Robust planning for FMS systems

Computers ind. Engng Vol. 21, Nos I-4, pp. 23-27, 1991 Printed in Great Britain. All rights reserved

0360.8352/91 $3.00+ 0.00 Copyright © 1991 Pergamon Press plc

ROBUST PLANNING FOR FMS SYSTEMS Naveen K. Velagapudi Assistant Professor Purdue University North Central INTRODUCTION Robust Planning and Scheduling Model (RPS Model) presents an architecture to improve the efficiency of FMS systems. The architecture utilizes existing sources of knowledge of operating principles to develop production plans to minimize implementation problems. The model provides for continuous improvement of system performance by learning from experience. ROBUSTNESS

OF PLAN

Production Plans usually face obstacles in implementation. These obstacles are referred to as disturbances in this paper. These disturbances cause variation in the system's performance. The less the system is susceptible to the dlsturbance, the more the ability of the system to achieve planned target!s). The ability of the plan to mlnlmlze or eliminate variation is referred to as robustness of the plan. Major characteristics of disturbances are: (a) System component associated with the disturbance (b) Stage of occurrence (c) Recovery time (4) Frequency of occurrence (5) Impact on the system The ability of the system to deal effectively with disturbances depends on the knowledge of disturbances and the validity of disturbance data. Disturbances could be handled by the following approaches. (a) Prepare robust plans: Production systems have to deal with many dynamic variables. Some of these variables are uncontrollable variables due to the fact that their causes are beyond the system's control. It is difficult to predict when and where an uncontrollable disturbance will impact the system. It is equally difficult to define the extent of impact. But it is possible to gather statistics as to the nature of these disturbances. Include these statistics in the p l a n n i n g model to select the proper set of controllable variables to minimize or eliminate the deviations. Ex: Disturbance: Changes in Production Quantities Poor Raw Material Quality Controllable Variable: Allocation of Resources to deal with Resource breakdowns CAIE 21:1-4-C

(b) Real time correction: As disturbances are unavoidable, the system performance is dependent on the restoration of order in real time. Restoration process should implement planned strategy to mlnlmlze the impact on the system. (c) Eliminate Disturbance: As we gain knowledge from the system steps must be taken to prevent recurrence of disturbances. This can be achieved by keeping track of disturbance data in the plan implementation stage. Some disturbances escape elimination due to cost considerations and technological feasibility. RPS MODEL RPS model incorporates all of the above approaches to direct the system to its planned target(s). The model has three major modules. The planning level, scheduling level, and learning module. These modules are discussed in detail in an earlier paper(2). This paper focuses on Robust Planning for FMS systems. PLANNING LEVEL Planning Level receives the following as input: (a) Aggregate plan (b) Product Data (c) System Status (a) Aqqreqate Plan: It provides product requirements with target times. Product code includes the target time to differentiate same product scheduled for different delivery times. Product requirement table(figure I) provides an example. b) Product Data: Planning level extracts the following information from the database: Manufacturina Tree: Each product to be produced will have its component information stored in the tree structure. The tree provides the assembly, sub assembly, manufactured and purchased parts. It also provides where a part is required, how many are required and lead times. Manufacturing tree(figure 2) provides an example. Routina Data: For each component (part), routing information provides for specific resources for desired and alternate paths.

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Proceedings of the 13th Annual Conference on Computers and Industrial Engineering

PLAN NUMBER

UNIT

PERIOD

PRODUCT NUMBER

PRODUCT NAME

QUANTITY

001

AI00

SAMPLE 1

i0

001

A300

SAMPLE2

20

001

A500

SAMPLE3

20

001

AI00

SAMPLE 1

20

Figure 1

PRODUCT REQUIREMENT TABLE I Samplel

I

Level 0 Lead time--> 1

I

I

I1

1 1 1 <-- #Required

All0

AI20

I

Level 1

I

.I 3 A122

Level 2 Figure 2

MANUFACTURING TREE

(c) System status: Loadina status: Demand on resources from unfinished jobs from previous plans for the planning horizon under consideration. Resource Status: Resource nonavailability due to breakdown. preventive Maintenance: Resources would not be available during preventive maintenance. Inventory Status: For all components, check the inventory. Disturbance Data: Disturbance statistics indicate the different types of disturbances, frequency of occurrence, impact and possible restoration action.

PLANNING STEPS An ideal plan would produce only the required part at the ri@ht time. Deviatlon ideal plan would Increase the cost of the plan. But system constraints force deviation from the ideal plan. In order to minimize such deviation, the RPS model attempts to create a smooth flow for as many parts as possible.

~D~IgJ~: The smallest time slot, i.e. scheduling time slot, in the planning horizon, ex: day

Planninu Period : The block of time t h a t is a multiple of planning Unit. Ex: Week = 5 working days Planninu Horizon: The entire time slot for preparing robust plan. It is a multiple of planning period. Ex: Quarter = 13 Weeks Load Scales: I = Resource group number, 0 indicates all resources J = Planning period number, 0 indicates entire horizon K = Planning unit number within planning period, 0 indicates entire period or horizon. Scale 1: For the entire planning horizon and for each group of resources (i,0,0). Scale 2: For each planning period (ex:week) and for the entire set of resources (0,j,0). Scale 3: For each planning period (ex:week) and for each group of resources (i,j,0). Scale 4: For each planning unit (ex:Day) and each group of resources (I,J,K). Resource and time slot indicator for scales(figure 3) provides an example for the different scales.

Velagapudi: FMS Systems

1 1

2

25

2

3

4

S

1

2

3

4

1

2

3

4

\

1

•

\!

@

i i

/\

....---

m

I

X

/

~

scale 1

scale 3

~ (o,j,o) scale 2

j~-]~ ~

(i,j,k) scale 4

Figure 3 Resources and time slot indicator for scales

O v e r v i e w of the s e q u e n c e of p l a n n i n g steps is p r e s e n t e d below:

STEP 2: For e a c h component, add c o m p o n e n t Float CW = W l + W 2 + W 3 + W 4 + W 5

S T E P i: D e v e l o p Float b a s e d on each c o m p o n e n t c h a r a c t e r i s t i c s . Float w e i g h t t a b l e ( f i g u r e 4) p r o v i d e s an example.

STEP 3: Define c l a s s e s of float. An e x a m p l e of class t h r e s h o l d s is g i v e n in figure 5.

WEIGHT

CHARACTERISTIC

HIGH

AVERAGE

LOW

Processing Time

Wl

0.5

1.5

P r o c e s s i n g Cost

W2

0.5

1.5

M a t e r i a l Cost

W3

0.5

1.5

Batch Size

W4

0.5

1.5

N u m b e r of R e s o u r c e s Visited

W5

0.5

1.5

F i g u r e 4. Class Class A Class B Class C

Figure 5

FLOAT

WEIGHT TABLE

Float Low Average High

Class T h r e s h o l d s 0

<

CW

<=

2.5

2.5

<

CW

<=

5

5

<

CW

FLOAT C L A S S I F I C A T I O N T A B L E

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Proceedings of the 13th Annual Conference on Computers and Industrial Engineering

Class C components are allowed to float across time slots. Class B components are allowed to float only after class C has been utilized. Class A components have least float and may not float. Step 4: For each product, Using the manufacturing tree and material requirement planning temporarily fix a ~lanning unit for each component. Thls time frame indicates the ideal time for component production. Step 5: Once the planning unit time slots for component production are set, resource requirement scale(RRS) is prepared. For the time slot under consideration, for the resource group under consideration, add all the demand for the resource by all components. Prepare R.R.S.(i,0,0)

(scale i)

R.R.S.(0,~0)(scale

2),

R.R.S.(i,j,O)(scale R.R.S.(i,j,k) (Scale 4). Step 6: Resource Available Percentaae(RAP): An important decision to be made is what Is the resource available time after disturbances. This is the important variable that will decide the plans ability to cope with disturbances. Higher Percentage (Ex:98%) may result in too little room to absorb the impact of disturbance. Smaller RAP may lead to lower utilization and demand for expensive additional resources. RAP is the controllable variable that can be used to minimize the impact of disturbances. Decide the number of times to repeat RAP selection. For each RAP, run steps 7 to 12. Step 7: Select a RAP. Develop resource availability scales(RAS). For the time slot under consideration, for the resource group under consideration, prepare availability scales. Subtract the preventive maintenance time from availability. If a resource is

R.R.S. VS R.A.S.

currently not available, consider it only available after taking into consideration the restoration time. Also give allowance for shift change time etc. Prepare RAS(I,0,0) (Scale I), RAS(0,J,0)(Scale 2), RAS(I,J,0)(Scale 3), and RAS(I,J,K) (scale 4). Step 8: Compare the RRS and RAS Load scales. Develop time slots ready to accept or discharge. Load comparison scale(figure 6) provides an example. Step 9: a) Permanently fix all components of class A in the ideal planning unit. Check capacity. If capacity is insufficient in any time unit, allow jobs to float. Break ties arbitrarily. b)Fix class B components and check for capacity. If movement is required, start with lowest float, move it to the closest time slot with acceptance. Do it starting from the first time unit and period. Check for capacity. Re~eat till capacity requirement is satisfied. Relax class thresholds to allow more jobs to move more freely. c) Fix class C Components and check for capacity. Start with lowest float, move it to the closest time slot with acceptance. Do it starting from the first time unit and period. Check for capacity. Repeat till capacity requirement is satisfied. Lack of capacity may sometimes require additional sources of a particular resource group.(Ex: extra shift or out sourcing). The prepared plan is then sent to load sequence file. STEP i0: Simulation model is provided with the current system status. The resource status, load Sequence, routing data is provided to the model. The execution of plan is then subjected to disturbances by the simulator. If a disturbance is noticed, alternatives are provided. If the impact of disturbance is

Scale 4 (i,j,k)

Scale 2 (0,j,0)

Scale 3 (i,j,0)

BlockingNeed extra Resources

Discharge Components

Discharge Components

Discharge Components

Possible blocking

Limited Discharge

Limited Discharge

Limited Discharge

Accept Components

Accept Components

Accept Components

Scale 1 (i,0,0)

Figure 6

LOAD COMPARISON TABLE

Velagapudi: FMS Systems estimated to be minimum, then the plan is allowed to continue with the time differential. If the impact of disturbance is estimated to be significant, then alternative resources and rerouting is suggested. If the disturbance is likely to cause blocking, then jobs are allowed to be delayed or pushed into a bin. Collect statistics and costs due to deviations from the plan. Cost of Implementation = Minimum delay cost+ Rerouting cost+ Blocking Cost Cost of RAP = Cost of Additional Resources+ Cost of Implementation STEP 11: Run the simulation with different seeds for disturbance and find average cost over the runs. STEP 12: Go to Step 6 if more RAP selections have to be made. Otherwise go to step 13. STEP 13: Compare cost of RAP and decide the best RAP. The resulting robust plan is sent to the execution level of RPS model. LEARNING MODULE Learning module keeps track of important measurements for future analysis. Candidates for such observation are Resource availability percentage, Class Thresholds, Disturbance correction measures and disturbance statistics. CONCLUSION The model provides an architecture for preparing Robust plan. The results of the plan are dependent on the selection of controllable variables. These variables have to observed over a period of time for the improvement of system performance. References I. Gershwin,S.B., Hildebrant,R.R., Suri,R., Mitter,S.K., 1986, "A Control Perspective on Recent Trends in Manufacturing Systems", IEEE Control Systems Magazine, April. 2. Velagapudi,N.K., Ghosh,B.K., 1989,"Robust Planning and Scheduling for Automated Batch Manufacturing Systems", Proceedings of llth conference on Computers and Industrial Engineering, Orlando, FL.

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