the 15'h day, -0.18889 for the 17Ih day, finally ending with a shortage of production corresponding to -0.86944 days at the end of the scheduling horizon. An STC chart using a 95% tolerance band indicates a confidence of 95% that the work center would not be able to complete the targeted production by the end of the scheduling horizon if it continues at the present rate. Similarly, a 5% tolerance band signifies a 5% confidence that the work center would fail to meet targeted production. Ideally, if the predictive schedule were adhered to perfectly, a confidence level of 50% would be maintained. This is true because for a confidence level of 50% Z, = 0 and the STC track is reduced to the X-axis in the absence of a production quota Q. Under such conditions, the probability of meeting the production schedule is exactly equal to 50% if the actual STC variable x = N, - S, is also equal to zero. Figure 1' displays an STC chart with large magnitude perturbations that breach the control limits after the 9lh day in the scheduling horizon. This indicates a 95% probability of failing to meet the schedule if the resource were to continue working at the same rate. STC in this modified form is used to monitor production at critical resources. A reduction in machine usage might indicate a slowing down of production upstream. An increase in machine usage might indicate a sudden buildup of jobs in the input buffer requiring the resource to be used to a greater extent indicating a potential bottleneck in the making. Using an instance of the STC filter at resources allows an effective filtering of perturbations until their cumulative effect exceeds a threshold value, indicating the need for the human scheduler to take action. STC filtering agents have been implemented for event filtering at the resource level in CllMPLEX [9]. 3 DATA REQUIREMENTS FOR STC The filtering techniques and tools described here are used as a part of a suite of software agents collectively known as the CAA or CllMPLEX Analysis Agent [7] developed by the CllMPLEX consortium. These software

agents are designed to plug into any enterprise environment where detailed schedules are available and an MES system records events such as job start and end time. The STC filter uses CllMPLEX agent technology and operates as an asynchronous software agent in an enterprise environment. An STC instance is created for each work center that is being monitored. A dispatch list for the work center is required for the work center in the scheduling period. The dispatch list is generated by a FCS system. Event information is provided by the MES system or any other form of supervisory controVevent reporting mechanism that is in place. In a CIM environment, this information is typically clocked in the MES database. The STC filter receives events from the MES system either by using CllMPLEX communication protocols or by querying the MES event database for the work center being monitored. An event that triggers the filter's tolerance band is reported to the human scheduler. This is shown in Figure 2.

ERP

patch

Exception to Scheduled

Manager

Figure 2. STC filter in production control As the schedule is executed, events are recorded by the MES system. These update reports are in the form of work in process (WIP) reporting at each work center. Depending upon the event reporting practices followed by the plant, events are recorded in the database as and when they occur or in a batch mode every hour or four times a shift. The events are obtained from the MES database and are compared with the STC track computed using the predictive schedule. The difference in the two tracks is laid over the STC track and a comparison of the current state of the work center is obtained. If the STC difference breaches the preset probability curves, a flag is raised and the filtering agent reports an exception to the human scheduler. The STC filter may be run synchronously as well and used to evaluate events as they occur. The data in the dispatch list is available in a flat file format and can be exported from the FCS system. This form of data interchange does not lend well in terms of a multi-application environment within an enterprise. The CllMPLEX integration methodology uses a standard format for representation of data generated by manufacturing applications called Business Object Documents or BOD'S. These standardized information interchange protocols are devised by the Open Applications Group [I 01. The OAG specifications allow standard representation for dispatch lists and for WIP events. 4 INTEGRATED FILTERING FRAMEWORK When filtering detects disruptions in the execution of a predictive schedule, the STC filter provides information on resources that are leading or lagging the predictive schedule. A number of selective schedule repair techniques might be used to reprioritize orders on the

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b Filtering

Agent

I 2. Inform

4 Scenario Manager Sensitivity

Analysis 3. Recommendations to

I Regenerate plans

4. Inform

Transformation 5. Recommendations to Update Upper Schedules

b

Change Orders/Reqwst Regen

b ERP

Change(XderslReqwst Regen

Figure 3. STC in CllMPLEX framework

filtered resource, avoiding a large scale rescheduling effort. As a part of the CllMPLEX framework, a temporal constraint propagation algorithm is used to reprioritize orders on each critical resource and create a new feasible schedule [Ill. As shown in Figure 3, an STC filter is coupled with a schedule repair algorithm to perform sensitivity analysis on schedule conformance. The output of sensitivity analysis feeds into a schedule transformation agent that assesses the impact of perturbations on the Master Production Schedule.

5 CONCLUSIONS An efficient and effective method of filtering disturbances at bottleneck resources and work centers in an integrated manufacturing environment has been developed. The proposed filtering methodology has been implemented as a part of the decision support agent framework developed for the CllMPLEX project. STC filter agents may be set up for each resource and can be coupled to any production reporting mechanism in place. Coupling STC filters with a sensitivity analysis agent and a schedule evaluation agent leads to a complete decision support framework in the enterprise. 6 ACKNOWLEDGMENTS This work is supported in part by the Advanced Technology Program administered by the National Institute of Standards and Technology under the agreement number: 70NANB6H2000. Additional support was provided by the University of North Carolina at Charlotte and the Cameron Applied Research Center. This material is based upon work supported by the National Science Foundation under Grant No. DMI- 94571 68.

I pq Request Regen

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