- Email: [email protected]
Using a CATS database for alternative machine loading
Using a CATS Database for Alternative Machine Loading Steven L. Hankins, Cincinnati Milacron, Inc., Cincinnati, Ohio Richard A. Wysk, The Pennsylvania State University, University Park, Pennsylvania Kenneth R. Fox, Mead Data Central, Dayton, Ohio
recently received considerable attention. The rise in the interest in CAPP systems can be attributed to the problems with the manual or man-variant planning approach. The practice of specifying the manufacturing processes based solely on the planner's experience or general guidelines introduces inconsistencies and nonstandardized plans, In much the same way that only one process plan is generated for a part, only one machine is usually specified to perform a given process. Rarely will an alternative machine be included as part of the machine routing. In those cases in which alternative machines are in fact used, it is usually at the discretion of the shop foreman, and the foreman's decision is more than likely based on some detail concerning the actual manufacturing process. Many times, the alternative machines are older pieces of equipment which have been substantially depreciated, and the alternative machines will usually be less efficient with respect to a particular part than the preferred machine. However, the older, less efficient manual machines have been known to possess a greater processing capability. Neither the foreman nor the process planner has the luxury to spend their time manually enumerating the various alternatives available to produce a given part. Both are usually under pressure to meet the production schedules imposed by some higher authority. These conditions or circumstances result in overloads on the most efficient machines with the marginally inefficient
Abstract Industry is currently utilizing the computer to generate shop time standards. A logical extension of these systems is to include the ability to generate alternative standards, which then permits alternative machine routings. The purpose of this paper is to discuss the advantages of using alternative machine routings to improve the productivity of the machine shop. Through an example problem, it is shown that total production requirements are completed sooner by using alternative machines, and overall machine utilization is improved.
Keywords." CA TS, Machine Balancing, Alternative Machine Routings, Loading Algorithms, Machine Balancing, Machine Selection.
Background Our nation's industrial sector continues to be concerned with lagging productivity. All aspects of the manufacturing process are being evaluated as candidates for productivity improvements and technological advancements are allowing industry to a u t o m a t e processes previously p e r f o r m e d manually. One way some manufacturing companies are coping with productivity problems is through the automation of production planning activities as well as production activities. Computer aided process planning (CAPP) is one topic which has
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Journal of Manufacturing Systems Volume 3/No. 2
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machines underloaded or idle. By being able to utilize the process capability of the alternative machines for surge capacity, the following advantages can be attained: 1. Greater utilization of all capital equipment. 2. M o r e u n i f o r m l o a d i n g of m a c h i n e s and smoother work flow. 3. Smaller lead times. 4. Reduction of work-in-process inventory.
LogicJ-L_C L-~ Process-I~ .... _J Figure 1 Totally Automated Process Planning System
must be made after the decision concerning the manufacturing process has been reached. However, a link to the C A T S database is necessary to make this decision as exhibited in Figure 2. In general, machine selection involves making decisions about where the parts should be processed based on the capability of the machines available to produce the part, the cost or time forfeited by specifying an alternative routing, and the conditions of the shop at the time a decision must be made about where to route the part. The "Machine Selection Logic" block (Figure 2) must therefore represent computer software which accesses the CATS database to perform the alternative time standards. Included in the C A T S database would be the information needed by the computer to make a decision about the capability of a machine to produce a specific part. An example of the data includes the processing tolerances, part dimensions, and other pertinent workpiece information such as hardness, etc.
CATS Systems One of the keys to developing an ability to r o u t e parts t o - a l t e r n a t i v e machines is having alternative time standards available. The alternative standards provide an indication of the marginal efficiency of the machines with respect to a part. Although computer aided time standards (CATS) systems have received little attention in the literature, many companies are utilizing the computer to generate time standards. These systems may exist in a stand-alone mode or as part of a C A P P system. There are numerous benefits of a CATS system over a manual time standards system. These include consistency in the application of specific factors such as machining parameters and the elimination of manual calculation errors. Perhaps the most important benefit is the ability to store the standards and related documentation in a database for future reference. Manual filing of documentation is drastically reduced. In addition, the existence of the database also provides a source of valuable information for various types of production planning functions.
(Drawing~
Process~.~~4e~eCch~ine
Figure 2
Machine Selection
Revised Automated Process Planning System
It is important to note how this research interfaces with automated process planning systems. Wysk ~ has detailed the processes involved in building automated process planning systems as shown in
Database Design One of the underlying problems that may be attributed to this topic's lack of development, is the inadequacy of current CATS systems to generate alternative computer aided time standards. Since tradition has dictated that only one machine routing is generated, the need to easily calculate alternative standards has not existed. The authors of CATS systems probably only intended to a u t o m a t e this traditional procedure. A key feature of the CATS system which allows for the calculation of alternative
Figure 1. As described in the paper by Wysk, "the block labelled L O G I C would include the capability to scan and interpret the drawing, to convert this information into process requirements, and to select machines, tools and operations to yield an economically acceptable product") In order to take advantage of alternative machines, a decision about the machine assignment
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Journal of Manufacturing Systems Volume
3 : No. 2
where:
standards is the efficient structure of the database. It is important to only store the pertinent information concerning machine operations in the database. In other words, avoid building unique characteristics about each machine into the database. These situations can be handled outside the realm of the database management system, in peripheral software programs. For existing CATS databases, the implementation of this feature may involve a massive overhaul of the database structure, a n d / o r revision of the computer software. However, it will be shown that this investment may well be one of the best uses of manpower available to many industries.
Pij = the probability of selecting job i for machine j, Tij = the processing time for processing job i on machine j, m = the number of candidate machines. Once the set of probabilities are determined, they can be used to form the endpoints within a (0.1) scale so that the drawing of the random deviates provides a machine assignment. The individual probabilities can also be viewed as a measure which reflects the relative advantage of processing a given job on a particular machine in comparison with all the other machines.
Shop Loading Algorithms An Illustrative Example
The inability of the process planners to manually investigate alternative machine routings was noted earlier. Even if the planners were able to investigate all of the possible combinations with a digital computer, the size of the problem will more than likely become computationally intractable. Assuming N jobs could be assigned to any of the M machines, the number of possible combinations of assignments is M N. Algorithms which focus on workload balance between like machines give rise to meeting the objectives mentioned in the beginning of this paper. Workload balance has been addressed in the literature, but the concentration has been in mathematical programming techniques. An example can be found in the paper by Irastorza and Deane. 2 With a large number of parts to schedule, and a large number of alternative machines available, use of these techniques become prohibitive. Furthermore, to make this process truly effective, the loading or balancing algorithm should be run almost on a daily basis. Shop loading heuristics are therefore required. One such heuristic will be described below. In the thesis by Khator, 3 he introduced a method of performing the machine assignments based on calculating the "probability" of choosing a particular assignment for each job. The calculation of this probability is simple and computationally efficient, and the equation used for this calculation is given as: eij 1/Tij m (1)
As a means of showing how alternative standards can be used to improve shop utilization, the following example is presented. The data in the example are purely hypothetical. The example problem consists of assigning six jobs to four machines, with each of the four machines being capable of producing any of the six parts. Table 1 is a brief set of specifications of actual equipment which could be used to produce the parts under consideration. These characteristics are representative of the basic numerically controlled machining centers of the early 1970s. The machine Table 1 Machine Characteristics for Example Problem I SPINDLE I HORSEI POHER
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TABLE I HORK ENVELOPE I AUTO. SIZE I SIZEX, Y, 1 TOOL MACHINE 1 L X hi [ AND Z TRAVEL [ CHANGER I I (IN.) I (IN.) L ........................................................
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I I0 HIP I 72 X 60 I 240 X 96 X 96 I I I I ........................................................ I i I I D L i0 liP I 20 X 20 I 24 X 15 X 20 I I t I I ........................................................
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table size and work envelope size is included in Table 1, since the set up and piece handling allowances are generally correlated with these characteristics. Table size and work envelope size give an indication of the size, volume, and oftentimes
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Journal of Manufacturing Systems Volume 3 / N o . 2
machines because of a reduction in the feed rate on the larger holes.
complexity of the parts which can be processed on the machine. The process plans for the parts require a varying amount of machining, and the difference in the part configurations require a different set up and piece handling time. The specific machining operations will include drilling, reaming, counterboring, and tapping. A detailed description of each machining operation on each part is given in Table 2. Subland drills are used to produce a countersink for a subsequent tapping operation.
The Simulation Two algorithms will be used to assign the parts to the machines, with shop balance as the primary objective. A simple simulation model was developed to test the effectiveness of the algorithms and the alternative routes. All six of the parts presented in Table 3 Alternative Standard Times
Table 2 Machining Requirements on the Example Parts
MACH'rNE8 1PART
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HOLE DEPTH
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.344" .500" .484" .500"
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TWIST DRILL COUNT~.a~BORE TWIST DRILL REAMER .
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SUBLAND DRILL UNC TAP TWIST DRILL
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I .422/.562" I .500 - 13
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PART NUMBER [ .969" Ii.000" I .375/.500" I .438 - 14 .
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I .281" I .625" I .531/.688" I .625 - Ii 11.188" .
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Table 2 comprise the production requirement in varying lot sizes from day to day. The lot sizes are drawn from a discrete, uniform distribution, with a mean of 16 pieces per lot for all parts. The upper limit on the lot size is set at 20 pieces per lot, and the lower limit is set at 12 pieces per lot. Once the lot sizes are generated and the algorithm reaches a solution, a day's production has been simulated. Each run is equivalent to simulating 100 days of production. The first algorithm, titled the "Random Loading Algorithm", was described in a previous section. The algorithm makes a random assignment for each job based on drawing a uniform (0.1) random deviate. The most efficient machine will have a higher probability of being selected, but on an average, this scheme should balance the workload between the candidate machines. After all jobs are assigned, the shop balance is measured, and an
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A complete table of the alternative set up, piece handling, and machining times for the example parts appear in Table 3. The machining times were established with cast iron as the work material. Four of the parts contain the same machining allowances, and the other two parts require a small increase in the machining allowances on the five horsepower
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Journal of Manu[acturing Systems Volume
increase of more than 15%. This number is both statistically as well as economically significant. The average part mix makespan time fell from 783.6 minutes to 536.8 minutes, or nearly 32% by using an exhaustive loading procedure. Even though the "Random Loading Algorithm" produced poor balances, it could closely represent the manual balancing effort of a process planner, particularly if the problem size had been larger. As previously noted, an exhaustive algorithm is limited by the problem size. The ideal algorithm will be fast, use a minimal amount of computer memory, and yet provide solutions which approach the solution generated by the complete enumeration procedure. Such an algorithm has been developed, but its discussion deserves a more thorough treatment than this paper allows. The computational aspects of the algorithm should be discussed in detail, along with a discussion concerning how shop balance is measured.
attempt is made to make an improvement. If an improvement cannot be made, the algorithm is terminated. This algorithm's weakness lies in its abrupt termination criteria. However, it is an extremely fast procedure. The second algorithm, is a "Complete Enumeration Algorithm", which looks at all possible combinations of assignments. For this particular problem, 4096 (46) distinct assignments are investigated. After all assignments are exhausted, the best assignment for machine balance is selected. The results of the simulation with both algorithms are presented in Table 4. A Hewlett Packard 3000 minicomputer was used for the simulation. Table 4 Simulation Results Using Alternative CATS Machine Routings RANDOM i
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LOADING I
ALGORIT}~4
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I 1 I 2 I 3 I 4 .............................................................. AVERAGE I I I I MACHINE I 78.3 I 77.5 I 77.5 I 76.9 UTIL. I I I I .............................................................. R U N NO.
MAKESPAN FOR PART MIX
I I 798
TIME IN SECONDS
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I [ 793
I I 761
I I 790
I I I I .............................................................. EXECUTION I I I I 5
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MIX
This paper provides an overview of how a CATS system can be used to more effectively control a manufacturing system. CATS systems are currently being used as conventional time standard systems might be, without giving much consideration to the extended capabilities of these systems. This paper provides an overview of how these systems might be more effectively utilized as well as an indication of the savings that might be realized.
LOADING
R U N NO. I 1 I 2 I 3 I 4 I 5 I AVE. I DEV. .............................................................. AVERAGE I I l I I [ I MACHINE I 93.6 I 93.2 I 92.9 I 93.6 I 93.8 I 93.42 I .36 UTrL. I I L I I t I .............................................................. MAKESPAN I I I I I I I
FOR
Concluding Remarks
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T I M E IN I 447 I 448 I 446 I 447 I 446 I 446.8 I SECONDS I I i I l I i ..............................................................
6.14
3 No. 2
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References
As can be seen from Table 4, alternative routings from a CATS database feeding an effective loading algorithm, can significantly improve both "Machine Utilization" and the "Makespan, or Completion Time" for the part mix. The use of these alternative routings resulted in an average utilization
! . R . A . W y s k , M . M . B a r a s h a n d C i . M o o d i e . 1980, " U n i t M a c h i n i n g Operations: An Automated Process Planning and Selection Program", Journal q[ Engineering for Industry, V o l u m e 102, N o . 4, N o v e m b e r . 2. J . C . I r a s t o r z a a n d R . J . D e a n e , 1 9 7 4 , " A L o a d i n g a n d B a l a n c i n g Methodology f o r J o b S h o p C o n t r o l " , AIIE Transactions, V o l u m e 6, N o . 4, D e c e m b e r . 3. S . K . K h a t o r , 1 9 7 5 , " A L o a d i n g M e t h o d o l o g y for Job Shops Having Conventional and NC Machine Tools", Unpublished Ph.D. Thesis, Purdue University, West Lafayette, Indiana.
Author(s) Biography Steven L. Hankins received a B.S. and a M.S. degree in Industrial Engineering and Operations Research from Virginia Polytechnic Institute and State University, Blacksburg, Virginia. He is currently employed in
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Journal of Manufacturing Systems Volume 3/No. 2
the Manufacturing Systems Division at Cincinnati Milacron, Inc., and is responsible for developing applications software to assist with manufacturing systems proposals. Mr. Hankins is a member of the Society of Manufacturing Engineers, the Computer and Automated Systems Association of SME, and is a senior member of liE. Richard A. Wysk is an Associate Professor of Industrial Engineering at The Pennsylvania State University, University Park, Pennsylvania. His current teaching and research activities are in the areas of design, control, and analysis of automated manufacturing systems. He earned a B.S. IEOR and an M.S. IEOR degree from the University of Massachusetts, and a Ph.D. in Industrial Engineering from Purdue University. Dr. Wysk is a recipient of the 1981 SME Outstanding Young Manufacturing Engineer Award and the 1982 AIIE Region III Award of Excellence. Dr. Wysk has served as a Production Control Manager with the General Electric Company and as a Research Analyst with Caterpillar Tractor Company. He is currently a senior member of SME and AIIE. Kenneth R. Fox is the Manager of Central Systems Architecture for Mead Data Central in Dayton, Ohio. He is responsible for conceptualization, development, and optimization of leading edge hardware/ software architectures for the full-text searching and retrieval of information from multibillion character legal and new commercial databases. Prior experience includes Manager, Systems and Operations Analysis for Cincinnati Milacron, Inc., where he developed state-of-the-art simulation and finite capacity scheduling software for large scale flexible manufacturing systems. Mr. Fox was also involved with the operation research activities at Marathon Oil where he developed mathematical models to plan and optimize U.S. refining and marketing operations. Mr. Fox received a B.S. degree in Systems Analysis, summa cum laude, and a M.S. degree in Statistics and Applied Mathematics from Miami University (Ohio). He has completed course work requirements for a Ph.D. in Statistics at Iowa State University.
120
recently received considerable attention. The rise in the interest in CAPP systems can be attributed to the problems with the manual or man-variant planning approach. The practice of specifying the manufacturing processes based solely on the planner's experience or general guidelines introduces inconsistencies and nonstandardized plans, In much the same way that only one process plan is generated for a part, only one machine is usually specified to perform a given process. Rarely will an alternative machine be included as part of the machine routing. In those cases in which alternative machines are in fact used, it is usually at the discretion of the shop foreman, and the foreman's decision is more than likely based on some detail concerning the actual manufacturing process. Many times, the alternative machines are older pieces of equipment which have been substantially depreciated, and the alternative machines will usually be less efficient with respect to a particular part than the preferred machine. However, the older, less efficient manual machines have been known to possess a greater processing capability. Neither the foreman nor the process planner has the luxury to spend their time manually enumerating the various alternatives available to produce a given part. Both are usually under pressure to meet the production schedules imposed by some higher authority. These conditions or circumstances result in overloads on the most efficient machines with the marginally inefficient
Abstract Industry is currently utilizing the computer to generate shop time standards. A logical extension of these systems is to include the ability to generate alternative standards, which then permits alternative machine routings. The purpose of this paper is to discuss the advantages of using alternative machine routings to improve the productivity of the machine shop. Through an example problem, it is shown that total production requirements are completed sooner by using alternative machines, and overall machine utilization is improved.
Keywords." CA TS, Machine Balancing, Alternative Machine Routings, Loading Algorithms, Machine Balancing, Machine Selection.
Background Our nation's industrial sector continues to be concerned with lagging productivity. All aspects of the manufacturing process are being evaluated as candidates for productivity improvements and technological advancements are allowing industry to a u t o m a t e processes previously p e r f o r m e d manually. One way some manufacturing companies are coping with productivity problems is through the automation of production planning activities as well as production activities. Computer aided process planning (CAPP) is one topic which has
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Journal of Manufacturing Systems Volume 3/No. 2
~
machines underloaded or idle. By being able to utilize the process capability of the alternative machines for surge capacity, the following advantages can be attained: 1. Greater utilization of all capital equipment. 2. M o r e u n i f o r m l o a d i n g of m a c h i n e s and smoother work flow. 3. Smaller lead times. 4. Reduction of work-in-process inventory.
LogicJ-L_C L-~ Process-I~ .... _J Figure 1 Totally Automated Process Planning System
must be made after the decision concerning the manufacturing process has been reached. However, a link to the C A T S database is necessary to make this decision as exhibited in Figure 2. In general, machine selection involves making decisions about where the parts should be processed based on the capability of the machines available to produce the part, the cost or time forfeited by specifying an alternative routing, and the conditions of the shop at the time a decision must be made about where to route the part. The "Machine Selection Logic" block (Figure 2) must therefore represent computer software which accesses the CATS database to perform the alternative time standards. Included in the C A T S database would be the information needed by the computer to make a decision about the capability of a machine to produce a specific part. An example of the data includes the processing tolerances, part dimensions, and other pertinent workpiece information such as hardness, etc.
CATS Systems One of the keys to developing an ability to r o u t e parts t o - a l t e r n a t i v e machines is having alternative time standards available. The alternative standards provide an indication of the marginal efficiency of the machines with respect to a part. Although computer aided time standards (CATS) systems have received little attention in the literature, many companies are utilizing the computer to generate time standards. These systems may exist in a stand-alone mode or as part of a C A P P system. There are numerous benefits of a CATS system over a manual time standards system. These include consistency in the application of specific factors such as machining parameters and the elimination of manual calculation errors. Perhaps the most important benefit is the ability to store the standards and related documentation in a database for future reference. Manual filing of documentation is drastically reduced. In addition, the existence of the database also provides a source of valuable information for various types of production planning functions.
(Drawing~
Process~.~~4e~eCch~ine
Figure 2
Machine Selection
Revised Automated Process Planning System
It is important to note how this research interfaces with automated process planning systems. Wysk ~ has detailed the processes involved in building automated process planning systems as shown in
Database Design One of the underlying problems that may be attributed to this topic's lack of development, is the inadequacy of current CATS systems to generate alternative computer aided time standards. Since tradition has dictated that only one machine routing is generated, the need to easily calculate alternative standards has not existed. The authors of CATS systems probably only intended to a u t o m a t e this traditional procedure. A key feature of the CATS system which allows for the calculation of alternative
Figure 1. As described in the paper by Wysk, "the block labelled L O G I C would include the capability to scan and interpret the drawing, to convert this information into process requirements, and to select machines, tools and operations to yield an economically acceptable product") In order to take advantage of alternative machines, a decision about the machine assignment
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Journal of Manufacturing Systems Volume
3 : No. 2
where:
standards is the efficient structure of the database. It is important to only store the pertinent information concerning machine operations in the database. In other words, avoid building unique characteristics about each machine into the database. These situations can be handled outside the realm of the database management system, in peripheral software programs. For existing CATS databases, the implementation of this feature may involve a massive overhaul of the database structure, a n d / o r revision of the computer software. However, it will be shown that this investment may well be one of the best uses of manpower available to many industries.
Pij = the probability of selecting job i for machine j, Tij = the processing time for processing job i on machine j, m = the number of candidate machines. Once the set of probabilities are determined, they can be used to form the endpoints within a (0.1) scale so that the drawing of the random deviates provides a machine assignment. The individual probabilities can also be viewed as a measure which reflects the relative advantage of processing a given job on a particular machine in comparison with all the other machines.
Shop Loading Algorithms An Illustrative Example
The inability of the process planners to manually investigate alternative machine routings was noted earlier. Even if the planners were able to investigate all of the possible combinations with a digital computer, the size of the problem will more than likely become computationally intractable. Assuming N jobs could be assigned to any of the M machines, the number of possible combinations of assignments is M N. Algorithms which focus on workload balance between like machines give rise to meeting the objectives mentioned in the beginning of this paper. Workload balance has been addressed in the literature, but the concentration has been in mathematical programming techniques. An example can be found in the paper by Irastorza and Deane. 2 With a large number of parts to schedule, and a large number of alternative machines available, use of these techniques become prohibitive. Furthermore, to make this process truly effective, the loading or balancing algorithm should be run almost on a daily basis. Shop loading heuristics are therefore required. One such heuristic will be described below. In the thesis by Khator, 3 he introduced a method of performing the machine assignments based on calculating the "probability" of choosing a particular assignment for each job. The calculation of this probability is simple and computationally efficient, and the equation used for this calculation is given as: eij 1/Tij m (1)
As a means of showing how alternative standards can be used to improve shop utilization, the following example is presented. The data in the example are purely hypothetical. The example problem consists of assigning six jobs to four machines, with each of the four machines being capable of producing any of the six parts. Table 1 is a brief set of specifications of actual equipment which could be used to produce the parts under consideration. These characteristics are representative of the basic numerically controlled machining centers of the early 1970s. The machine Table 1 Machine Characteristics for Example Problem I SPINDLE I HORSEI POHER
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TABLE I HORK ENVELOPE I AUTO. SIZE I SIZEX, Y, 1 TOOL MACHINE 1 L X hi [ AND Z TRAVEL [ CHANGER I I (IN.) I (IN.) L ........................................................
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I I0 HIP I 72 X 60 I 240 X 96 X 96 I I I I ........................................................ I i I I D L i0 liP I 20 X 20 I 24 X 15 X 20 I I t I I ........................................................
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table size and work envelope size is included in Table 1, since the set up and piece handling allowances are generally correlated with these characteristics. Table size and work envelope size give an indication of the size, volume, and oftentimes
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Journal of Manufacturing Systems Volume 3 / N o . 2
machines because of a reduction in the feed rate on the larger holes.
complexity of the parts which can be processed on the machine. The process plans for the parts require a varying amount of machining, and the difference in the part configurations require a different set up and piece handling time. The specific machining operations will include drilling, reaming, counterboring, and tapping. A detailed description of each machining operation on each part is given in Table 2. Subland drills are used to produce a countersink for a subsequent tapping operation.
The Simulation Two algorithms will be used to assign the parts to the machines, with shop balance as the primary objective. A simple simulation model was developed to test the effectiveness of the algorithms and the alternative routes. All six of the parts presented in Table 3 Alternative Standard Times
Table 2 Machining Requirements on the Example Parts
MACH'rNE8 1PART
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HOLE DEPTH
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SUBLAND DRILL UNC TAP TWIST DRILL
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PART NUMBER [ .969" Ii.000" I .375/.500" I .438 - 14 .
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Table 2 comprise the production requirement in varying lot sizes from day to day. The lot sizes are drawn from a discrete, uniform distribution, with a mean of 16 pieces per lot for all parts. The upper limit on the lot size is set at 20 pieces per lot, and the lower limit is set at 12 pieces per lot. Once the lot sizes are generated and the algorithm reaches a solution, a day's production has been simulated. Each run is equivalent to simulating 100 days of production. The first algorithm, titled the "Random Loading Algorithm", was described in a previous section. The algorithm makes a random assignment for each job based on drawing a uniform (0.1) random deviate. The most efficient machine will have a higher probability of being selected, but on an average, this scheme should balance the workload between the candidate machines. After all jobs are assigned, the shop balance is measured, and an
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TWIST DRILL COU~T~BORE SUBLAND DRILL UNC TAP TWIST DRILL .
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TWIST DRILL REAMER SUBLAND D R I L L UNC T A P .
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A complete table of the alternative set up, piece handling, and machining times for the example parts appear in Table 3. The machining times were established with cast iron as the work material. Four of the parts contain the same machining allowances, and the other two parts require a small increase in the machining allowances on the five horsepower
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Journal of Manu[acturing Systems Volume
increase of more than 15%. This number is both statistically as well as economically significant. The average part mix makespan time fell from 783.6 minutes to 536.8 minutes, or nearly 32% by using an exhaustive loading procedure. Even though the "Random Loading Algorithm" produced poor balances, it could closely represent the manual balancing effort of a process planner, particularly if the problem size had been larger. As previously noted, an exhaustive algorithm is limited by the problem size. The ideal algorithm will be fast, use a minimal amount of computer memory, and yet provide solutions which approach the solution generated by the complete enumeration procedure. Such an algorithm has been developed, but its discussion deserves a more thorough treatment than this paper allows. The computational aspects of the algorithm should be discussed in detail, along with a discussion concerning how shop balance is measured.
attempt is made to make an improvement. If an improvement cannot be made, the algorithm is terminated. This algorithm's weakness lies in its abrupt termination criteria. However, it is an extremely fast procedure. The second algorithm, is a "Complete Enumeration Algorithm", which looks at all possible combinations of assignments. For this particular problem, 4096 (46) distinct assignments are investigated. After all assignments are exhausted, the best assignment for machine balance is selected. The results of the simulation with both algorithms are presented in Table 4. A Hewlett Packard 3000 minicomputer was used for the simulation. Table 4 Simulation Results Using Alternative CATS Machine Routings RANDOM i
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I
I
I 1 I 2 I 3 I 4 .............................................................. AVERAGE I I I I MACHINE I 78.3 I 77.5 I 77.5 I 76.9 UTIL. I I I I .............................................................. R U N NO.
MAKESPAN FOR PART MIX
I I 798
TIME IN SECONDS
I
I [ 793
I I 761
I I 790
I I I I .............................................................. EXECUTION I I I I 5
I
6
I
6
I
I
I
I~WUMERATION
i
I
I
8TD. DEV.
I
I AVE.
I I 78.4 I
I I 78.04 I
I I I
1.2
I I I
I I 776
I I 783.6
i i
15.0
I 1
I
I
I
5
I 5
[ I I I .............................................................. COMPLETE
I
I
I
I
I 6
1
I
I
I I
I
I
{
i
l
5.6
STD.
I I
I 539 I 538 I 526 I 540 I I I I .............................................................. EXECUTION I I L I PART
MIX
This paper provides an overview of how a CATS system can be used to more effectively control a manufacturing system. CATS systems are currently being used as conventional time standard systems might be, without giving much consideration to the extended capabilities of these systems. This paper provides an overview of how these systems might be more effectively utilized as well as an indication of the savings that might be realized.
LOADING
R U N NO. I 1 I 2 I 3 I 4 I 5 I AVE. I DEV. .............................................................. AVERAGE I I l I I [ I MACHINE I 93.6 I 93.2 I 92.9 I 93.6 I 93.8 I 93.42 I .36 UTrL. I I L I I t I .............................................................. MAKESPAN I I I I I I I
FOR
Concluding Remarks
[
I
I
I
I 541 I
I 536.8 t
I I
I
I
I
T I M E IN I 447 I 448 I 446 I 447 I 446 I 446.8 I SECONDS I I i I l I i ..............................................................
6.14
3 No. 2
I I I I
I I [ I i
References
As can be seen from Table 4, alternative routings from a CATS database feeding an effective loading algorithm, can significantly improve both "Machine Utilization" and the "Makespan, or Completion Time" for the part mix. The use of these alternative routings resulted in an average utilization
! . R . A . W y s k , M . M . B a r a s h a n d C i . M o o d i e . 1980, " U n i t M a c h i n i n g Operations: An Automated Process Planning and Selection Program", Journal q[ Engineering for Industry, V o l u m e 102, N o . 4, N o v e m b e r . 2. J . C . I r a s t o r z a a n d R . J . D e a n e , 1 9 7 4 , " A L o a d i n g a n d B a l a n c i n g Methodology f o r J o b S h o p C o n t r o l " , AIIE Transactions, V o l u m e 6, N o . 4, D e c e m b e r . 3. S . K . K h a t o r , 1 9 7 5 , " A L o a d i n g M e t h o d o l o g y for Job Shops Having Conventional and NC Machine Tools", Unpublished Ph.D. Thesis, Purdue University, West Lafayette, Indiana.
Author(s) Biography Steven L. Hankins received a B.S. and a M.S. degree in Industrial Engineering and Operations Research from Virginia Polytechnic Institute and State University, Blacksburg, Virginia. He is currently employed in
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Journal of Manufacturing Systems Volume 3/No. 2
the Manufacturing Systems Division at Cincinnati Milacron, Inc., and is responsible for developing applications software to assist with manufacturing systems proposals. Mr. Hankins is a member of the Society of Manufacturing Engineers, the Computer and Automated Systems Association of SME, and is a senior member of liE. Richard A. Wysk is an Associate Professor of Industrial Engineering at The Pennsylvania State University, University Park, Pennsylvania. His current teaching and research activities are in the areas of design, control, and analysis of automated manufacturing systems. He earned a B.S. IEOR and an M.S. IEOR degree from the University of Massachusetts, and a Ph.D. in Industrial Engineering from Purdue University. Dr. Wysk is a recipient of the 1981 SME Outstanding Young Manufacturing Engineer Award and the 1982 AIIE Region III Award of Excellence. Dr. Wysk has served as a Production Control Manager with the General Electric Company and as a Research Analyst with Caterpillar Tractor Company. He is currently a senior member of SME and AIIE. Kenneth R. Fox is the Manager of Central Systems Architecture for Mead Data Central in Dayton, Ohio. He is responsible for conceptualization, development, and optimization of leading edge hardware/ software architectures for the full-text searching and retrieval of information from multibillion character legal and new commercial databases. Prior experience includes Manager, Systems and Operations Analysis for Cincinnati Milacron, Inc., where he developed state-of-the-art simulation and finite capacity scheduling software for large scale flexible manufacturing systems. Mr. Fox was also involved with the operation research activities at Marathon Oil where he developed mathematical models to plan and optimize U.S. refining and marketing operations. Mr. Fox received a B.S. degree in Systems Analysis, summa cum laude, and a M.S. degree in Statistics and Applied Mathematics from Miami University (Ohio). He has completed course work requirements for a Ph.D. in Statistics at Iowa State University.
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