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IPPM – A Prototype to Integrate Process Planning and Job Shop Scheduling Functions
IPPM - A Prototype to Integrate Process Planning and Job Shop Scheduling Functions Hong-Chao Zhang, Texas Tech University - Submitted by M. Eugene Merchant (1) Received on January 15,1993
S U M M A R Y: Functional integration has taken the foreground in the current manufacturing system development. Process planning and job shop scheduling are two main manufacturing functions involved in shopfloor activities. An integrated process planning model (IPPM) is introduced in this paper. The two functions (process planning and job shop scheduling) are truly integrated by means of distributed approach which is different from current nonlinear and alternative process planning approaches. In this paper, the two functions are integrated at task stage, while the nonlinear and alternative approaches are taken place at result stage which cannot b e considered as real integration but rather interface. The introduced IPPM works in three levels, based on manufacturing resource availability and real time feedback from shop floor. they are. pre-planning, pairing planning, and final planning. The IPPM consists of three modules, introduced in detailed in the paper, they are: process planning module, production scheduling module, and decision making module. An example is provided to illustrate the model. This paper is to b e considered as a contribution to the research efforts in process planning and integrated manufacturing Kevwords: CAPP, Production Scheduling, Functional Integration. 1. Introduction
Computer aided process planning (CAPP) has been recognized as playing a key role in computer integrated manufacturing (CIM). In the last thrn decades, many CAPP systems have been developcd in terms of variant or generative approaches. To realize the expected integrated manufacturing. considerable effort has k e n made to integrate varieties of manufacturing functions from design phase to shopfloor control. Especially, the integration of process planning with design function has received remarkable progress. Whik many different geometrical models and feature-based techniques have been developed and applied in industry. the integration of process planning with production scheduling has not received some satisfactory results. In this paper, the issue of inregration of p m s s planning with production scheduling is discussed in detail. Several current approaches 0 t h used for integration of process planning with production scheduling are introduced. Finally, an integrated process planning model is provided in the paper. The question of the integration of process planning and production scheduling arose m the middle of the 1980s [14].After then. the issue has been frequently addressed in literature. [S-81. and several prototype integrated models have been reported. RTCAPP [9-10]. DPP [11-12], FLAXPLAN [13-141. XMAPP [lS]. Although these models have mt solved the problem completely. they are good enough to spark light for trends of future research. Process planning is such a complex function that it was too diffzult to concern the impact on the overall performance of the production aspects by a process planner, even though it was a sophisticated planner. The early automated process planning systems could not address the problem either, b c w w the capacity of hardware and software was limited, and most research attentions were focused on stand alone system and integration with design aspect. Since process planning and scheduling are performed separately, and process plans are generated without consideration of job shop status information, many problems arise within the manufacturing environment.
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Process p l m r s assume unlimited resources on the shopfloor and an idle factory. They plan at the most recommended alternate processes. and desirable machines are repeatedly selected by various process planners. Scheduling follows the process planning stage. Thus. when the process is going to be carried out. some constraints will be encountered. making the generated "optimal" process plans infeasible or less optimal in a scheduling engineer's view.
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The throughput of orders in a workshop often suffers from disruptions caused by stochastic bottlenecks. non-availability of tools and personnel. or breakdown of equipment. A readily generated schedule becomes invalid and has to be regenerated. Rescheduling is mostly carried out by improvisation and can result in long throughput times. A large number of process plans perhaps cannot be executed and have to be altered.
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0th process planning and scheduling have conflicting objectives. Process planning emphasizes the technological requirements of a task, while scheduling involves the timing aspects of it. Usually, single criterion optimization is considered for both process planning and scheduling. However. in a real production environment. more than one criterion necd to be considered simultaneously. and their relative importance in decision making change dynamically due to the dynamic shopfloor condition.
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Due to the complexity of the real production environment, neither the process plans nor the planned schedules arc truly followed at Ihe shopfloor. Without the feedback from the shopfloor, it becomes difficult to measure the quality or valve of a plan for future enhancement.
2. Approaches of Integration
Integration of process planning and production scheduling is essential to achieve eventually integrated manufamring and dismiss the traditional sequential manufacturing function. So far. several approaches for integration of process planning and production scheduling have been discussed in terms of nonlinear process planning. flexible process planning. closed loop process planning, dynamic process planning. alternative process planning. as well as just-in-time process planning, [12. 14. 16-22]. With studying of these reports. examination of these different terms, and analysis of the structure and information flow of the proposals. three types of general integration approaches can be categorized.
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Nonlinear process planning (NLPP): The NLPP makes all possible (or in other word alternative) plans for each part before it enters into shopfloor. The NLPP is still based on a static shopfloor situation. All these possible plans are ranked according to process planning criteria and stored in process planning database. The first priority plan is always ready for submission when the job is rquired. The scheduling makes the real decision. If the first priority plan is not suitable for Ihc current SUNS of tk shopfloor. the second priority plan will be provided to the scheduling, This procedure is repeated until a suitable plan is determined. The StrUCNlZ of NLPP is illustrated in Figure I . A typical example of NLPP system named FLEXPLAN has been developed in the University of Hanover. Germany [131.
Closed loop process planning (CLPP): The CLPP generates plans by meam of a dynamic feedback from production scheduling. The p m s planning mechanism generates process plans based on available resources. Production scheduling tells process planning what machms are available in the shopfloor for r k coming job, so hat every plan is feasible with respect to the current availability of production facilities. Real time status is crucial for thc CLPP. Since a real time dynamic feedback is required for the CLPP, it is also referred to as real time process planning or dynamic process planning. The sWcNre of the CLPP is illustrated in Figure 2 . Two prntotypc CLPP systems. DPP [ 11121, and RTCAPP [9-101 have been r e p o d .
Even with considering the shopfloor condition. the time delay between thc planning phase and the planning execution phase will cause trouble. Due to the dynamic n a ~ r of e a production environment, it is very likely that when a design is ready to be manufactured the consmints that were used in generating the plan have already changed greatly, thus, that plan has become less-optimal or totally invalid R g m 2. The svuetu~cof c l m d Imp p m u u planning.
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Distributed process planning (DTPP): Distributed process planning performs both the process and production scheduling simultaneously. It divides the process planning and production scheduling tasks into two phases. The first phase is pre-planning. In this phase. process planning analyzes the job based on product data. The features and feanrre relationships are recognized, and corresponding manufacturing processes are determined. The required machine capabilities are also estimated. The second phase is final-planning. which matches the required job operations with the operation capabilities of the available production resource. The integration occurs at the point when resources are available and the job is required. The process planning and production scheduling are carried out simultaneously. This approach is also referred to as just-in-time process planning. The result is dynamic process and production scheduling constraint by MI time events. The structure of distributed process planning is illustrated in Figure 3 [17]. The proposed distributed process planning approach should be an ideal approach for integration of process planning and production scheduling. However, this approach requires high capacity and capability from both hardware and software. Funhermore. the proposed structure of the distributed process planning had not k n realized yet previously. According to recent literature reviews [23. 241. so far there is IW) such distributed pmcess planning system ~ ~ p o t t e dIn. the following section. we are going to introduce a substantial integrated process planning model which may be the first integrattd process planning and production scheduling model based on the distributed process planing concept.
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_ _ . _ _ _ _ _ _ _ __._____.__._.......... _______._ -...-...-..-______
3. The Integrated Process Planning Model (PPM Before we are going to discuss the proposed integrated process planning model (IPPM). We shall clarify the difference between interfacing and integration. In general. interfacing can be achieved at the result level while integration must be addressed at the task level [18]. In other words, it would be too late to integrate a task when its sub-results are already decided separately. For instance, once the all process plans have been generated (including alternative process plans). the following scheduling function can only be interfacing (selection of alternative plans) but rather not integrated. To achieve truly integration process planning and production scheduling. the integration
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hetween them should be addressed at a much early stage. According to above discussion. the NLPP and CLPP approaches discussed in the previous section cannot be considered as fully integration but rather interfacing process planning md production scheduling. Only the DTPP is a truly integrated approach. The :!hove discussion also indicates that most of current research works are still focused on interfacing rather than integration. Here we are going to introduce an Integrated Process Planning Model tIPPM), which is based on the distributed process planning concept.The model provides the detail information and concrete architecture. illustrated in Figure -1. From Figure 4. one can see that the system consisrs of three modules -process planning module. production scheduling module, which are indicated in dash line blocks, and decision making module indicated in a solid line block in the center of Figure 4. These three modules are integrated in three levels -- preplanning (initial integration) level, pairing planning (decision making) level, and final-planning (functional integration) level. 3.1 Process planning module The process planning module works completely in nine steps. At rhe preplanning level, the module works in thm steps: fealure reasoning. processes recognition. and setups determination. The data of the input to the first step, features reasoning, are from two resources. One is the product model from a design system which represents the geometrical shape of the product. design tolerances. surface requirement. and the propcny of the material. The other one is the equipment information from the shopfloor management database. The communication here between feature analysis and rCsource information is for retrieval equipment information, which encloses all machines, tools, fixtures, and some other auxiliary quipment. The feature reasoning step analyzes the features and feature relationships at first. The features and feanrre relationships are recognized by means of feature relation recognition. In order to select corrected setups. the nxognized features are classified into different orders. The first order features are usually the basic features on which some other features can be generated. The first order features must be manufactured prior to the second order features. as do the pmceeding order features. The concept of the feature orders is the basic process planning rule, which avoids Ihe infeasible of process sequence. For instance. a shaft part is shown in Figure 5. Looking at diameter-3 in Figure 5. if the diameter-3 (cylinder) is considered as first order feature. then. the chamfer-2 and square groove-2 are considered as the second order features. while the external thread is considered as the third order feature. Total feaNre relationship can be represented by means of a feature relation graph (FRG) shown in Figure 6 . Once the features and feature relationships are recognized. the relevant machining processes are selected. The machining processes selection is based on characteristics of the feature and equipment information from the shopfloor. me selection at this level will not concern the real ti= feedback. but that the machines must be existed in the shopfloor. For instaxe, a Slot manufactured by either a mill. or a shaper. or a planer. If the Plamr docs M t exist in the shopfloor, then only (1) milling the s101. and (2) shaping the slot are selected. objective of this step is to provide availability and flexibility for machines selection. The last step of the pre-planning level is setup selection which forms an important issue for integrated process planning and production scheduling. In general. a feasible and detailed process plan should consist of several layers sequentially. i.e. setups -> Pr0~ei.u~ -> o ~ r a t i o n-> Cuts.
It is seldom to split setups by means of each feature and manufacture them on individual machines (although it is possible theoretically). unless it is a mass type production. Here again. the basic process planning rule must be concerned for setup selection. The exua-unnccessarily split setups can only cause the increasing of machine cost and manufacturing lead lime. It docs not make any sense for providing flexibility of process planning and operation sequencing. We propose that the smallest unit used for machine selection is setup. How to select the adequate and optimal setups is based on feature recognition and most importantly on the automated tolerance analysis. We have developed a particular algorithm for automated tolerance analysis and setups selection for the integrated model. Thc driving concept of tolerance analysis is that the looser the operation tolerance obtained the higher the flexibility in machining process (machine tool) selection. In the integrated model. the method of tolerance analysis is applied, and all the tasks of process planninz are integrated with the analysis. In a typical machining process on a NC machine tool, features obtained in one setup are interrelated through programmed nominal coordinates and not based on the actual position of any other feature as datum. This relationship proves that feature tolerances in a setup do not form a tolerance chain and leu the process planner have a wider manufacturing tolerance. By providing wider manufacturing tolerance. jobs require machine tools with higher machine capability. This increases the choice of machine tools IS any machine with lower machine capability can be selected. In effect by using this concept, we will be able to reduce the requiremenu from a machine tool to process this job. This reduction in requirements can be especially felt while scheduling machines on the shopfloor. Tolerance analysis is done in following steps. The data of output from the pre-planning are possible setups. candidate machining processes. and estimated machining and setup times. In fact. all these alternative setups form the alternative process plans. How to select the optimal plan by means of real time feedback is the exactly problem of how to select machine tools. The process planning module in the decision making level works in three steps: machine selection. tooling and fixcuring selection. and exactly time calculation. The selecKiOn machines are based on the real time feedback from scheduling. The real time feedback informalion provides which machines, and when they will be available. According to the selected Setups. machining processes. and estimated times. Ihe appropriate machines are selected. and rhc exactly machining and setup times are calculated immediately. In the decision making model. scheduling constraints arc involved rather than process planning rules. The emphasis of this level focus on decision making and scheduling modules which we will discuss lam. When the system works in this level, the real integration occurs. The data of the output from this level are selected datum and setups, processes. machines. tools and fixtures, inspection and auxiliary equipment. exact machining and setup times. as well as pcmnncl information. The final planning level works in three steps: operational tolerance analysis, operation sequencing, and overall time and cost calculation. The detailed process planning and the detailed scheduling are performed simultaneously in this level. Once the overall time is provided. the detailed scheduling simulales the shopfloor situation and gain the available machines for the next time window. Simultaneously. the output data such as the detailed process plans, NC path, requirement of machines, tools. fixtures. personncl. as well material handling information are sent to the shopfloor.
3.2 Production Scheduling Module The production scheduling module works in three steps. respectively with one step for each level. In the pre-planning level, the production scheduling module provides available equipment in the next time window. In the decision making level, the available equipment is matched with the requirement of the setups and candidate machining processes. To carry out an optimal scheduling and fulfilled with process planning constraints, a comprehensive decision making module, introduced later, is required. The decision making level wrries out the information about machines selection for the jobs and the setup and machining times. In the following step. the derail scheduling is performed simultaneously with detail process planning. Since most scheduling decision strategies are built in the decision making module here, the main function of detail scheduling is to simulate the shopfloor status and calculate the start and finish times of the jobs. Priority rules for scheduling arc changed based on the calculation of system performance. thereby generating dynamic scheduling rules. What allows the module to simulate the shopfloor in a more realistic term. Performance of the shopfloor can be enhanced by computing rules individually for every machine and for every time window. As a matter of fact. Ihc funhcr performance of the production scheduling module should be drawn iN0 the shopfloor control. which has not been concerned very well in this papcr. The proposed production scheduling model fits well to a recommended control model by Bilberg and Alting [251.
3.3 Dedsion Making Module The decision making module is the central element of the architecture and performs by means of real time information. Traditionally. process planning and production scheduling have different constrainu. respectively. by means of their strategy criteria. while process planning concerns more technical problems such as feasibility of machines. lower cost of tools and machines. scheduling concerns timing problems such as due dates. and minimum waiting rim, sometimes these criteria conflict with each other. The conflict is the difficult problem for integration of process planning and scheduling. The previously discussed NLPP and CLPP approaches in this papcr evade the problems but w l t in a functional interface
Figure 5. An crsmplc of slun pan
Figure 6. F a w e Rclarion Gnph fcu h e MIpul.
rather than functional integration. To reach the real integration at the task level, the constraints from both process planning and production scheduling must be concerned simultaneously. Here the most importance is how to select machincs based on real time shopfloor status. In the proposed integrated model. the issue has been resolved homogeneously. 3.3.1 Real-time machine status The information about real-time machine Status in the shopfloor is stored in a database. The database is called Real-Time Machine Database constructed base on the task and time assignment of the machines. The database should have information about all the machines in the shopfloor. From the database it must be possible for us to kmw when a specific machine is available (waiting to machine some parts), when it will not be available (some parts are going to be machined) . Table I shows the content of a Real-Time Machine Database, which has two columns. The ‘MACHINE” column indicates a specific machine. The “TIME TABLE’ column indicates real-time status of that specific machine. The two numbers within the parenthesis indicate the period of times that the machine is idle (available).
rable 1 Real-time machine database ( I ) .
From Table 1, we can know that at time 7 to 18. and a h r time 22, machine MILL02 is available. while at other time it is not available. The RealTime Machine Database should be well maintained to reflect actual status of the shopfloor. What we concern is the current and future status of a machine. So. as the time WCN by, information about past Status of machines can be eliminated from the database. Based on the real-time shopfloor status. one suitable process plan can be selected for a part from the alternative process plans generated in the pre-process planning stage.
515
3.3.2 Process plan selection Once a pan has been decided to be manufactured. we need to schedule the setups to machines. As we mentioned in the pre- process planning stage. there arc different setups to manUfaCture the pan. thcse setups can have different sequence. To select setups and their sequence, one always needs to have certain criterion. though different companies may have different criterion. The basic criteria are: minimize manufacturing cost. and minimize manufacturinx lead time. The problem of minimize manufacturing lead time can be solved in two steps. First. list all the possible process plans (including different setup sequence). Then. base on the real-time machine status. calculate their manufacturing lead time. Select the process plan with least manufacturing lead time. Setup can be assigned to machines in the same time. In the pre-planning stage, alternative setups are given in form of setup specification graph (SSG) as shown in Figure 7. Here.a frame-based approach is used to represent the SSG in the decision making module, as shown in Table
x,
1. if process plan 'p' is selected
=
[ 0. otherwise Y,
=
1
1. if setup
0. otherwise Coefficients: period of time available on machine "m". R, tmp time for machine "m" to perform setup "s' of process plan 'p". idle time for setup *I' of process plan 'p' using machine "m". T,,
[ 1. if a machine is assigned to setup "s' of process plan 'p' b,, = {
2.
0.otherwise
Function:
Table 2. Setup specification.
minimize:
s1
I
0
s2
I
0
1
2
1
3
I B
1
5
I s 4
's" is performed using machine 'm.
I I
- C ( t M p + T m S -pY) ~ , * x
F
rsp
subject to:
Fxs-' t,,piR,
The "PRECEDENCE SETUP" column in the table means the setup in the correspond row can k manufactud only after the setup(s) in the column have already been manufactured. For example, the "S6" in row 7, column 2. means setup 7 can be manufactured only after setup 6 has been manufactured. While a blank means the setup can be performed instantly. The "EXCLUSIVE SETUP" column in the table means if the setup in the correspond row is selected, the setup(s) indicated in the column cannot be selected anymore. For example, the "S6 AND S7" in row 4 column 3, means if setup 4 is selected. setup 6 and setup 7 cannot k selected.
(3.1)
(3.2)
V m
(3.31
The objective function (3.1) minimize manufacturing lead time. The constraint of the optimization added are given by (3.2). (3.3). and (3.4). Constraint (3.2) guarantee that only one process plan is selected. Constraint (3.3) ensure that the machine capacity is not violated. Constraint (3.4) ensure that a11 the setups in the selected process plan are performed on one of the available machine
R g m 7. Srmp spcifiuuon gnph (SSG)
The two restrictions maintain the relationship shown in the setup specification graph in Figure 7. All Ihe possible process plans (including different setup sequence) can be found from the table. To list all thcsc process plans. we need to represent it using a tree stmcture. T k algorithm is as the following: Step 1. Find the setups whose "PRECEDENCE SETUP" content is blank. Theses setups form the first layer of the tree structure. Assume there are ENPS ss,. s&. ...ss,. Step 2. For i from 1 to A. do the following: (a). Change the content of 'PRECEDENCE SETUP" into "selected". (b). Search 'PRECEDENCE SETUP" column, if setup SS, appear. delete it. (c). Search "PRECEDENCE SETUP" column again, if in content is blank, check its 'EXCLUSIVE SETUP" content, if SS,is not in it, this setup is a child node of SS, Step 3. The setups found in step 2 form the second layer of the uee. Repeat the same procedure as step 2. third layer of the trce can k formed. The procedure is npeated until no more layer can be formed. Using this algorithm. the tree structure for Table 2 can be formed (shown in Figure 8). Every path of thc tree is a process plan. From the vee StruCNTC. we can list all the possible process plans (including different setup sequcncC). Now, real-time machine status nccd to be considered in order to select the process plan which can be performed with least manufacturing lead time. A mathematics model can be built. Indexing sew p = 1. 2. ...P s = I, 2 , . . s p m = 1, 2 . . . . q p plan 'p'. Decision variable:
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prccess plans. setups for process plan "p", machines that can perform setup "5" of process
Figure 8. Tree structure for process plans specification.
The optimum process plan can always be selected based on rhe mathematic model. An algorithm is given in the following: Step 1, Estimate the time T when the part is going to be manufactured. For every process plan "p", do the following: (a). i = 1. 6 = T. (b) From real-time machine database. find machines that can perform setup 5,. calculate their finish time 1., (c). Find machine M which t,, = MIN[t,J. m = I , 2 ,... assign setup s, to machine M. b = tM,. (d). If i = sp. go to step 2: else. i = i + 1. go to (b). Step 2. Find process plan pp, which k = MI"$]. p = 1. 2. ...P.
Q.
Process plan pp is selected. and the machine assignment for process plan pp is updated. An example is illustrated in the following: Assume there are three process plans to manufacture pan Q. The process plans are: 5: + s3 P, = 5, P, = 5: -I s, + S? P, = 5, + 5, Assume the part is going to be released to shopflwr at time 0. For each process plan, the least manufacturing lead rime can be calculated. For process plan pI, xtup s1 can only be performed in machinc m,.m, will be available at time 2. s, need to be performed using m, in 10 time unit. so it will be finished at time 12. Then setup s; can be performed either use machine m2 or m,. at time 12. m2 and m, are all available. if use m2 it will finish at time 20 (12 + 8). while use m,. at time 18 (12 + 6) it will be finished. so we assign s2 to m,. the finish time is 18. Now consider Setup P,. it can be performed using machine m,, m,. or mb. Machine m, and m, are both available at time 18. while m5 will not be available until lime 23. So. if use m,. the finish time is 25 (18 + 7); use m,. hfinish time is 30 (23 7); use m,. the finish time is 30 (18 + 12). Thus we assign s1 to m,. the manufacturing lead time tl = 2 5 . This procedure is represented in Table 3. In the table, bold letter means that machine is selected. the finish time is indicated using underline. T k machine assignment and manufacturing lead time for process plan p. and p, are represented in Table 4 and Table 5 .
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Table 3. Machine assignment and manufacturing lead time for process plan 1,
m, (18
+7
=
W m, (23 + 7
=
Table 4. Machine assignment and manufacturing lead time for process plan 2. machine assignment and finish time
setup _ I -
SI
S?
+ 10 = 1l) m, (17 + 7 = m, (23 + 8 = 31) m,(14 + 12 =26) m, (2
m, (24
+ 8 = 32)
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m, (24
+6 =
1p)
From Table 4 and Table 5 . we can see if use process plan p:, the manufacturing lead time will be 30. while usc process plan p,. the IIlanUfaCturing lead time will be 27. Table 5 . Machine assignment and manufacturing lead time for process plan 3.
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achine assignment and finish time
m, (25
+ 3 = 28)
m, (22
+5
21) I
Table 6. Modified real-time m h i n e database.
=
Since use process plan pI. the manufacturing lead time is the least (25). so process plan pI is selected. Setup s, assigned to machine m,. machining time
from time 2 to time 12. Setup s: assigned to machine m,. machining time from time I?. to time 18. Setup s, assigned to machine m,. machining time from time 18 to time 2 5 . The information about the machine assignment need to be updated to the Real-Time Machine Database, since it is going to change the real-time shopfloor status. The real-time status must be modified 3s shown in Table 6. In rhis example. one can also see if ignore the ral-time shopfloor status. we will select process plan p,. U x machine m: for setup s, and machine m, for setup s!. Because process plan p, has only two setups and the least machining time (15 + 3 = 18). Bur. acrually. if process plan p, is selected. the manufacturing lead time will be 27 (as indicated in Table 5 ) . 2 time unit more than using process plan pI. So. il is very imponant to consider real-time shopfloor status when selecring process plan and assign setups to machines. 4. Conclusion
Integration of process planning and scheduling has been recognized as playing an imponant role to form the integrated manufacturing. In this paper. we systematically discussed the issue of integration of process planning and production scheduling. Three integrated approaches (1) nonlinear (alternative or flexible) process planning. ( 2 ) closed loop (dynamic or real time) process planning, and (3) distributed (just-in-time) process planning have been discussed in terms of their advantages and disadvantages. Basis of the discussion, an integrated process planning model which is hased on distributed process planning concept is proposed. The proposed IPPM model is a framework to extend the concept of the distributed process planning and demonstrate the feasibility. The architecture and the three imponant modules - process planning module, production scheduling module, and decision making module are discussed in detail. While the process planning and decision making modules have been developed, the production scheduling module is in the progress of completion. This paper will contribute to the research area of integrated process planning and job shop scheduling. The decision making module discussed in the paper plays an important role for integration of proccss planning and production scheduling. To increase the capacity and capability of the decision making module,a combination of frame-based approach with neural networks and funy logic scheme is undertaken as the next research step of the project. 5. Acknowledgements This research was supported in pan by the National Science Foundation under the Grant contract #DDM-9211657.
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Control. Proceedings of the 22nd ClRP lntemtional Seminar on Manufacturing Systems. University of Twente. Ensch.de, Netherlands. 1990. I151 Inui. M..Suzuki, H..Kimura. F.. and Sata, T.. 1987. "Extending Process Planning Capabilities with Dynamic Manipulation of Product Models, " Proceedings of the 19th CIRP International Seminar on Manufacturing Systems. The Pennsylvania State University, 1987. [16] Larun. N.E.. and Alting. L.. 1990. Requirements 10 Scheduling Simulation Systems, the proceedings of the 1990 Summer Computer Simulation Conference. Galgary. Alberta, Canada. July. 16-18. 1990. [17] Larxn. N.E.. and Alting. L., 1990. Simultaneous Engineering Within Process- and Production planning. 1990 Pacific Conference on Manufacturing, Australia, December. 1990 (181 Ham. I. and Lu, C . - Y . . 1988. "Computer-Aided Process Planning: The Present and the Future", Annals of the CIRP Vol. 37/2/1988. ppl-11. [I91 Lin. C-W. R.. and Wyrk. R.. 1989. Generating Alternative Computer Aided Process Plans, IIE Integrated Systems Conference & Society for Integrated Manufacturing Conference. pp438-143. Nov. 1989. [ZO] Sight N.,and Mohanty. B.K.. 1991. A Fuzzy approach to multi-objective routing problem with applications to process planning in manufacturing systems. Internationat Journal of Production Research, vo1.29. [n0.6. pp1161-1170, 1991. [21] Bhaskaran, K., 1990, Process Planning Selection, International Journal of Production Research, vo1.28. [no.& pp1527-1539. 1990 [22] Larsen, N.E.. and Clausen, J . , 1992. Applied Methods for Integration of Process Planning and Production Control, Proceedings of Manufacturing International'9Z. ASME, Dallas, pp 349-364, 1992. [23] Mallur. S., 1992. Functional Integration of Process Planning and Job shop Scheduling. Master thesis in the Depanment of Industrial Engimring. Texas Tech University. April, 1992 [24] Huang. H.D.. 1992. Decision making module for integrating process planning and scheduling, Working report in the Depanment of Industrial Engineering, Texas Tech University. June, 1992 [25] Bilberg. A . , and Alting. L., 1991. When Simulation T a k a Control. Journal of Manufacturing Systems. vol. 10. no.3. pp179-193. 1991
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S U M M A R Y: Functional integration has taken the foreground in the current manufacturing system development. Process planning and job shop scheduling are two main manufacturing functions involved in shopfloor activities. An integrated process planning model (IPPM) is introduced in this paper. The two functions (process planning and job shop scheduling) are truly integrated by means of distributed approach which is different from current nonlinear and alternative process planning approaches. In this paper, the two functions are integrated at task stage, while the nonlinear and alternative approaches are taken place at result stage which cannot b e considered as real integration but rather interface. The introduced IPPM works in three levels, based on manufacturing resource availability and real time feedback from shop floor. they are. pre-planning, pairing planning, and final planning. The IPPM consists of three modules, introduced in detailed in the paper, they are: process planning module, production scheduling module, and decision making module. An example is provided to illustrate the model. This paper is to b e considered as a contribution to the research efforts in process planning and integrated manufacturing Kevwords: CAPP, Production Scheduling, Functional Integration. 1. Introduction
Computer aided process planning (CAPP) has been recognized as playing a key role in computer integrated manufacturing (CIM). In the last thrn decades, many CAPP systems have been developcd in terms of variant or generative approaches. To realize the expected integrated manufacturing. considerable effort has k e n made to integrate varieties of manufacturing functions from design phase to shopfloor control. Especially, the integration of process planning with design function has received remarkable progress. Whik many different geometrical models and feature-based techniques have been developed and applied in industry. the integration of process planning with production scheduling has not received some satisfactory results. In this paper, the issue of inregration of p m s s planning with production scheduling is discussed in detail. Several current approaches 0 t h used for integration of process planning with production scheduling are introduced. Finally, an integrated process planning model is provided in the paper. The question of the integration of process planning and production scheduling arose m the middle of the 1980s [14].After then. the issue has been frequently addressed in literature. [S-81. and several prototype integrated models have been reported. RTCAPP [9-10]. DPP [11-12], FLAXPLAN [13-141. XMAPP [lS]. Although these models have mt solved the problem completely. they are good enough to spark light for trends of future research. Process planning is such a complex function that it was too diffzult to concern the impact on the overall performance of the production aspects by a process planner, even though it was a sophisticated planner. The early automated process planning systems could not address the problem either, b c w w the capacity of hardware and software was limited, and most research attentions were focused on stand alone system and integration with design aspect. Since process planning and scheduling are performed separately, and process plans are generated without consideration of job shop status information, many problems arise within the manufacturing environment.
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Process p l m r s assume unlimited resources on the shopfloor and an idle factory. They plan at the most recommended alternate processes. and desirable machines are repeatedly selected by various process planners. Scheduling follows the process planning stage. Thus. when the process is going to be carried out. some constraints will be encountered. making the generated "optimal" process plans infeasible or less optimal in a scheduling engineer's view.
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The throughput of orders in a workshop often suffers from disruptions caused by stochastic bottlenecks. non-availability of tools and personnel. or breakdown of equipment. A readily generated schedule becomes invalid and has to be regenerated. Rescheduling is mostly carried out by improvisation and can result in long throughput times. A large number of process plans perhaps cannot be executed and have to be altered.
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0th process planning and scheduling have conflicting objectives. Process planning emphasizes the technological requirements of a task, while scheduling involves the timing aspects of it. Usually, single criterion optimization is considered for both process planning and scheduling. However. in a real production environment. more than one criterion necd to be considered simultaneously. and their relative importance in decision making change dynamically due to the dynamic shopfloor condition.
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Due to the complexity of the real production environment, neither the process plans nor the planned schedules arc truly followed at Ihe shopfloor. Without the feedback from the shopfloor, it becomes difficult to measure the quality or valve of a plan for future enhancement.
2. Approaches of Integration
Integration of process planning and production scheduling is essential to achieve eventually integrated manufamring and dismiss the traditional sequential manufacturing function. So far. several approaches for integration of process planning and production scheduling have been discussed in terms of nonlinear process planning. flexible process planning. closed loop process planning, dynamic process planning. alternative process planning. as well as just-in-time process planning, [12. 14. 16-22]. With studying of these reports. examination of these different terms, and analysis of the structure and information flow of the proposals. three types of general integration approaches can be categorized.
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Nonlinear process planning (NLPP): The NLPP makes all possible (or in other word alternative) plans for each part before it enters into shopfloor. The NLPP is still based on a static shopfloor situation. All these possible plans are ranked according to process planning criteria and stored in process planning database. The first priority plan is always ready for submission when the job is rquired. The scheduling makes the real decision. If the first priority plan is not suitable for Ihc current SUNS of tk shopfloor. the second priority plan will be provided to the scheduling, This procedure is repeated until a suitable plan is determined. The StrUCNlZ of NLPP is illustrated in Figure I . A typical example of NLPP system named FLEXPLAN has been developed in the University of Hanover. Germany [131.
Closed loop process planning (CLPP): The CLPP generates plans by meam of a dynamic feedback from production scheduling. The p m s planning mechanism generates process plans based on available resources. Production scheduling tells process planning what machms are available in the shopfloor for r k coming job, so hat every plan is feasible with respect to the current availability of production facilities. Real time status is crucial for thc CLPP. Since a real time dynamic feedback is required for the CLPP, it is also referred to as real time process planning or dynamic process planning. The sWcNre of the CLPP is illustrated in Figure 2 . Two prntotypc CLPP systems. DPP [ 11121, and RTCAPP [9-101 have been r e p o d .
Even with considering the shopfloor condition. the time delay between thc planning phase and the planning execution phase will cause trouble. Due to the dynamic n a ~ r of e a production environment, it is very likely that when a design is ready to be manufactured the consmints that were used in generating the plan have already changed greatly, thus, that plan has become less-optimal or totally invalid R g m 2. The svuetu~cof c l m d Imp p m u u planning.
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Distributed process planning (DTPP): Distributed process planning performs both the process and production scheduling simultaneously. It divides the process planning and production scheduling tasks into two phases. The first phase is pre-planning. In this phase. process planning analyzes the job based on product data. The features and feanrre relationships are recognized, and corresponding manufacturing processes are determined. The required machine capabilities are also estimated. The second phase is final-planning. which matches the required job operations with the operation capabilities of the available production resource. The integration occurs at the point when resources are available and the job is required. The process planning and production scheduling are carried out simultaneously. This approach is also referred to as just-in-time process planning. The result is dynamic process and production scheduling constraint by MI time events. The structure of distributed process planning is illustrated in Figure 3 [17]. The proposed distributed process planning approach should be an ideal approach for integration of process planning and production scheduling. However, this approach requires high capacity and capability from both hardware and software. Funhermore. the proposed structure of the distributed process planning had not k n realized yet previously. According to recent literature reviews [23. 241. so far there is IW) such distributed pmcess planning system ~ ~ p o t t e dIn. the following section. we are going to introduce a substantial integrated process planning model which may be the first integrattd process planning and production scheduling model based on the distributed process planing concept.
n 113pn-l
F!=J Y
h -
_ _ . _ _ _ _ _ _ _ __._____.__._.......... _______._ -...-...-..-______
3. The Integrated Process Planning Model (PPM Before we are going to discuss the proposed integrated process planning model (IPPM). We shall clarify the difference between interfacing and integration. In general. interfacing can be achieved at the result level while integration must be addressed at the task level [18]. In other words, it would be too late to integrate a task when its sub-results are already decided separately. For instance, once the all process plans have been generated (including alternative process plans). the following scheduling function can only be interfacing (selection of alternative plans) but rather not integrated. To achieve truly integration process planning and production scheduling. the integration
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hetween them should be addressed at a much early stage. According to above discussion. the NLPP and CLPP approaches discussed in the previous section cannot be considered as fully integration but rather interfacing process planning md production scheduling. Only the DTPP is a truly integrated approach. The :!hove discussion also indicates that most of current research works are still focused on interfacing rather than integration. Here we are going to introduce an Integrated Process Planning Model tIPPM), which is based on the distributed process planning concept.The model provides the detail information and concrete architecture. illustrated in Figure -1. From Figure 4. one can see that the system consisrs of three modules -process planning module. production scheduling module, which are indicated in dash line blocks, and decision making module indicated in a solid line block in the center of Figure 4. These three modules are integrated in three levels -- preplanning (initial integration) level, pairing planning (decision making) level, and final-planning (functional integration) level. 3.1 Process planning module The process planning module works completely in nine steps. At rhe preplanning level, the module works in thm steps: fealure reasoning. processes recognition. and setups determination. The data of the input to the first step, features reasoning, are from two resources. One is the product model from a design system which represents the geometrical shape of the product. design tolerances. surface requirement. and the propcny of the material. The other one is the equipment information from the shopfloor management database. The communication here between feature analysis and rCsource information is for retrieval equipment information, which encloses all machines, tools, fixtures, and some other auxiliary quipment. The feature reasoning step analyzes the features and feature relationships at first. The features and feanrre relationships are recognized by means of feature relation recognition. In order to select corrected setups. the nxognized features are classified into different orders. The first order features are usually the basic features on which some other features can be generated. The first order features must be manufactured prior to the second order features. as do the pmceeding order features. The concept of the feature orders is the basic process planning rule, which avoids Ihe infeasible of process sequence. For instance. a shaft part is shown in Figure 5. Looking at diameter-3 in Figure 5. if the diameter-3 (cylinder) is considered as first order feature. then. the chamfer-2 and square groove-2 are considered as the second order features. while the external thread is considered as the third order feature. Total feaNre relationship can be represented by means of a feature relation graph (FRG) shown in Figure 6 . Once the features and feature relationships are recognized. the relevant machining processes are selected. The machining processes selection is based on characteristics of the feature and equipment information from the shopfloor. me selection at this level will not concern the real ti= feedback. but that the machines must be existed in the shopfloor. For instaxe, a Slot manufactured by either a mill. or a shaper. or a planer. If the Plamr docs M t exist in the shopfloor, then only (1) milling the s101. and (2) shaping the slot are selected. objective of this step is to provide availability and flexibility for machines selection. The last step of the pre-planning level is setup selection which forms an important issue for integrated process planning and production scheduling. In general. a feasible and detailed process plan should consist of several layers sequentially. i.e. setups -> Pr0~ei.u~ -> o ~ r a t i o n-> Cuts.
It is seldom to split setups by means of each feature and manufacture them on individual machines (although it is possible theoretically). unless it is a mass type production. Here again. the basic process planning rule must be concerned for setup selection. The exua-unnccessarily split setups can only cause the increasing of machine cost and manufacturing lead lime. It docs not make any sense for providing flexibility of process planning and operation sequencing. We propose that the smallest unit used for machine selection is setup. How to select the adequate and optimal setups is based on feature recognition and most importantly on the automated tolerance analysis. We have developed a particular algorithm for automated tolerance analysis and setups selection for the integrated model. Thc driving concept of tolerance analysis is that the looser the operation tolerance obtained the higher the flexibility in machining process (machine tool) selection. In the integrated model. the method of tolerance analysis is applied, and all the tasks of process planninz are integrated with the analysis. In a typical machining process on a NC machine tool, features obtained in one setup are interrelated through programmed nominal coordinates and not based on the actual position of any other feature as datum. This relationship proves that feature tolerances in a setup do not form a tolerance chain and leu the process planner have a wider manufacturing tolerance. By providing wider manufacturing tolerance. jobs require machine tools with higher machine capability. This increases the choice of machine tools IS any machine with lower machine capability can be selected. In effect by using this concept, we will be able to reduce the requiremenu from a machine tool to process this job. This reduction in requirements can be especially felt while scheduling machines on the shopfloor. Tolerance analysis is done in following steps. The data of output from the pre-planning are possible setups. candidate machining processes. and estimated machining and setup times. In fact. all these alternative setups form the alternative process plans. How to select the optimal plan by means of real time feedback is the exactly problem of how to select machine tools. The process planning module in the decision making level works in three steps: machine selection. tooling and fixcuring selection. and exactly time calculation. The selecKiOn machines are based on the real time feedback from scheduling. The real time feedback informalion provides which machines, and when they will be available. According to the selected Setups. machining processes. and estimated times. Ihe appropriate machines are selected. and rhc exactly machining and setup times are calculated immediately. In the decision making model. scheduling constraints arc involved rather than process planning rules. The emphasis of this level focus on decision making and scheduling modules which we will discuss lam. When the system works in this level, the real integration occurs. The data of the output from this level are selected datum and setups, processes. machines. tools and fixtures, inspection and auxiliary equipment. exact machining and setup times. as well as pcmnncl information. The final planning level works in three steps: operational tolerance analysis, operation sequencing, and overall time and cost calculation. The detailed process planning and the detailed scheduling are performed simultaneously in this level. Once the overall time is provided. the detailed scheduling simulales the shopfloor situation and gain the available machines for the next time window. Simultaneously. the output data such as the detailed process plans, NC path, requirement of machines, tools. fixtures. personncl. as well material handling information are sent to the shopfloor.
3.2 Production Scheduling Module The production scheduling module works in three steps. respectively with one step for each level. In the pre-planning level, the production scheduling module provides available equipment in the next time window. In the decision making level, the available equipment is matched with the requirement of the setups and candidate machining processes. To carry out an optimal scheduling and fulfilled with process planning constraints, a comprehensive decision making module, introduced later, is required. The decision making level wrries out the information about machines selection for the jobs and the setup and machining times. In the following step. the derail scheduling is performed simultaneously with detail process planning. Since most scheduling decision strategies are built in the decision making module here, the main function of detail scheduling is to simulate the shopfloor status and calculate the start and finish times of the jobs. Priority rules for scheduling arc changed based on the calculation of system performance. thereby generating dynamic scheduling rules. What allows the module to simulate the shopfloor in a more realistic term. Performance of the shopfloor can be enhanced by computing rules individually for every machine and for every time window. As a matter of fact. Ihc funhcr performance of the production scheduling module should be drawn iN0 the shopfloor control. which has not been concerned very well in this papcr. The proposed production scheduling model fits well to a recommended control model by Bilberg and Alting [251.
3.3 Dedsion Making Module The decision making module is the central element of the architecture and performs by means of real time information. Traditionally. process planning and production scheduling have different constrainu. respectively. by means of their strategy criteria. while process planning concerns more technical problems such as feasibility of machines. lower cost of tools and machines. scheduling concerns timing problems such as due dates. and minimum waiting rim, sometimes these criteria conflict with each other. The conflict is the difficult problem for integration of process planning and scheduling. The previously discussed NLPP and CLPP approaches in this papcr evade the problems but w l t in a functional interface
Figure 5. An crsmplc of slun pan
Figure 6. F a w e Rclarion Gnph fcu h e MIpul.
rather than functional integration. To reach the real integration at the task level, the constraints from both process planning and production scheduling must be concerned simultaneously. Here the most importance is how to select machincs based on real time shopfloor status. In the proposed integrated model. the issue has been resolved homogeneously. 3.3.1 Real-time machine status The information about real-time machine Status in the shopfloor is stored in a database. The database is called Real-Time Machine Database constructed base on the task and time assignment of the machines. The database should have information about all the machines in the shopfloor. From the database it must be possible for us to kmw when a specific machine is available (waiting to machine some parts), when it will not be available (some parts are going to be machined) . Table I shows the content of a Real-Time Machine Database, which has two columns. The ‘MACHINE” column indicates a specific machine. The “TIME TABLE’ column indicates real-time status of that specific machine. The two numbers within the parenthesis indicate the period of times that the machine is idle (available).
rable 1 Real-time machine database ( I ) .
From Table 1, we can know that at time 7 to 18. and a h r time 22, machine MILL02 is available. while at other time it is not available. The RealTime Machine Database should be well maintained to reflect actual status of the shopfloor. What we concern is the current and future status of a machine. So. as the time WCN by, information about past Status of machines can be eliminated from the database. Based on the real-time shopfloor status. one suitable process plan can be selected for a part from the alternative process plans generated in the pre-process planning stage.
515
3.3.2 Process plan selection Once a pan has been decided to be manufactured. we need to schedule the setups to machines. As we mentioned in the pre- process planning stage. there arc different setups to manUfaCture the pan. thcse setups can have different sequence. To select setups and their sequence, one always needs to have certain criterion. though different companies may have different criterion. The basic criteria are: minimize manufacturing cost. and minimize manufacturinx lead time. The problem of minimize manufacturing lead time can be solved in two steps. First. list all the possible process plans (including different setup sequence). Then. base on the real-time machine status. calculate their manufacturing lead time. Select the process plan with least manufacturing lead time. Setup can be assigned to machines in the same time. In the pre-planning stage, alternative setups are given in form of setup specification graph (SSG) as shown in Figure 7. Here.a frame-based approach is used to represent the SSG in the decision making module, as shown in Table
x,
1. if process plan 'p' is selected
=
[ 0. otherwise Y,
=
1
1. if setup
0. otherwise Coefficients: period of time available on machine "m". R, tmp time for machine "m" to perform setup "s' of process plan 'p". idle time for setup *I' of process plan 'p' using machine "m". T,,
[ 1. if a machine is assigned to setup "s' of process plan 'p' b,, = {
2.
0.otherwise
Function:
Table 2. Setup specification.
minimize:
s1
I
0
s2
I
0
1
2
1
3
I B
1
5
I s 4
's" is performed using machine 'm.
I I
- C ( t M p + T m S -pY) ~ , * x
F
rsp
subject to:
Fxs-' t,,piR,
The "PRECEDENCE SETUP" column in the table means the setup in the correspond row can k manufactud only after the setup(s) in the column have already been manufactured. For example, the "S6" in row 7, column 2. means setup 7 can be manufactured only after setup 6 has been manufactured. While a blank means the setup can be performed instantly. The "EXCLUSIVE SETUP" column in the table means if the setup in the correspond row is selected, the setup(s) indicated in the column cannot be selected anymore. For example, the "S6 AND S7" in row 4 column 3, means if setup 4 is selected. setup 6 and setup 7 cannot k selected.
(3.1)
(3.2)
V m
(3.31
The objective function (3.1) minimize manufacturing lead time. The constraint of the optimization added are given by (3.2). (3.3). and (3.4). Constraint (3.2) guarantee that only one process plan is selected. Constraint (3.3) ensure that the machine capacity is not violated. Constraint (3.4) ensure that a11 the setups in the selected process plan are performed on one of the available machine
R g m 7. Srmp spcifiuuon gnph (SSG)
The two restrictions maintain the relationship shown in the setup specification graph in Figure 7. All Ihe possible process plans (including different setup sequence) can be found from the table. To list all thcsc process plans. we need to represent it using a tree stmcture. T k algorithm is as the following: Step 1. Find the setups whose "PRECEDENCE SETUP" content is blank. Theses setups form the first layer of the tree structure. Assume there are ENPS ss,. s&. ...ss,. Step 2. For i from 1 to A. do the following: (a). Change the content of 'PRECEDENCE SETUP" into "selected". (b). Search 'PRECEDENCE SETUP" column, if setup SS, appear. delete it. (c). Search "PRECEDENCE SETUP" column again, if in content is blank, check its 'EXCLUSIVE SETUP" content, if SS,is not in it, this setup is a child node of SS, Step 3. The setups found in step 2 form the second layer of the uee. Repeat the same procedure as step 2. third layer of the trce can k formed. The procedure is npeated until no more layer can be formed. Using this algorithm. the tree structure for Table 2 can be formed (shown in Figure 8). Every path of thc tree is a process plan. From the vee StruCNTC. we can list all the possible process plans (including different setup sequcncC). Now, real-time machine status nccd to be considered in order to select the process plan which can be performed with least manufacturing lead time. A mathematics model can be built. Indexing sew p = 1. 2. ...P s = I, 2 , . . s p m = 1, 2 . . . . q p plan 'p'. Decision variable:
516
prccess plans. setups for process plan "p", machines that can perform setup "5" of process
Figure 8. Tree structure for process plans specification.
The optimum process plan can always be selected based on rhe mathematic model. An algorithm is given in the following: Step 1, Estimate the time T when the part is going to be manufactured. For every process plan "p", do the following: (a). i = 1. 6 = T. (b) From real-time machine database. find machines that can perform setup 5,. calculate their finish time 1., (c). Find machine M which t,, = MIN[t,J. m = I , 2 ,... assign setup s, to machine M. b = tM,. (d). If i = sp. go to step 2: else. i = i + 1. go to (b). Step 2. Find process plan pp, which k = MI"$]. p = 1. 2. ...P.
Q.
Process plan pp is selected. and the machine assignment for process plan pp is updated. An example is illustrated in the following: Assume there are three process plans to manufacture pan Q. The process plans are: 5: + s3 P, = 5, P, = 5: -I s, + S? P, = 5, + 5, Assume the part is going to be released to shopflwr at time 0. For each process plan, the least manufacturing lead rime can be calculated. For process plan pI, xtup s1 can only be performed in machinc m,.m, will be available at time 2. s, need to be performed using m, in 10 time unit. so it will be finished at time 12. Then setup s; can be performed either use machine m2 or m,. at time 12. m2 and m, are all available. if use m2 it will finish at time 20 (12 + 8). while use m,. at time 18 (12 + 6) it will be finished. so we assign s2 to m,. the finish time is 18. Now consider Setup P,. it can be performed using machine m,, m,. or mb. Machine m, and m, are both available at time 18. while m5 will not be available until lime 23. So. if use m,. the finish time is 25 (18 + 7); use m,. hfinish time is 30 (23 7); use m,. the finish time is 30 (18 + 12). Thus we assign s1 to m,. the manufacturing lead time tl = 2 5 . This procedure is represented in Table 3. In the table, bold letter means that machine is selected. the finish time is indicated using underline. T k machine assignment and manufacturing lead time for process plan p. and p, are represented in Table 4 and Table 5 .
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Table 3. Machine assignment and manufacturing lead time for process plan 1,
m, (18
+7
=
W m, (23 + 7
=
Table 4. Machine assignment and manufacturing lead time for process plan 2. machine assignment and finish time
setup _ I -
SI
S?
+ 10 = 1l) m, (17 + 7 = m, (23 + 8 = 31) m,(14 + 12 =26) m, (2
m, (24
+ 8 = 32)
-
m, (24
+6 =
1p)
From Table 4 and Table 5 . we can see if use process plan p:, the manufacturing lead time will be 30. while usc process plan p,. the IIlanUfaCturing lead time will be 27. Table 5 . Machine assignment and manufacturing lead time for process plan 3.
-
achine assignment and finish time
m, (25
+ 3 = 28)
m, (22
+5
21) I
Table 6. Modified real-time m h i n e database.
=
Since use process plan pI. the manufacturing lead time is the least (25). so process plan pI is selected. Setup s, assigned to machine m,. machining time
from time 2 to time 12. Setup s: assigned to machine m,. machining time from time I?. to time 18. Setup s, assigned to machine m,. machining time from time 18 to time 2 5 . The information about the machine assignment need to be updated to the Real-Time Machine Database, since it is going to change the real-time shopfloor status. The real-time status must be modified 3s shown in Table 6. In rhis example. one can also see if ignore the ral-time shopfloor status. we will select process plan p,. U x machine m: for setup s, and machine m, for setup s!. Because process plan p, has only two setups and the least machining time (15 + 3 = 18). Bur. acrually. if process plan p, is selected. the manufacturing lead time will be 27 (as indicated in Table 5 ) . 2 time unit more than using process plan pI. So. il is very imponant to consider real-time shopfloor status when selecring process plan and assign setups to machines. 4. Conclusion
Integration of process planning and scheduling has been recognized as playing an imponant role to form the integrated manufacturing. In this paper. we systematically discussed the issue of integration of process planning and production scheduling. Three integrated approaches (1) nonlinear (alternative or flexible) process planning. ( 2 ) closed loop (dynamic or real time) process planning, and (3) distributed (just-in-time) process planning have been discussed in terms of their advantages and disadvantages. Basis of the discussion, an integrated process planning model which is hased on distributed process planning concept is proposed. The proposed IPPM model is a framework to extend the concept of the distributed process planning and demonstrate the feasibility. The architecture and the three imponant modules - process planning module, production scheduling module, and decision making module are discussed in detail. While the process planning and decision making modules have been developed, the production scheduling module is in the progress of completion. This paper will contribute to the research area of integrated process planning and job shop scheduling. The decision making module discussed in the paper plays an important role for integration of proccss planning and production scheduling. To increase the capacity and capability of the decision making module,a combination of frame-based approach with neural networks and funy logic scheme is undertaken as the next research step of the project. 5. Acknowledgements This research was supported in pan by the National Science Foundation under the Grant contract #DDM-9211657.
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