- Email: [email protected]
Compatibility with Total Optimization and Partial Optimization in Material Industry
Copyright €l IFAC Management and Control of Production and Logistics, Grenoble, France, 2000
COMPATIBILITY WITH TOTAL OPTIMIZATION AND PARTIAL OPTIMIZATION IN MATERIAL INDUSTRY
H. Takahashi*, H.Iketkl*, S.Sugizaki*, H.Morihisa**, and Y.Ikkai** *Nippon Steel Information & Communication Systems Inc.(ENICOM), Osaka University
** Department ofInformation Systems Engineering, Faculty ofEngineering, * 1 Fuji-Cho, Hirohata-ku, Himeji, Hyogo 671-1188 Japan
Telephone +81-792-36-9412, Fax +81-792-36-8277 [email protected], [email protected], [email protected]
** 2-1 Yamadaoka, SUita, Osaka 565-0871 Japan Telephone +81-6-6879-7825, Fax +81-6-6879-7827 [email protected], ikkai @ise.eng.osaka-u.ac.jp
Abstract: We have developed a production planning system for a works in a material industry. The works focused on improving productivity by efficiently operating production lines. The conventional production planning did not guarantee the optimization of the entire works. Globalization of competition in recent years compelled the works to strengthen non-price competitiveness by shortening production lead time. The works had to realize production planning accomplishing shorter lead time. We achieved total optimization by making use of the theory of constraints or the basic principle of supply chain management. We adapted partial optimization of bottleneck line by using genetic algorithm-based logic to guarantee the total optimization of the works. We devised a method for assuring both total optimization and partial optimization and applied to an actual production planning system. Therefore, the production lead time was reduced by one-third, and the shipping lead time was reduced by two-thirds. Copyright ti' 2000IFAC Keywords: Optimization Problem, Constraints Satisfaction Problem, Production Control, Scheduling Algorithms, ManlMachine Interface, and Genetic Algorithms
1. BACKGROUND FOR SYSTEM DEVELOPMENT
Each works receives the production instructions from the head office and prepares its own production plans. First, a monthly production target is set for each production line. Then, a week production plan is prepared on the basis of the production instructions from the head office. Furthermore, a daily production plan is prepared by reflecting daily production data. The production preconditions of the works for which the production planning system was developed are as follows: - Product shipment: About 10,000 products/month - Number of product types: About 200 - Number of production lines: 25
1.1 Overview of production control at works for which system was developed
The company in question supplies materials to customers and operates ten works in Japan, each works manufacturing materials to orders. All orders are received at the head office. The head office issues production instructions to each works once a week. The time from the receipt of orders to the delivery of products is about two months.
929
- Number of processes through which products pass: Minimum of one to maximum of ten The production processes range from the process of refining raw materials to the process of packaging products. The intennediate product is formed to an overall shape and is then treated through various processes to suit its final product type. J.2 Problems with shipping lead time Products are mainly transported by ship from the works to the customers. Ship transport is lower in cost, but has severer route constraints and loading rate guarantee than other means of transport. When the works is unable to assure a planned shipment due to a production delay, it must pay the shipping company the penalty for demurrage or reduced loading rate. Once the works misses a shipping opportunity, it must arrange for another ship. The resultant time delays the delivery of the products to the customer. Under the conventional production planning system, the accuracy of predicting the production completion date was low. To avoid the above-mentioned risk, the production of the products in question was started early, and a ship to transport the products was arranged for when the production of the products was completed. The time actually taken from production completion to shipping was very long at about one month. The main challenges of the conventional production planning system were improving the accuracy of predicting the production completion date and reducing the time from production completion to shipping.
coordination with the downstream and upstream lines. This means that total optimization cannot be always attained by partial optimization of individual lines without coordination. To optimize the productivity of the entire works, we prepared production plans for the entire works starting at bottleneck lines by utilizing the theory of constraints (TOC) or basic principle of supply chain management (SCM). The production plans made by the production planning system can achieve shorter production lead time, shorter shipping lead time, smaller inventory and due delivery date while keeping the productivity of each line at a certain level. 2.2 Needfor panial optimization plans to achieve total optimization If production plans oriented toward total optimization are combined to maximize the total profit, each line must guarantee conventional or higher productivity. This is an especially important factor for a bottleneck line, because the bottleneck line is a key line for the production plans to accomplish total optimization. To guarantee the productivity of the bottleneck line in question, the puroduction lots for the bottleneck line have to be processed according to prodcution plan. In other words, there is requirement to soomthly process semifInished products in a production lot. We built an optimization system for the bottleneck line. The system is designed to optimize the processing sequence of semifInished products through the bottleneck line by using genetic algorithm(GA). It is exceedingly partial optimization from the viewpoint of the entire works, however, the optimaization of the bottleneck line guaranteies stable production of its own line. And also, stable production of the bottleneck line guaranteies the total optimization-oriented production plans of the entire works. We think that partial optimization is need to realize total optimization.
2. CONSIDERATIONS ABOUT TOTAL OPTIMIZATION AND PARTIAL OPTIMIZATION
2. J Approach to total optimization
Prior to the introduction of the new production planning system, improvement activities were carried out to achieve the goal of improving the specific productivity of each production line. The evaluation functions were to achieve the desired production level and to reduce the operating cost. To meet the evaluation functions, each production line normally carried large work-in-process inventories and aimed at the largest possible production lots. There were two big problems from the viewpoint of the entire works. One was the obstruction of stable production. The production variation of the upstream line influenced the amount of work-in-process inventory at the entry end of the downstream line. As a result, there occurred many events in which a resource shortage forced the shutdown of the line. The other problem was the delay in delivery. Since each line gave priority to production lot size increase over delivery due date, there occurred many delivery delays. The root cause of these problems was that each line pursued productivity improvement without
3. PRODUCTION PLANNING SYSTEM ORIENTED TOWARD TOTAL OPTIMIZATION The purposes of the production planning system oriented toward the total optimization of the entire works are as follows: (1) Reduction in production and shipping lead time (2) Reduction in product inventory (3) Improvement in due date achievement rate The system we have developed to meet these purposes is based on two improvement mechanisms. One is the mechanism to determine a shipping date from a ship allocation plan and to calculate the production start date. This is effective in improving the shortening the shipping lead time, reducing the product inventory and the due date achievement rate . The other is the mechanism to prepare production
930
plans for the entire works, starting with the production plan of the bottleneck line. This aims at shortening the production lead time.
(2)The production lots for the downstream line are formed at such timing that the semifInished products are supplied from the bottleneck line. (3)The production plans for the upstream and downstream lines are prepared to keep the due dates.
3. J Ship allocation plans
The new production planning system automatically generates the production plan for the bottleneck line and the production plans for the upstream and downstream lines. The production planners confirm the respective results and determines the production plans after making some modifications as required. Fig.2 shows typical results of production planning.
The products of the works are mainly transported by ship. A ship to transport the products was arranged for when the production of the products was completed. The new production planning system allows the works to prepare a ship allocation plan from due date information by considering past ship allocation patterns, smoothening of shipping tasks and smoothening of production volume by type of product. The ship allocation plan can not only determine ship arrival dates but also calculate the shipping dates of products from the ship arrival dates. If the production start date of a particular product is calculated backward from the shipping date of the product, for example, the product can be produced to meet the shipping date, and the lead time from production completion to shipping can be sharply reduced.
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3.2 Production planning system starting at bottleneck line
Fig.2 . Results of production planning The production planning of the entire works flow is shown in Fig. I. 4. APPROACH TO PARTIAL OPTIMIZATION I
The overall production plan is affected by the bottleneck line. It is thus preparing a production plan starting at the bottleneck line at first. The production plan for the bottleneck line is prepared by taking the following two points into account: (1) Prioritize the product types that can form the largest production lots as required to ensure desired productivity. (2) Prioritize the due date of semifInished products to be assembled into lots. Based on the production plan for the bottleneck line, production plans are prepared for the upstream and downstream lines. In that case, the following points are taken into account: (I)The production plan for the upstream line is prepared to guarantee the supply of semifInished products to the bottleneck line.
4.1 Overview ofprocess to be partly optimized
The subject of partial optimization is the continuous process line. The continuous process line is the bottleneck line through which 40% of the products to be produced at the works pass. It is controlled as one of the most important lines at the works. Its conflgration is schematically illustrated Fig.4. The role of the line is to conduct various precision working operations on completely shaped serniflnished products. The continuous process line has the following characteristics: (l)Multiple processes are arranged in series and continuously. (2)Each process has individual constraints. (Any failure to meet the constraints may cause a quality or equipment trouble.) (3)Yield and operating cost vary with the sequence in which the semifinished products pass through the line.
(I) Prepare production plan for bottleneck line.
The point of production planning for the continuous process line is to determine the sequence in which the semifInished products in a production lot pass through the continuous process line. A processing sequence plan is prepared for each production lot. Fig. I. Priodution planning flow
931
Then, they modify the solution by adding or deleting the semifinished products and determines a final processing sequence. Optimization by genetic algorithm (GA) ; There are many evaluation functions that are in trade-off relations to each other. It is impossible to optimize all evaluation functions at the same time. There is no guarantee that there is a processing sequence that can meet all of the constraints. When we developed the new production planning system, we were requested by the production planners that the computing time to prepare a complex and difficult processing sequence plan should not exceed ten minutes. We studied optimization algorithms to solve this problem. As a result, we decided on the adoption of the genetic algorithm (GA) as solution logic. The reasons are as follows: (l)The GA can get an approximately optimal solution in a short time. (2)The GA allows the easy addition and modification of evaluation functions and constraints. (3)We can implement in a shorter time than other optimization algorithms.
P3
P2
PI
Continuous Process Line P3
P2
PI
Fig.3. Overview of continuous process line The semifmished products assembled into the production lot number a maximum of 300 and an average of 200. The production planners indicated to the line in question a processing sequence plan checked by using various evaluation functions (e.g.• amount by which the sernifinished product size changes. number of heat treating furnace changes. and number of working tool changes). To resolve the constraint violation or improve the evaluation functions, however, the line operator sometimes modified the processing sequence by using unscheduled semifmished products. This is because it is difficult to search for an optimal processing sequence plan within limited time and there are too many constraints to be judged by the production planners.
The characteristics of the GA-based optimization system developed by us are described below. (l)Method for preparation initial solutions The initial solutions are the start point for the GA. Its quality predominantly determines the convergence time of solutions. When production planners were investigated. it was found that the evaluation function they made the most of during the preparation of processing sequence plans was the size(e.g.• width, thickness) change. It was thereupon decided to introduce this thinking when preparing the initial solution. (2)Method for determining number of generations and number of individuals The number of generations and the number of individuals per generation were determined by finding a combination with good balance between solution optimality and computing time after trying various combinations. The number of individuals per generation is 100, and the number of generations is 500. The number of generations can be changed by the production planners.
4.2 Preparation ofoptimal processing sequence plan Man-machine role allocation considerations; The flow of the tasks performed by the production planners to prepare a processing sequence plan is shown in Fig. 5. The production planners first obtains an approximately optimal processing sequence for the semifinished products according to the due date and other constraints by using the computer logic (GA). Unplanned Semifinished Products
Pinned Semifmished Products
(3)Treatrnent of evaluation functions There are various evaluation functions. so that the fitness value was formulated by using a weighing factor. This approach can obtain a solution that emphasizes a particular criterion and can prioritize the evaluation functions. The production planners can change the weighting factor and can arbitrarily control the trade-off between the evaluation functions. Modification; The production planners modify the approximately optimal solution computed by the GA and determines the final processing sequence plan. The modification involves deleting several semifinished products from the solution or adding
approximately oplimal Sequence
Fig.4. Flow prepareation.
of
processing
sequence
plan
932
The shipping lead time is sharply reduced compared with the target value. This is a result of the fact that the functions of determining the production start date from the shipping date and preparing a production plan starting with the bottleneck line have properly worked. In other words, semifinished products are supplied to respective lines in a balanced manner by the total optimization-oriented production plans.
semifinished products to the solution from a group of unplanned semifinished products. The modification may be made to the approximately optimal solution in two cases. In one case, an attempt is made to eliminate constraints. The GA system developed by us has no function of changing given semifinished products. For this reason, semifmished products that adversely affect the entire processing sequence cannot be removed, inevitably resulting in constraint violation. In the other case, an attempt is made to improve further the evaluation functions of the solution. The production planners attempt to improve the solution produced by the GA by deleting semifmished products from the solution or adding semifinished products to the solution from a group of unplanned semifinished products. Table 1 shows an example in which the number of violated constraints and the overall evaluation are improved as a result of such attempt.
5.2 Evaluation and problems ofprocessing sequence planning system
Table 3 compares the new and old systems in the processing sequence planning time. The realized automatic planning time is shorter than the target value and is shorter by as much as 82 minutes than that computed by the old system. Table 3 Comparison of processing sequence planning system
Table I Relationship between number of violated constraints and overall evaluation before and after modification Before modification #of Overall violated evaluation constraints Lot A Lot
, B
16
B
10
A
After modification # of Overall violated evaluation constraints
o o
A
15
8
o
5. EVALUATION
5.1 Evaluation of total optimization-oriented production planning system
Table 2 shows how much the production lead time, shipping lead time and inventory are improved by the introduction of the new production planning system.
Target
Realized
Production Lead Time
30.1 days
19.7 days
19.2 days
TargetRealized -0.5 days
Shipping Lead Time
30.5 days
13.9 days
10.5 days
-3.4 days
Inventory Reduction
100%
50%
47.3%
-2.7 %
IOmin
8 min
Modification
120min
60min
70min
IOmin
Realized
6. CONCLUSIONS The new production planning system was released in May 1999 and has been smoothly operating since then. It has performed better than expected and has achieved higher productivity and shorter lead time. The results of production planning have come to directly govern the productivity of the works, and the philosophy of production planning has completely switched from emphasis on the productivity of individual lines to increase in the throughput (shipment) of the entire works. This has been achieved with the optimization of individual lines guaranteed. Especially, we evaluate that optimization
Table 2 Target Vs Realized Old
90min
Target
The realized modification time is longer than the target value. We first attempted to develop a mechanism for computing the necessary modification by computer, but then decided to exclude this modification from the scope of automatic computation by computer because the computing time is prolonged by the addition of the knapsack problem and because it is very difficult to leave final human judgment to logic. We thought it important to clarify the roles of the human and the computer rather than to develop a system that automatically computes all things. Judgment of optimality constantly changes and regrettably is subject to the personality of people who make the judgment. More desirable solutions than obtainable from automatic logic should be derived by utilizing people who have a wealth of know-how.
A
# of semi-finished products Deleted added Lot A Lot B
Automatic Plannnig
Target Realized -2 min
Old
933
of the bottleneck line plays an important role in total optimization of the works. There are two future problems. One is to increase the throughput further. The throughput can be increased to enjoy greater than ever profits if such a system is built that products in demand can be supplied faster and cheaper in response to market trends. The bottleneck line changes with the proportions of individual products (product mix). We think it also important to develop a system capable of flexibly coping with the change in the bottleneck line. The other problem is to decrease the modification work load during processing sequence plan preparation. We are planning to develop the functions of deleting and adding semifinished products by focusing only on the resolution of constraint violation. We have made only the first step from the standpoint of supply chain management (SCM). We will go ahead with optimization of the entire supply chain.
REFERENCES H.Morihisa, H.Ikeda, H.Uchibori, R.Oshita, and N.Komoda (1999). An Infonnation System Infrastructure and Applications for Improving White Collar Productivity in a Steel In: Information Manufacturing Works. Infrastructure Systems for Manufacturing (I.Mills and F.Kimura(Ed.), pp.229-247, Kluwer Academic Publishers. I.Okinaka (1986). Problems in the Production Management System of the Steel Industry - An Approach to the Problem of Multi-Purpose Decision- Making-. In: Preprint of • -th International Conference on Multiple Criteria Decision Making -Toward Interactive & Intelligent Decision Suppon Systems-, Vo1.2, pp.551-560. Mitchell, M. (1996). An Introduction To Genetic Algorithms. The MIT Press. S.Sugizaki, H.Ikeda, T.Kitabayashi, H.Morihisa, and Y.Ikkai (1999). Construction of Integrated Manufacturing Optimization System in Material Industry. In Proc. of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'99), pp.1329-1337, Barcelona, Spain. Z.Imaoka (1998). Supply Chain Management. Kogyo Chousa Publishing.
934
COMPATIBILITY WITH TOTAL OPTIMIZATION AND PARTIAL OPTIMIZATION IN MATERIAL INDUSTRY
H. Takahashi*, H.Iketkl*, S.Sugizaki*, H.Morihisa**, and Y.Ikkai** *Nippon Steel Information & Communication Systems Inc.(ENICOM), Osaka University
** Department ofInformation Systems Engineering, Faculty ofEngineering, * 1 Fuji-Cho, Hirohata-ku, Himeji, Hyogo 671-1188 Japan
Telephone +81-792-36-9412, Fax +81-792-36-8277 [email protected], [email protected], [email protected]
** 2-1 Yamadaoka, SUita, Osaka 565-0871 Japan Telephone +81-6-6879-7825, Fax +81-6-6879-7827 [email protected], ikkai @ise.eng.osaka-u.ac.jp
Abstract: We have developed a production planning system for a works in a material industry. The works focused on improving productivity by efficiently operating production lines. The conventional production planning did not guarantee the optimization of the entire works. Globalization of competition in recent years compelled the works to strengthen non-price competitiveness by shortening production lead time. The works had to realize production planning accomplishing shorter lead time. We achieved total optimization by making use of the theory of constraints or the basic principle of supply chain management. We adapted partial optimization of bottleneck line by using genetic algorithm-based logic to guarantee the total optimization of the works. We devised a method for assuring both total optimization and partial optimization and applied to an actual production planning system. Therefore, the production lead time was reduced by one-third, and the shipping lead time was reduced by two-thirds. Copyright ti' 2000IFAC Keywords: Optimization Problem, Constraints Satisfaction Problem, Production Control, Scheduling Algorithms, ManlMachine Interface, and Genetic Algorithms
1. BACKGROUND FOR SYSTEM DEVELOPMENT
Each works receives the production instructions from the head office and prepares its own production plans. First, a monthly production target is set for each production line. Then, a week production plan is prepared on the basis of the production instructions from the head office. Furthermore, a daily production plan is prepared by reflecting daily production data. The production preconditions of the works for which the production planning system was developed are as follows: - Product shipment: About 10,000 products/month - Number of product types: About 200 - Number of production lines: 25
1.1 Overview of production control at works for which system was developed
The company in question supplies materials to customers and operates ten works in Japan, each works manufacturing materials to orders. All orders are received at the head office. The head office issues production instructions to each works once a week. The time from the receipt of orders to the delivery of products is about two months.
929
- Number of processes through which products pass: Minimum of one to maximum of ten The production processes range from the process of refining raw materials to the process of packaging products. The intennediate product is formed to an overall shape and is then treated through various processes to suit its final product type. J.2 Problems with shipping lead time Products are mainly transported by ship from the works to the customers. Ship transport is lower in cost, but has severer route constraints and loading rate guarantee than other means of transport. When the works is unable to assure a planned shipment due to a production delay, it must pay the shipping company the penalty for demurrage or reduced loading rate. Once the works misses a shipping opportunity, it must arrange for another ship. The resultant time delays the delivery of the products to the customer. Under the conventional production planning system, the accuracy of predicting the production completion date was low. To avoid the above-mentioned risk, the production of the products in question was started early, and a ship to transport the products was arranged for when the production of the products was completed. The time actually taken from production completion to shipping was very long at about one month. The main challenges of the conventional production planning system were improving the accuracy of predicting the production completion date and reducing the time from production completion to shipping.
coordination with the downstream and upstream lines. This means that total optimization cannot be always attained by partial optimization of individual lines without coordination. To optimize the productivity of the entire works, we prepared production plans for the entire works starting at bottleneck lines by utilizing the theory of constraints (TOC) or basic principle of supply chain management (SCM). The production plans made by the production planning system can achieve shorter production lead time, shorter shipping lead time, smaller inventory and due delivery date while keeping the productivity of each line at a certain level. 2.2 Needfor panial optimization plans to achieve total optimization If production plans oriented toward total optimization are combined to maximize the total profit, each line must guarantee conventional or higher productivity. This is an especially important factor for a bottleneck line, because the bottleneck line is a key line for the production plans to accomplish total optimization. To guarantee the productivity of the bottleneck line in question, the puroduction lots for the bottleneck line have to be processed according to prodcution plan. In other words, there is requirement to soomthly process semifInished products in a production lot. We built an optimization system for the bottleneck line. The system is designed to optimize the processing sequence of semifInished products through the bottleneck line by using genetic algorithm(GA). It is exceedingly partial optimization from the viewpoint of the entire works, however, the optimaization of the bottleneck line guaranteies stable production of its own line. And also, stable production of the bottleneck line guaranteies the total optimization-oriented production plans of the entire works. We think that partial optimization is need to realize total optimization.
2. CONSIDERATIONS ABOUT TOTAL OPTIMIZATION AND PARTIAL OPTIMIZATION
2. J Approach to total optimization
Prior to the introduction of the new production planning system, improvement activities were carried out to achieve the goal of improving the specific productivity of each production line. The evaluation functions were to achieve the desired production level and to reduce the operating cost. To meet the evaluation functions, each production line normally carried large work-in-process inventories and aimed at the largest possible production lots. There were two big problems from the viewpoint of the entire works. One was the obstruction of stable production. The production variation of the upstream line influenced the amount of work-in-process inventory at the entry end of the downstream line. As a result, there occurred many events in which a resource shortage forced the shutdown of the line. The other problem was the delay in delivery. Since each line gave priority to production lot size increase over delivery due date, there occurred many delivery delays. The root cause of these problems was that each line pursued productivity improvement without
3. PRODUCTION PLANNING SYSTEM ORIENTED TOWARD TOTAL OPTIMIZATION The purposes of the production planning system oriented toward the total optimization of the entire works are as follows: (1) Reduction in production and shipping lead time (2) Reduction in product inventory (3) Improvement in due date achievement rate The system we have developed to meet these purposes is based on two improvement mechanisms. One is the mechanism to determine a shipping date from a ship allocation plan and to calculate the production start date. This is effective in improving the shortening the shipping lead time, reducing the product inventory and the due date achievement rate . The other is the mechanism to prepare production
930
plans for the entire works, starting with the production plan of the bottleneck line. This aims at shortening the production lead time.
(2)The production lots for the downstream line are formed at such timing that the semifInished products are supplied from the bottleneck line. (3)The production plans for the upstream and downstream lines are prepared to keep the due dates.
3. J Ship allocation plans
The new production planning system automatically generates the production plan for the bottleneck line and the production plans for the upstream and downstream lines. The production planners confirm the respective results and determines the production plans after making some modifications as required. Fig.2 shows typical results of production planning.
The products of the works are mainly transported by ship. A ship to transport the products was arranged for when the production of the products was completed. The new production planning system allows the works to prepare a ship allocation plan from due date information by considering past ship allocation patterns, smoothening of shipping tasks and smoothening of production volume by type of product. The ship allocation plan can not only determine ship arrival dates but also calculate the shipping dates of products from the ship arrival dates. If the production start date of a particular product is calculated backward from the shipping date of the product, for example, the product can be produced to meet the shipping date, and the lead time from production completion to shipping can be sharply reduced.
fi'XX'jI 02"288102"298103"018 03"028 03"038 03"048103"058 ~ 1jI1Z. ~11jI Z. ~11jI1Z.1~ IjI Z. ~ IjI
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3.2 Production planning system starting at bottleneck line
Fig.2 . Results of production planning The production planning of the entire works flow is shown in Fig. I. 4. APPROACH TO PARTIAL OPTIMIZATION I
The overall production plan is affected by the bottleneck line. It is thus preparing a production plan starting at the bottleneck line at first. The production plan for the bottleneck line is prepared by taking the following two points into account: (1) Prioritize the product types that can form the largest production lots as required to ensure desired productivity. (2) Prioritize the due date of semifInished products to be assembled into lots. Based on the production plan for the bottleneck line, production plans are prepared for the upstream and downstream lines. In that case, the following points are taken into account: (I)The production plan for the upstream line is prepared to guarantee the supply of semifInished products to the bottleneck line.
4.1 Overview ofprocess to be partly optimized
The subject of partial optimization is the continuous process line. The continuous process line is the bottleneck line through which 40% of the products to be produced at the works pass. It is controlled as one of the most important lines at the works. Its conflgration is schematically illustrated Fig.4. The role of the line is to conduct various precision working operations on completely shaped serniflnished products. The continuous process line has the following characteristics: (l)Multiple processes are arranged in series and continuously. (2)Each process has individual constraints. (Any failure to meet the constraints may cause a quality or equipment trouble.) (3)Yield and operating cost vary with the sequence in which the semifinished products pass through the line.
(I) Prepare production plan for bottleneck line.
The point of production planning for the continuous process line is to determine the sequence in which the semifInished products in a production lot pass through the continuous process line. A processing sequence plan is prepared for each production lot. Fig. I. Priodution planning flow
931
Then, they modify the solution by adding or deleting the semifinished products and determines a final processing sequence. Optimization by genetic algorithm (GA) ; There are many evaluation functions that are in trade-off relations to each other. It is impossible to optimize all evaluation functions at the same time. There is no guarantee that there is a processing sequence that can meet all of the constraints. When we developed the new production planning system, we were requested by the production planners that the computing time to prepare a complex and difficult processing sequence plan should not exceed ten minutes. We studied optimization algorithms to solve this problem. As a result, we decided on the adoption of the genetic algorithm (GA) as solution logic. The reasons are as follows: (l)The GA can get an approximately optimal solution in a short time. (2)The GA allows the easy addition and modification of evaluation functions and constraints. (3)We can implement in a shorter time than other optimization algorithms.
P3
P2
PI
Continuous Process Line P3
P2
PI
Fig.3. Overview of continuous process line The semifmished products assembled into the production lot number a maximum of 300 and an average of 200. The production planners indicated to the line in question a processing sequence plan checked by using various evaluation functions (e.g.• amount by which the sernifinished product size changes. number of heat treating furnace changes. and number of working tool changes). To resolve the constraint violation or improve the evaluation functions, however, the line operator sometimes modified the processing sequence by using unscheduled semifmished products. This is because it is difficult to search for an optimal processing sequence plan within limited time and there are too many constraints to be judged by the production planners.
The characteristics of the GA-based optimization system developed by us are described below. (l)Method for preparation initial solutions The initial solutions are the start point for the GA. Its quality predominantly determines the convergence time of solutions. When production planners were investigated. it was found that the evaluation function they made the most of during the preparation of processing sequence plans was the size(e.g.• width, thickness) change. It was thereupon decided to introduce this thinking when preparing the initial solution. (2)Method for determining number of generations and number of individuals The number of generations and the number of individuals per generation were determined by finding a combination with good balance between solution optimality and computing time after trying various combinations. The number of individuals per generation is 100, and the number of generations is 500. The number of generations can be changed by the production planners.
4.2 Preparation ofoptimal processing sequence plan Man-machine role allocation considerations; The flow of the tasks performed by the production planners to prepare a processing sequence plan is shown in Fig. 5. The production planners first obtains an approximately optimal processing sequence for the semifinished products according to the due date and other constraints by using the computer logic (GA). Unplanned Semifinished Products
Pinned Semifmished Products
(3)Treatrnent of evaluation functions There are various evaluation functions. so that the fitness value was formulated by using a weighing factor. This approach can obtain a solution that emphasizes a particular criterion and can prioritize the evaluation functions. The production planners can change the weighting factor and can arbitrarily control the trade-off between the evaluation functions. Modification; The production planners modify the approximately optimal solution computed by the GA and determines the final processing sequence plan. The modification involves deleting several semifinished products from the solution or adding
approximately oplimal Sequence
Fig.4. Flow prepareation.
of
processing
sequence
plan
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The shipping lead time is sharply reduced compared with the target value. This is a result of the fact that the functions of determining the production start date from the shipping date and preparing a production plan starting with the bottleneck line have properly worked. In other words, semifinished products are supplied to respective lines in a balanced manner by the total optimization-oriented production plans.
semifinished products to the solution from a group of unplanned semifinished products. The modification may be made to the approximately optimal solution in two cases. In one case, an attempt is made to eliminate constraints. The GA system developed by us has no function of changing given semifinished products. For this reason, semifmished products that adversely affect the entire processing sequence cannot be removed, inevitably resulting in constraint violation. In the other case, an attempt is made to improve further the evaluation functions of the solution. The production planners attempt to improve the solution produced by the GA by deleting semifmished products from the solution or adding semifinished products to the solution from a group of unplanned semifinished products. Table 1 shows an example in which the number of violated constraints and the overall evaluation are improved as a result of such attempt.
5.2 Evaluation and problems ofprocessing sequence planning system
Table 3 compares the new and old systems in the processing sequence planning time. The realized automatic planning time is shorter than the target value and is shorter by as much as 82 minutes than that computed by the old system. Table 3 Comparison of processing sequence planning system
Table I Relationship between number of violated constraints and overall evaluation before and after modification Before modification #of Overall violated evaluation constraints Lot A Lot
, B
16
B
10
A
After modification # of Overall violated evaluation constraints
o o
A
15
8
o
5. EVALUATION
5.1 Evaluation of total optimization-oriented production planning system
Table 2 shows how much the production lead time, shipping lead time and inventory are improved by the introduction of the new production planning system.
Target
Realized
Production Lead Time
30.1 days
19.7 days
19.2 days
TargetRealized -0.5 days
Shipping Lead Time
30.5 days
13.9 days
10.5 days
-3.4 days
Inventory Reduction
100%
50%
47.3%
-2.7 %
IOmin
8 min
Modification
120min
60min
70min
IOmin
Realized
6. CONCLUSIONS The new production planning system was released in May 1999 and has been smoothly operating since then. It has performed better than expected and has achieved higher productivity and shorter lead time. The results of production planning have come to directly govern the productivity of the works, and the philosophy of production planning has completely switched from emphasis on the productivity of individual lines to increase in the throughput (shipment) of the entire works. This has been achieved with the optimization of individual lines guaranteed. Especially, we evaluate that optimization
Table 2 Target Vs Realized Old
90min
Target
The realized modification time is longer than the target value. We first attempted to develop a mechanism for computing the necessary modification by computer, but then decided to exclude this modification from the scope of automatic computation by computer because the computing time is prolonged by the addition of the knapsack problem and because it is very difficult to leave final human judgment to logic. We thought it important to clarify the roles of the human and the computer rather than to develop a system that automatically computes all things. Judgment of optimality constantly changes and regrettably is subject to the personality of people who make the judgment. More desirable solutions than obtainable from automatic logic should be derived by utilizing people who have a wealth of know-how.
A
# of semi-finished products Deleted added Lot A Lot B
Automatic Plannnig
Target Realized -2 min
Old
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of the bottleneck line plays an important role in total optimization of the works. There are two future problems. One is to increase the throughput further. The throughput can be increased to enjoy greater than ever profits if such a system is built that products in demand can be supplied faster and cheaper in response to market trends. The bottleneck line changes with the proportions of individual products (product mix). We think it also important to develop a system capable of flexibly coping with the change in the bottleneck line. The other problem is to decrease the modification work load during processing sequence plan preparation. We are planning to develop the functions of deleting and adding semifinished products by focusing only on the resolution of constraint violation. We have made only the first step from the standpoint of supply chain management (SCM). We will go ahead with optimization of the entire supply chain.
REFERENCES H.Morihisa, H.Ikeda, H.Uchibori, R.Oshita, and N.Komoda (1999). An Infonnation System Infrastructure and Applications for Improving White Collar Productivity in a Steel In: Information Manufacturing Works. Infrastructure Systems for Manufacturing (I.Mills and F.Kimura(Ed.), pp.229-247, Kluwer Academic Publishers. I.Okinaka (1986). Problems in the Production Management System of the Steel Industry - An Approach to the Problem of Multi-Purpose Decision- Making-. In: Preprint of • -th International Conference on Multiple Criteria Decision Making -Toward Interactive & Intelligent Decision Suppon Systems-, Vo1.2, pp.551-560. Mitchell, M. (1996). An Introduction To Genetic Algorithms. The MIT Press. S.Sugizaki, H.Ikeda, T.Kitabayashi, H.Morihisa, and Y.Ikkai (1999). Construction of Integrated Manufacturing Optimization System in Material Industry. In Proc. of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'99), pp.1329-1337, Barcelona, Spain. Z.Imaoka (1998). Supply Chain Management. Kogyo Chousa Publishing.
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