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1 241 Journal of Technology, Vol. 30, No. 3, pp (2015) * A SYSTEM DEVELOPMENT OF INTELLIGENT SCHEDULING UNDER SELECTED OBJECTIVE AND PRODUCTS MANAGEMENT Yun-Cheng Huang Cheng-Yang Yu* Department of Industrial Management National Pingtung University of Science and Technology Pingtung, Taiwan 912, R.O.C. Key Words: scheduling, dispatching rule, revised genetic algorithm. ABSTRACT This study examined the OEM manufacturer of parts to develop a products management and production scheduling system. Besides purchase, sales and inventory management, we proposed an intelligent scheduling system that search for an optimal scheduling result depend on different performance indicators. The searching steps are as follows. At first, the first-in/first-out method was taken, and then shortest processing time, earliest due date and critical ratio methods were followed. If the results were not good enough, then a revised genetic algorithm method was applied to improve the scheduling results. In general, the proposed system can improve the scheduling performance more efficiently than the traditional scheduling approach. * M @mail.npust.edu.tw Corresponding author: Cheng-Yang Yu, M @mail.npust.edu.tw

2 () () () Stevenson [1] Blackstone [2] 2. Low [3] Loukil [4] Prot Morineau [5] [4] Holland [6] Adaptation in Natural and Artificial Systems Holland [6] DNA Goldberg [7] Ramteke Srinivasan [8] Balin [9] Guo [10] Yang [11] Koksalan Keha [12] Oh Wu [13] 1.

3 (kg) () 2.7 g/cm 3 R (kg)r (kg) R () 7.9 g/cm 3 R (kg)r (kg) () 8.9 g/cm 3 R (kg)r 2 Visual Basic 2008 SQL () (1) Factorylist Factorylist () fid () fname ()address ()call () cellphone ()mail ()notes () fid fname (2) buylist Buylist () No ()fname ()mname ()buydate ( )amount () No fname factorylist mname materiallist (3) Materiallist Materiallist () mid () mname ()fname ()unit ( )notes ()R () mid

4 244 4 mname fname factorylist (4) Stocklist buylist Stocklist () No ()fname ()mname ()date () buydate ()amount ()stockcost orderstatustransactionqualitystatussign Nofnamemnamebuydate buylist buylist stocklist (5) Materialcheck materialcheck () No ( )mname ()date ()amount ( )mtake () No mname materiallist (6) Productlist productlist () pid ()cname ( )pname ()unit ()note ( )machinetype ()singlep () pid cname customerlist (7) Customerlist customerlist () cid () cname ()address ()call () cellphone ()mail ()notes () cid cname (8) Orderlist orderlist () No ()pname ( )cname ()amount ()total ()data ()duedate () worktime ()outsourcing () machine ()avibletime ()rvalue (CR ) No pname productlist cname customerlist (9) Productcheck productcheck () No ( )pname ()date ()amount ()ptake () No pname productlist (10) Shippinglist orderlist shippinglist () No () cname ()pname ()amount ( )total ()data ()realday ( )saleprice ()salestatusa () Transaction ()note () Nocnamepnamedata orderlist orderlist shippinglist (11) productdetail productdetail () No (

5 245 )pname ()detailname () material ()long () No pname productlist () ( 4) 4. (FCFS) (SPT) (EDD) (CR value) (genetic algorithms, GA) 5 () (1) (2) (3) (4) () (FCFS) (SPT) (EDD) (CR value). 5. (1) (FCFS) (1) Ascending 5 (AC i ) Rank AC, i (1) Ascending i (2) (SPT) (2) OP i i OP i W i + O i Ascending Rank OP, i (2) Ascending i (3) (EDD) (3) DU i Ascending Rank DU, i (3) Ascending i (4) (CR Value) SPT EDD

6 246 (1) (5) i i 1 i 0 0 DTn MIN DUij F ij Wij Oij,0 (5) i j 6 (4) (AV i ) (OP i ) Ascending (2) (6) F i F i i (S i ) i (W i ) i (O i ) AV i Rank, i Ascending OPi (4) ND n n 1, if Fi DUi i i i1 0, if Fi DUi (6) 5. () () () () () 6 () () (3) (7) i j (Q ij ) j ( j ) j (P j ) j (C j ) Q ij j j (PU j ) i j (Q ij ) i (F i ) i (DU i ) 0 Max T Max Q PU Q Max F DU n ij j j ij i i,0 i1 (7) ()

7 (1) 7 (2) Shaffer [14] 0.75~ () De Jong [15] Shaffer [14] Grefenstette [16] () N M M ( 11) ( 12) 12 2.

8 () ( 13) () ( 14) () ( 15)

9 ( 12) ( 17) + ( 17) () ( 16) 3. ( 18) () () ()

250 () (FCFS) 156-175-183-157-187-191-195-176-180-184-158-1 88-192-196-177-181-185-159-189-193-197-178 (SPT) 177-181-183-191-192-193-184-185-178-180-175-1 96-156-195-197-188-176-157-187-189-158-159

10 250 () (FCFS) (SPT) (EDD) (CR) ,882, ,390, ,295, ,190, ,008,160 ( ) 2 3 ( 19) () ( FCFSSPTEDDCR Value ) ( ) n i i = 1, 2, 3,, n

11 251 j j= 1, 2, 3,, J AV i i AC i i O ij i j O i i O i = Oij j tu j j Q ij i j W ij i j W ij = tu j Q ij W i i W i = Wij j OP i i OP i = W i + O i S i i F i i F i = S i + W i + O i DU i i DU i = DU ij DU ij i j i P j j C j j j j j = P j C j PU j j i j F ij 1. Stevenson, W. J., Production and Operations Management, Second Edition, Irwin, Homewood, IL (1986). 2. Blackstone, J. H., Phillips, D. T., and Hogg, G. L., A State-of-the-art Survey of Dispatching Rules for Manufacturing Job Shop Operation, International Journal of Production Research, Vol. 20, No. 1, pp (1982). 3. Low, C., Hsu, C. J., and Su, C. T., A Modified Particle Swarm Optimization Algorithm for a Single-Machine Scheduling Problem with Periodic Maintenance, Expert Systems with Applications, Vol. 37, No. 9, pp (2010). 4. Loukil, T., Teghem, J., and Fortemps, P., A Multiobjective Production Scheduling Case Study Solved by Simulated Annealing, European Journal of Operational Research, Vol. 179, No. 3, pp (2007). 5. Prot, D. and Bellenguez-Morineau, O., Tabu Search and Lower Bound for an Industrial Complex Shop Scheduling Problem, Computers & Industrial Engineering, Vol. 62, No. 4, pp (2012). 6. Holland, J. H., Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA (1992). 7. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co, Inc. Boston, MA (1989). 8. Ramteke, M. and Srinivasan, R., Novel Genetic Algorithm for Short-term Scheduling of Sequence Dependent Changeovers in Multiproduct Polymer Plants, Computers and Chemical Engineering, Vol. 35, No. 12, pp (2011). 9. Balin, S., Non-identical Parallel Machine Scheduling Using Genetic Algorithm, Expert Systems with Applications, Vol. 38, No. 6, pp (2011). 10. Guo, Z. X., Wong, W. K., Li, Z., and Ren, P., Modeling and Pareto Optimization of Multi-objective Order Scheduling Problems in Production Planning, Computers & Industrial Engineering, Vol. 64, No. 4, pp (2013). 11. Yang, C., Gao, J., and Sun, L., A Multi-objective Genetic Algorithm for Mixed-model Assembly Line Rebalancing, Computers & Industrial Engineering, Vol. 65, No. 1, pp (2013). 12. Köksalan, M. and Keha, A. B., Using Genetic Algorithms for Single-Machine Bicriteria Scheduling Problems, European Journal of Operational Research, Vol. 145, No. 3, pp (2003). 13. Oh, J. and Wu, C., Genetic-Algorithm-Based Real-Time Task Scheduling with Multiple Goals, Journal of Systems and Software, Vol. 71, No. 3, pp (2004). 14. Shaffer, J. D., Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, Proceedings of 1 st International Conference on Genetic Algorithms, Pittsburgh, PA, pp (1985). 15. De Jong, K. A., An Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D Dissertation, University of Michigan, Ann Arbor, MI (1975). 16. Grefenstette, J. J., Optimization of Control Parameters for Genetic Algorithms, IEEE Transactions on Systems, Man and Cybernetics, Vol. 16, No. 1, pp (1986)

92 (When) (Where) (What) (Productivity) (Efficiency) () (2) (3) (4) (5) (6) (7) em-plant( SiMPLE++) Scheduling When Where Productivity Efficiency [5]

92 (When) (Where) (What) (Productivity) (Efficiency) () (2) (3) (4) (5) (6) (7) em-plant( SiMPLE++) Scheduling When Where Productivity Efficiency [5] DYNAMIC SCHEDULING IN TWO-MACHINE FLOW-SHOP WITH RECIRCULATION em-plant( SiMPLE++) Jen-Shiang Chen, Jar-Her Kao, Chun-Chieh Chen, Po-Cheng Liu, and Wen-Pin Lin Department of Industrial Engineering and