5.1、結論
隨著TFT-LCD 產業走向上下游垂直整合之趨勢,附帶提高彩色濾光片 內製化之需求及比例。然而在內製廠環境中,須針對下游組立製程所開立 之需求數量及交期,完善地考量瓶頸資源、附屬資源─光罩、以及外包之 數量及種類限制下,制定出自製及外包之訂單最適配置種類及數量,及自 製產能之最佳生產順序,用以回覆上層管理單位交貨時點及下層生產單位 確切之生產目標。
有鑒於此,本文針對彩色濾光片內製廠之生產特性,建構一主生產排 程機制。該機制包含兩個模組:
一、 產能估算模組
本模組針對規劃幅度內之總需求數量,進行初步之產能估算;於估算 過程中,依序判斷瓶頸機台產能與外包數量限制是否能滿足總需求量,以 及「現有光罩數」及「總需求所推估出的預計光罩數」間之比對。藉由此 模組過濾出不合理之需求,以利後續模組之進行。
二、 主生產排程規劃模組
承接產能估算模組評估後之合理需求,依續對各筆訂單依交期時間序 進行規劃。針對產能估算模組評估後的需求情境,分別以「產能充足排程 模式」、「產能不足排程模式」執行最佳解之計算,並考量機台製程規格能 力、光罩數量、機台產能、外包合約數量等限制,求解出該期之主生產排 程結果。接續運用滾動排程及暫存排程之概念,以及外包縮減機制之執 行,完成一個月之主生產排程。規劃結果囊括各期別採自製生產時,各機 台之最佳生產順序、產品種類及數量、光罩調度結果、以及交予外包商生 產之各期產品種類與數量。
藉由第四章之實例驗證結果分析,將本文之成效彙整如下:
1. 考量淡、旺季之情境,設計規劃機制提供管理者制定自製及外包種類 與數量之決策依據。
2. 同時考量各機台製程規格能力及光罩數量限制,可明確得知各光罩於 不同機台間之調度結果。再者,針對順序相依之排程問題求解出最適 之生產順序;以利生產活動之進行。
3. 採用變動規劃週期之設計進行分段規劃,可大幅減少變數個數及瓶頸 機台之排程複雜度。
4. 可假設所有訂單之備料完成時點皆為 0,對整個規劃幅度進行排程之 求解,以推導出各訂單之產品別的最適備料完成時間,有助於成本最 小化之追求。
5. 將求解結果經由撰寫 Excel VBA 自動產出甘特圖,提供管理者圖示化 結果以供參考。
5.2、未來研究方向
綜觀本文所發展之主生產排程機制,在其過程中查覺仍有不足之處,
值得後續研究繼續深入探討,彙整如下:
1. 在彩色濾光片產業中,除了本文提及之換線規格外(光罩、光阻液、玻 璃規格),為節省換線時間,於實務環境中存在「不同玻璃規格間之替 代」,即採用同一尺寸之玻璃用以生產不同尺寸之面板,但此舉動將會 造成額外之成本花費。如何在「減少換線時間」與「增加成本」兩者 中取捨為可探討議題。
2. 以往組立製程相關之研究,將薄膜電晶及彩色濾光片視為來料直接進 行後續規劃。但陣列及彩色濾光片製程本身之生產型態、製程特性及 目標皆不相同;再者,不同世代之陣列及彩色濾光片製程可互相支援,
因此協同兩製程進行生產規劃為當務之急。有鑑於此,若能考量「陣 列及彩色濾光片」兩者進行生產規劃,將有助於TFT-LCD 產業制定出 完善之生產排程結果。
129
參考文獻
[1] Adenso-Diaz, B. and Laguna, M., “Modelling the load levelling problem in master production scheduling for MRP systems,” International Journal
of Production Research, Vol.34, pp.483–493, 1996.
[2] Akturk, M.S., “An exact tool allocation approach for CNC machines,”
International Journal of Computer Integrated Manufacturing, Vol.12,
No.2, pp.129-140, 1999.[3] Avic, S. and Akturk, M.S., “Tool magazine arrangement and operations sequencing on CNC machines,” Computers Operation Research, Vol.23, No.11, pp.1069-1081, 1996.
[4] Baker, K. R., “An experimental study of the effectiveness of rolling schedules in prodcution planning,” Decision Sciences, 8(1), 19-27, 1977.
[5] Bing, W., Jie S., “Two-level rolling strategy for single-machine scheduling problem with release times,”In Proceedings of the 5th
World Congress on Intelligent Control and Automation, June 15-19, 2004, Hangzhou, P.R.
China.
[6] Blackburn, J.D., Kropp, D. H., and Millen, R.A., “Comparisons of strategies to dampen nervousness in MRP systems,” Management Science, 32, 413-429, 1986.
[7] Buyurgan, N., Saygin, S., and Kilic, S.E., “Tool allocation in flexible manufacturing systems with tool alternatives,” Robotics and
Computer-Integrated Manufacturing, Vol.20, pp.341-349, 2004.
[8] Carlson, R. C., Beckman, S. L., and Kropp, D. H., “The effectiveness of extending the horizon in rolling production scheduling”, Decision Sciences, 13, 129-146, 1982.
[9] Chand, S., Traub, R. and Uzsoy, R., “Rolling horizon procedures for the single machine deterministic total completion time scheduling problem with release dates”, Annals of Operations Research, 70, 115-125, 1997.
[10] Chen, T. R. and Hsia, T. C., “Job shop scheduling with multiple resources
and an application to a Testing Facility,” In Proceedings of the 33rd
conference on Decision and Control, Lake Buena Vista, pp. 1564-1570,
1994.[11] Chen, D.,Luh, P. B., Thakur, L. S., and Moreno, J. J., “Optimization-based manufacturing scheduling with multiple resources, setup requirements, and transfer lots,” IIE Transactions, Vol. 35, pp. 973-985, 2003.
[12] DisplaySearch Quarterly Color Filter Report, 2006.
[13] Gargeya, V.B. and Deane R.H., “Scheduling in the dynamic job shop under auxiliary resource constraints: a simulation study,” International
Journal of Production Research, Vol.37, No.12, pp.2817-2834, 1999.
[14] Johnson, S.M., “Optimal two- and three-stage production schedules with setup times included.” Naval Research Logistics Quarterly, Vol 1, pp.
61–68., 1954.
[15] Kayaligil, S. and Ozlu, M., “Loading of pallets on identical CNC machines with cyclic schedules,” Computers & Industrial Engineering, pp.221-230, 2002.
[16] Kropp, D. H .and Carlson, R. C., “A lot-sizing algorithm for reducing nervousness in MRP systems,” “Management Science,” 30, 240-244, 1984.
[17] Low, C.Y, Hsu C.J. and Su C.T., “A two-stage hybrid flowshop scheduling problem with a function constraint and unrelated alternative machines,”
Computers & Operations Research, Vol.35, No.3, pp.845-853, MAR,
2008.[18] Proust, C., Gupta, J.N.D. and Deschamps, V., “Flowshop scheduling with set-up, processing and removal times separated,” International Journal of
Production Research, Vol.29, pp.479-493, 1991.
[19] Sridharan, V., Berry W.L., Udayabhanu V., “Measure master production schedule stability under rolling planning horizons”, Decision Sciences, Vol.
19, pp. 147-166, 1988.
131
[20] Sule, D.R., “Sequencing n jobs on two machines with setup, processing and removal times separated,” Naval Research Logistics Quarterly, Vol.29, pp.517-519, 1982.
[21] Sule, D.R., and Huang, K.Y., “Sequency on two and three machines with setup, processing and removal times separated,” International Journal of
Production Research, Vol.21, pp.723-732, 1983.
[22] Wang, K. J. and Hou, T.C., “Modeling and resolving the joint problem of capacity expansion and allocation with multiple resources and a limited budget in the semiconductor testing industry,” International Journal of
Production Research, Vol.41, pp.3217-3235, 2003.
[23] Yamada, Y., Matui, T. and Sugiyama, M., “New analysis of efficiency based on DEA,” Journal of the Operations Research Society of Janpan, Vol.37, No.2, pp.158-167. 1993.
[24] Yang, D.L., Hsu, C.J. and Kuo, W.H., “A two-machine flowshop scheduling problem with a separated maintenance constraint,” Computers
& Operations Research Vol.35, No.3, pp.845-853 MAR 2008.
[25] Yoshida, T. and Hitomi, K., “Optimal two-stage production scheduling with setup times separated,” AIIE Transactions, Vol.11, pp.261-263, 1979.
[26] Zhang, X., Fujii, S. and Kaihara, T., “Eval uation of tool allocation strategies in flexible manufacturing system,” JSME International Journal,
Series C, Vol.48, No. 1, 2005.
[27] Zhang, Z., Zhang, M.T., Niu, S. and Zheng L., “Capacity planning with reconfigurable kits in semiconductor test manufacturing” International
Journal of Production Research, Vol.44, No.13, pp.2625-2644, 1 July,
2006.[28] 李俊昇,「液晶面板組裝廠批量製程派工法則之設計」,國立交通大學 工業工程與管理研究所,碩士論文,民國92 年。
[29] 周威良,「彩色濾光片專業廠主生產排程機制之構建」,2008 年國科 會計畫書。
[30] 胡雅傑,「彩色濾光片生產之批量排程」,清華大學工業工程研究所,
碩士論文,民國92 年。
[31] 溫大君,「TFT-LCD 產業中彩色濾光片製造業的運籌管理解決方案」,
國立清華大學工業工程與工程管理研究所,碩士論文,民國94 年。
[32] 詹宗憲,「搜尋法應用於彩色濾光片之生產排程」,清華大學工業工程 研究所,碩士論文,民國95 年。
[33] 楊東琦,「晶圓針測廠考量針測卡資源限制下主生產排程系統之設 計」,國立交通大學工業工程與管理學系,碩士論文,民國96 年。
[34] 劉美君,「2005 年全球彩色濾光片產業發展回顧與未來動向」,工研院 IEK-ITIS 計畫。
[35] 賴建良,「彩色濾光片三原色製程之批量與排程問題」,清華大學工業 工程研究所,碩士論文,民國94 年。
[36] 謝仲為,「先進規劃與排程系統應用於TFT-LCD 產業之研究」,東海大 學工業工程與經營資訊研究所,碩士論文,民國91 年。
[37] 顏如敏,「TFT Array 廠在光罩限制下之現場排程問題」,國立清華大 學工業工程與管理學系,碩士論文,民國95 年。
133
附錄
附錄A iLOG 求解結果
輸 出 結 果 與 變 數 之 對 應 為 :w[i,m,t]=ωi,m,t、BG[i,m,t]=BGi,m,t、 FG[i,m,t]=FGi,m,t、alpha[i,m,t]=αi,m,t、beta[i,m,t]=βi,m,t、gamma[i,i’,m,t]=γi,i’,m,t、 O[i,t]=Oi,t、X[i,m,t]=Xi,m,t、delta[a,m,t] =δa,m,t、Y[i,t]=Yi,t、OY[i]=OYi,此外 下列表格皆併除數值為0 之變數。
A-1. 求解範圍(1,1)之完整規劃結果
t=1
alpha[1,1,1] = 1 beta[0,8,2,1] = 1 FG[5,2,1] = 1 X[7,3,1] = 3312 alpha[3,1,1] = 1 gamma[1,3,1,1] = 1 FG[10,3,1] = 1 X[8,2,1] = 3499 alpha[5,2,1] = 1 gamma[7,5,2,1] = 1 w[1,1,1] = 1 X[9,3,1] = 2300 alpha[5,3,1] = 1 gamma[7,11,3,1] = 1 w[3,1,1] = 1 X[10,3,1] = 880 alpha[6,2,1] = 1 gamma[8,7,2,1] = 1 w[5,2,1] = 1 X[11,3,1] = 2696 alpha[6,3,1] = 1 gamma[9,10,3,1] = 1 w[7,2,1] = 1 O[8,1] = 1 alpha[7,2,1] = 1 gamma[11,9,3,1] = 1 w[7,3,1] = 1 O[10,1] = 2120 alpha[7,3,1] = 1 delta[1,1,1] = 1 w[8,2,1] = 1 O[11,1] = 4
alpha[8,2,1] = 1 delta[2,2,1] = 1 w[9,3,1] = 1 --
alpha[8,3,1] = 1 delta[2,3,1] = 1 w[10,3,1] = 1 --
alpha[9,3,1] = 1 delta[3,3,1] = 1 w[11,3,1] = 1 --
alpha[10,3,1] = 1 BG[1,1,1] = 1 X[1,1,1] = 5000 --
alpha[11,3,1] = 1 BG[7,3,1] = 1 X[3,1,1] = 4070 --
beta[0,1,1,1] = 1 BG[8,2,1] = 1 X[5,2,1] = 5100 --
beta[0,7,3,1] = 1 FG[3,1,1] = 1 X[7,2,1] = 288 --
求解結果
目標值 =155,580 (自製成本:117,338、外包成本 38,242)
;決策變數 = 1,120;限制式 = 17,405; 求解時間 = 4.11 秒
A-2. 求解範圍(2,2)之完整規劃結果
t=2
alpha[2,1,2] = 1 beta[3,3,1,2] = 1 BG[10,3,2] = 1 X[5,2,2] = 2895 alpha[3,1,2] = 1 beta[5,5,2,2] = 1 FG[2,1,2] = 1 X[6,2,2] = 310 alpha[5,2,2] = 1 beta[10,10,3,2] = 1 FG[6,2,2] = 1 X[6,3,2] = 2313 alpha[5,3,2] = 1 gamma[3,2,1,2] = 1 FG[6,3,2] = 1 X[10,3,2] = 400 alpha[6,2,2] = 1 gamma[5,6,2,2] = 1 w[2,1,2] = 1 O[2,2] = 1849 alpha[6,3,2] = 1 gamma[10,6,3,2] = 1 w[3,1,2] = 1 O[3,2] = 5 alpha[7,2,2] = 1 delta[1,1,2] = 1 w[5,2,2] = 1 O[5,2] = 5 alpha[7,3,2] = 1 delta[2,2,2] = 1 w[6,2,2] = 1 O[6,2] = 477 alpha[8,2,2] = 1 delta[2,3,2] = 1 w[6,3,2] = 1 O[8,2] = 200 alpha[8,3,2] = 1 delta[3,3,2] = 1 w[10,3,2] = 1 O[11,2] = 400 alpha[10,3,2] = 1 BG[3,1,2] = 1 X[2,1,2] = 1151 O[12,2] = 500
alpha[11,3,2] = 1 BG[5,2,2] = 1 X[3,1,2] = 2295 --
求解結果
目標值 =106,832 (自製成本:45,804、外包成本 61,028)
;決策變數 = 1,120;限制式 = 17,369; 求解時間 = 3.83 秒
A-3. 求解範圍(3,3)之完整規劃結果
t=3
alpha[1,2,3] = 1 beta[6,6,3,3] = 1 FG[2,2,3] = 1 X[5,2,3] = 14899 alpha[2,2,3] = 1 gamma[1,2,2,3] = 1 FG[7,3,3] = 1 X[6,2,3] = 7895 alpha[5,2,3] = 1 gamma[5,1,2,3] = 1 FG[11,1,3] = 1 X[6,3,3] = 9205 alpha[5,3,3] = 1 gamma[6,5,2,3] = 1 w[1,2,3] = 1 X[7,3,3] = 18499 alpha[6,2,3] = 1 gamma[6,8,3,3] = 1 w[2,2,3] = 1 X[8,3,3] = 8647 alpha[6,3,3] = 1 gamma[8,7,3,3] = 1 w[5,2,3] = 1 X[9,1,3] = 8200 alpha[7,2,3] = 1 gamma[9,10,1,3] = 1 w[6,2,3] = 1 X[10,1,3] = 8285 alpha[7,3,3] = 1 gamma[10,12,1,3] = 1 w[6,3,3] = 1 X[11,1,3] = 6500 alpha[8,2,3] = 1 gamma[12,11,1,3] = 1 w[7,3,3] = 1 X[12,1,3] = 12799 alpha[8,3,3] = 1 delta[1,2,3] = 1 w[8,3,3] = 1 O[4,3] = 10500 alpha[9,1,3] = 1 delta[2,2,3] = 1 w[9,1,3] = 1 O[5,3] = 1 alpha[10,1,3] = 1 delta[2,3,3] = 1 w[10,1,3] = 1 O[7,3] = 1 alpha[11,1,3] = 1 delta[3,1,3] = 1 w[11,1,3] = 1 O[8,3] = 1153 alpha[12,1,3] = 1 BG[6,2,3] = 1 w[12,1,3] = 1 O[10,3] = 3755 beta[1,9,1,3] = 1 BG[6,3,3] = 1 X[1,2,3] = 12900 O[12,3] = 1
beta[6,6,2,3] = 1 BG[9,1,3] = 1 X[2,2,3] = 2000 --
求解結果
目標值 =814,372 (自製成本:536,978、外包成本 277,394)
;決策變數 = 1,120;限制式 = 17,369; 求解時間 = 4.78 秒
135
目標值 =814,384 (自製成本:536,988、外包成本 277,396)
;決策變數 = 1,144;限制式 = 17,431; 求解時間 = 4.66 秒
目標值 =406,212 (自製成本:120,456、外包成本 136,782、未排入成本 148,974)
;決策變數 = 1,144;限制式 = 17,431; 求解時間 = 4.73 秒
A-6. 求解範圍(4,4)之扣除未排入產品規劃結果
目標值 =257,238 (自製成本:120,456、外包成本 136,782)
;決策變數 = 1,144;限制式 = 17,431; 求解時間 = 4.25 秒
目標值 =325,840 (自製成本:325,840)
;決策變數 = 1,144;限制式 = 17,431; 求解時間 = 4.80 秒
137
A-9. Case6 求解範圍(4,5)之完整規劃結果
t=4
alpha[1,1,4] = 1 beta[1,5,2,4] = 1 BG[5,2,4] = 1 X[1,1,4] = 1319 alpha[2,1,4] = 1 beta[5,5,3,4] = 1 BG[5,3,4] = 1 X[2,1,4] = 4000 alpha[3,1,4] = 1 beta[10,2,1,4] = 1 FG[4,1,4] = 1 X[3,1,4] = 4980 alpha[4,1,4] = 1 gamma[1,3,1,4] = 1 FG[6,2,4] = 1 X[4,1,4] = 1700 alpha[5,2,4] = 1 gamma[2,1,1,4] = 1 FG[9,3,4] = 1 X[5,2,4] = 7246 alpha[5,3,4] = 1 gamma[3,4,1,4] = 1 w[1,1,4] = 1 X[5,3,4] = 54 alpha[6,2,4] = 1 gamma[5,6,2,4] = 1 w[2,1,4] = 1 X[6,2,4] = 5173 alpha[6,3,4] = 1 gamma[5,7,3,4] = 1 w[3,1,4] = 1 X[7,3,4] = 4900 alpha[7,2,4] = 1 gamma[7,11,3,4] = 1 w[4,1,4] = 1 X[9,3,4] = 4960 alpha[7,3,4] = 1 gamma[11,9,3,4] = 1 w[5,2,4] = 1 X[11,3,4] = 2900 alpha[8,2,4] = 1 delta[1,1,4] = 1 w[5,3,4] = 1 O[6,4] = 2327 alpha[8,3,4] = 1 delta[2,2,4] = 1 w[6,2,4] = 1 O[10,4] = 1000 alpha[9,3,4] = 1 delta[2,3,4] = 1 w[7,3,4] = 1 O[12,4] = 1300
alpha[10,3,4] = 1 delta[3,3,4] = 1 w[9,3,4] = 1 --
alpha[11,3,4] = 1 BG[2,1,4] = 1 w[11,3,4] = 1 --
t=5
alpha[1,1,5] = 1 alpha[12,3,5] = 1 delta[3,3,5] = 1 w[9,3,5] = 1 alpha[2,1,5] = 1 beta[4,4,1,5] = 1 BG[4,1,5] = 1 w[10,3,5] = 1 alpha[3,1,5] = 1 beta[6,6,2,5] = 1 BG[6,2,5] = 1 w[12,3,5] = 1 alpha[4,1,5] = 1 beta[9,9,3,5] = 1 BG[9,3,5] = 1 X[1,1,5] = 2981 alpha[5,2,5] = 1 gamma[1,2,1,5] = 1 FG[2,1,5] = 1 X[2,1,5] = 11550 alpha[5,3,5] = 1 gamma[3,1,1,5] = 1 FG[5,2,5] = 1 X[3,1,5] = 6270 alpha[6,2,5] = 1 gamma[4,3,1,5] = 1 FG[7,3,5] = 1 X[4,1,5] = 1100 alpha[6,3,5] = 1 gamma[6,5,2,5] = 1 w[1,1,5] = 1 X[5,2,5] = 9760 alpha[7,2,5] = 1 gamma[8,7,3,5] = 1 w[2,1,5] = 1 X[6,2,5] = 9520 alpha[7,3,5] = 1 gamma[9,10,3,5] = 1 w[3,1,5] = 1 X[7,3,5] = 5800 alpha[8,2,5] = 1 gamma[10,12,3,5] = 1 w[4,1,5] = 1 X[8,3,5] = 4700 alpha[8,3,5] = 1 gamma[12,8,3,5] = 1 w[5,2,5] = 1 X[9,3,5] = 3770 alpha[9,3,5] = 1 delta[1,1,5] = 1 w[6,2,5] = 1 X[10,3,5] = 2750 alpha[10,3,5] = 1 delta[2,2,5] = 1 w[7,3,5] = 1 X[12,3,5] = 5200
alpha[11,3,5] = 1 delta[2,3,5] = 1 w[8,3,5] = 1 --
求解結果
目標值 = 577,204 (自製成本:493,918、外包成本 83,286)
;決策變數 = 2,264;限制式 = 35,255;求解時間 =352.67 秒
139
A-10. 淡季情境:對
l=1
求解範圍(1,1)之完整規劃結果t=1
alpha[1,2,1] = 1 alpha[10,1,1] = 1 BG[4,2,1] = 1 w[10,1,1] = 1 alpha[3,2,1] = 1 beta[0,4,2,1] = 1 BG[6,3,1] = 1 X[1,2,1] = 5300 alpha[4,2,1] = 1 beta[0,6,3,1] = 1 BG[9,1,1] = 1 X[3,2,1] = 4796 alpha[5,2,1] = 1 beta[0,9,1,1] = 1 FG[1,2,1] = 1 X[4,2,1] = 1000 alpha[5,3,1] = 1 gamma[3,1,2,1] = 1 FG[5,3,1] = 1 X[5,3,1] = 18000 alpha[6,2,1] = 1 gamma[4,3,2,1] = 1 FG[10,1,1] = 1 X[6,3,1] = 10500 alpha[6,3,1] = 1 gamma[6,5,3,1] = 1 w[1,2,1] = 1 X[9,1,1] = 6000 alpha[7,2,1] = 1 gamma[9,10,1,1] = 1 w[3,2,1] = 1 X[10,1,1] = 7500 alpha[7,3,1] = 1 delta[1,2,1] = 1 w[4,2,1] = 1 O[3,1] = 204 alpha[8,2,1] = 1 delta[2,2,1] = 1 w[5,3,1] = 1 O[11,1] = 4500
alpha[8,3,1] = 1 delta[2,3,1] = 1 w[6,3,1] = 1 --
alpha[9,1,1] = 1 delta[3,1,1] = 1 w[9,1,1] = 1 --
求解結果
目標值 =325,648 (自製成本:250,384、外包成本 75,264)
;決策變數 = 1,120;限制式 = 17,406; 求解時間 =3.5 秒
A-11. 淡季情境:對
l=2
求解範圍(1,1)之完整規劃結果t=1
alpha[1,2,1] = 1 beta[1,1,2,1] = 1 BG[1,2,1] = 1 w[11,1,1] = 1 alpha[2,2,1] = 1 beta[5,5,3,1] = 1 BG[5,3,1] = 1 w[12,1,1] = 1 alpha[4,2,1] = 1 beta[10,10,1,1] = 1 BG[10,1,1] = 1 X[1,2,1] = 4500 alpha[5,2,1] = 1 gamma[1,2,2,1] = 1 FG[6,3,1] = 1 X[2,2,1] = 4500 alpha[5,3,1] = 1 gamma[2,4,2,1] = 1 FG[7,2,1] = 1 X[4,2,1] = 2000 alpha[6,2,1] = 1 gamma[4,8,2,1] = 1 FG[11,1,1] = 1 X[5,3,1] = 18885 alpha[6,3,1] = 1 gamma[5,6,3,1] = 1 w[1,2,1] = 1 X[6,3,1] = 7500 alpha[7,2,1] = 1 gamma[8,7,2,1] = 1 w[2,2,1] = 1 X[7,2,1] = 6389 alpha[7,3,1] = 1 gamma[10,12,1,1] = 1 w[4,2,1] = 1 X[8,2,1] = 9300 alpha[8,2,1] = 1 gamma[12,11,1,1] = 1 w[5,3,1] = 1 X[10,1,1] = 5500 alpha[8,3,1] = 1 delta[1,2,1] = 1 w[6,3,1] = 1 X[11,1,1] = 4000 alpha[10,1,1] = 1 delta[2,2,1] = 1 w[7,2,1] = 1 X[12,1,1] = 5800 alpha[11,1,1] = 1 delta[2,3,1] = 1 w[8,2,1] = 1 O[5,1] = 1747 alpha[12,1,1] = 1 delta[3,1,1] = 1 w[10,1,1] = 1 O[7,1] = 3011
求解結果
目標值 =418,824 (自製成本:342,696、外包成本 76,128)
;決策變數 = 1,120;限制式 = 17,406; 求解時間 =3.47 秒
A-12. 淡季情境:對
l=3 及 4
求解範圍(2,3)之完整規劃結果t=2
alpha[1,2,2] = 1 alpha[11,1,2] = 1 delta[3,1,2] = 1 w[11,1,2] = 1 alpha[2,2,2] = 1 alpha[12,1,2] = 1 BG[6,3,2] = 1 w[12,1,2] = 1 alpha[3,2,2] = 1 beta[6,6,3,2] = 1 BG[7,2,2] = 1 X[2,2,2] = 3074 alpha[4,2,2] = 1 beta[7,7,2,2] = 1 BG[11,1,2] = 1 X[3,2,2] = 4080 alpha[5,2,2] = 1 beta[11,11,1,2] = 1 FG[2,2,2] = 1 X[4,2,2] = 1000 alpha[5,3,2] = 1 gamma[3,4,2,2] = 1 FG[8,3,2] = 1 X[6,3,2] = 7000 alpha[6,2,2] = 1 gamma[4,2,2,2] = 1 FG[9,1,2] = 1 X[7,2,2] = 5684 alpha[6,3,2] = 1 gamma[6,8,3,2] = 1 w[2,2,2] = 1 X[8,3,2] = 9805 alpha[7,2,2] = 1 gamma[7,3,2,2] = 1 w[3,2,2] = 1 X[9,1,2] = 9878 alpha[7,3,2] = 1 gamma[11,12,1,2] = 1 w[4,2,2] = 1 X[11,1,2] = 5700 alpha[8,2,2] = 1 gamma[12,9,1,2] = 1 w[6,3,2] = 1 X[12,1,2] = 3500 alpha[8,3,2] = 1 delta[1,2,2] = 1 w[7,2,2] = 1 O[7,2] = 3495
alpha[9,1,2] = 1 delta[2,2,2] = 1 w[8,3,2] = 1 --
alpha[10,1,2] = 1 delta[2,3,2] = 1 w[9,1,2] = 1 --
t=3
alpha[1,2,3] = 1 alpha[10,1,3] = 1 delta[3,1,3] = 1 w[10,1,3] = 1 alpha[2,2,3] = 1 alpha[12,1,3] = 1 BG[2,2,3] = 1 w[12,1,3] = 1 alpha[3,2,3] = 1 beta[2,2,2,3] = 1 BG[8,3,3] = 1 X[1,2,3] = 5000 alpha[4,2,3] = 1 beta[8,8,3,3] = 1 BG[10,1,3] = 1 X[2,2,3] = 1196 alpha[5,2,3] = 1 beta[9,10,1,3] = 1 FG[1,2,3] = 1 X[5,3,3] = 12000 alpha[5,3,3] = 1 gamma[2,1,2,3] = 1 FG[5,3,3] = 1 X[7,3,3] = 9321 alpha[6,2,3] = 1 gamma[7,5,3,3] = 1 FG[12,1,3] = 1 X[8,3,3] = 4194 alpha[6,3,3] = 1 gamma[8,7,3,3] = 1 w[1,2,3] = 1 X[10,1,3] = 8000 alpha[7,2,3] = 1 gamma[10,12,1,3] = 1 w[2,2,3] = 1 X[12,1,3] = 7000 alpha[7,3,3] = 1 delta[1,2,3] = 1 w[5,3,3] = 1 O[3,3] = 4420 alpha[8,2,3] = 1 delta[2,2,3] = 1 w[7,3,3] = 1 O[8,3] = 1 alpha[8,3,3] = 1 delta[2,3,3] = 1 w[8,3,3] = 1 O[9,3] = 622
求解結果
目標值 = 611,876 (自製成本:475,266、外包成本 136,610)
;決策變數 = 2,240;限制式 = 35,148;求解時間 =134.41 秒