Minimizing makespan in a three-stage flexible flowshop with identical machines and two batch processors

Σχετικά έγγραφα
Z L L L N b d g 5 * " # $ % $ ' $ % % % ) * + *, - %. / / + 3 / / / / + * 4 / / 1 " 5 % / 6, 7 # * $ 8 2. / / % 1 9 ; < ; = ; ; >? 8 3 " #


ACTA MATHEMATICAE APPLICATAE SINICA Nov., ( µ ) ( (

ØÖÓÒÓÑ ÈÖ Ø ÙÑ Ù Ò Ö Ò Ë Ð ØÛ ØØ Ö¹ ØÖÓÒÓÑ Íº Ù ÍÒ Ú Ö ØØ Ù ÙÖ ¹ Ò Ö ËÓÒÒ ÒÐ Ù Ñ Î ÖÐ Ù Ò Â Ö Ð ÙÒ ½ Û ÙÒ Ö ËÓÒÒ Ö Ò À ÑÑ Ð ÞÙ Ï ÒØ Ö Ò Ò Ö Ð Ò Ò Ò ÙÒ


Θεωρία Συνόλων. Ενότητα: Διατακτικοί αριθμοί. Γιάννης Μοσχοβάκης. Τμήμα Μαθηματικών

2 SFI

Ó³ Ÿ , º 2(214).. 171Ä176. Š Œ œ ƒˆˆ ˆ ˆŠ

ˆ Œ ˆŸ Š ˆˆ ƒ Šˆ ƒ ƒ ˆ Šˆ ˆ ˆ Œ ˆ

Ó³ Ÿ , º 7(163).. 855Ä862 ˆ ˆŠ ˆ ˆŠ Š ˆ. . ƒ. ² ͱ 1,.. μ μ Íμ,.. μ²ö,.. ƒ² μ,.. ² É,.. ³ μ μ, ƒ.. Š ³ÒÏ,.. Œμ μ μ,. Œ.

Ηυλοποίησ ητηςπαραπάνωκατηγορίαςβρίσ κεταισ τοναλγόριθμο º¾ºΗγραμμή

P ƒ.. Š ³ÒÏ,.. Š ³ÒÏ,.. ± ˆ ŒˆŠˆ Š ˆŠ

UDC. An Integral Equation Problem With Shift of Several Complex Variables 厦门大学博硕士论文摘要库

Αλγόριθμοι Δικτύων και Πολυπλοκότητα Προσεγγιστικοί Αλγόριθμοι. Άρης Παγουρτζής

p din,j = p tot,j p stat = ρ 2 v2 j,

Ανώτερα Μαθηματικά ΙI

Ó³ Ÿ , º 5(147).. 777Ä786. Œ ˆŠ ˆ ˆ Š ƒ Š ˆŒ. ˆ.. Š Öαμ,. ˆ. ÕÉÕ ±μ,.. ²Ö. Ñ Ò É ÉÊÉ Ö ÒÌ ² μ, Ê

Ó³ Ÿ , º 2(131).. 105Ä ƒ. ± Ï,.. ÊÉ ±μ,.. Šμ ² ±μ,.. Œ Ì ²μ. Ñ Ò É ÉÊÉ Ö ÒÌ ² μ, Ê

P Ë ³μ,.. μ μ³μ²μ,.. ŠμÎ μ,.. μ μ,.. Š μ. ˆ œ ˆ Š Œˆ ŠˆŒ ƒ Œ Ÿ ˆŸ Š ˆ ˆ -ˆ ˆŠ

½ Τετραγωνίζω=κατασκευάζωκάτιίσουεμβαδούμεδοθέντετράγωνο. Δείτεκαιτην υποσημείωσηστηνπρότασηβ 14. ¾

Σανπρώτοπαράδειγμαχρήσ εωςτης ÉÈ ÒØ Öπαρουσ ιάζεταιέναπαράδειγμασ χεδιασ μούκύκλωνμέσ ασ εένακεντρικόπαράθυροº

S i L L I OUT. i IN =i S. i C. i D + V V OUT

P Ò±,. Ï ± ˆ ˆŒˆ Š ƒ ˆŸ. Œ ƒ Œ ˆˆ γ-š Œˆ ƒ ƒˆ 23 ŒÔ. ² μ Ê ². Í μ ²Ó Ò Í É Ö ÒÌ ² μ, É μí±, μ²óï

M 2. T = 1 + κ 1. p = 1 + κ 1 ] κ. ρ = 1 + κ 1 ] 1. 2 κ + 1

v w = v = pr w v = v cos(v,w) = v w

P ˆ.. Œμ ±μ ±μ,. ˆ. ˆ Ó±μ,.. Š ²μ

ÈÖÓ Ö ÑÑ Ò ÑÓÖ Û ÈÖÓÔØÙÕ ÛÒ ËÔÓÙ ÛÒ ÌÑ Ñ ØÓ Å Ñ Ø ÛÒ È Ò Ô Ø Ñ Ó È ØÖÛÒ Å Ñ Û Ø Ò Ô Ø Ñ ØÛÒ ÍÔÓÐÓ ØôÒ

Morganναδώσειμίαεναλλακτικήμέθοδο,αποδεικνύονταςπρώταότιηευθείαπουδιχοτομεί κάθεταμίαχορδήπεριέχειτοκέντροτουκύκλου. Παρ όλααυτά,καιαυτήημέθοδοςέχει

P ƒ. μ μ², Œ.. ˆ μ,.. μ ± Î Š Ÿ ˆ Œ ˆŸ ˆ Ÿ Š ˆ. ² μ Ê ² μ Ò É Ì ± Ô± ³ É.

ΕΙΣΑΓΩΓΗ ΣΤΑ ΟΠΤΙΚΑ ΣΥΣΤΑΤΙΚΑ

ΑΡΧΕΙΑ ΚΑΙ ΒΑΣΕΙΣ ΔΕΔΟΜΕΝΩΝ

Š Šˆ ATLAS: ˆ ˆŸ ˆ Šˆ, Œ ˆ Œ ˆ.. ƒê ±μ,. ƒ ² Ï ², ƒ.. Š ± ²,. Œ. Ò,.. ŒÖ²±μ ±,.. Ï Ìμ μ,.. Ê ±μ Î,.. ±μ,. Œ. μ

ƒê,.. ± É,.. Ëμ μ. ˆŸ Œ ƒ ˆ ƒ Ÿ ˆ ˆˆ ˆ ˆ ˆ Šˆ- ˆŒŒ ˆ ƒ Œ ƒ ˆ. ² μ Ê ² ² ±É Î É μ

Μαθηματικά ΙΙΙ. Ανοικτά Ακαδημαϊκά Μαθήματα. Ενότητα 10: Μέθοδος Ελάχιστων Τετραγώνων. Αθανάσιος Μπράτσος. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας ΤΕ

High order interpolation function for surface contact problem

Θεωρία Συνόλων. Ενότητα: Τα πάντα σύνολα; Γιάννης Μοσχοβάκης. Τμήμα Μαθηματικών

Ó³ Ÿ , º 1(130).. 7Ä ±μ. Ñ Ò É ÉÊÉ Ö ÒÌ ² μ, Ê

Εφαρμοσμένα Μαθηματικά

P Œ ²μ, Œ.. ƒê Éμ,. ƒ. ²μ,.. μ. ˆ ˆŸ Œˆ ˆŸ ˆ Š Œ ˆŸ Ÿ - ˆ ˆ ŠˆŒˆ Œ Œˆ ˆ œ ˆ Œ ˆ ŒˆŠ Œ -25

ˆ ˆŠ Œ ˆ ˆ Œ ƒ Ÿ Ä ƒ ² ± Ñ Ò É ÉÊÉ Ô É Î ± Ì Ö ÒÌ ² μ Å μ Ò Í μ ²Ó μ ± ³ ʱ ²μ Ê, Œ ±

þÿ ɺÁ Ä ÅÂ, ±»Î¼ Neapolis University þÿ Á̳Á±¼¼± ¼Ìù±Â ¹ º à Â, Ç» Ÿ¹º ½ ¼¹ºÎ½ À¹ÃÄ ¼Î½ º±¹ ¹ º à  þÿ ±½µÀ¹ÃÄ ¼¹ µ À»¹Â Æ Å

ˆ ˆŠ Œ ˆ ˆ Œ ƒ Ÿ Ä616 Š ˆŒ CMS LHC

Θεωρία Συνόλων. Ενότητα: Επιλογής επόμενα. Γιάννης Μοσχοβάκης. Τμήμα Μαθηματικών

ƒ Š ˆ ˆ ˆˆ. ƒ. Ê ÖÏμ a,.. Š Ê,.. Šμ²μ ÊÉμ a, ƒ..œ ÍÒ a,. ƒ. Œμ²μ± μ a,.. ± a a Ñ Ò É ÉÊÉ Ö ÒÌ ² μ, Ê

ˆ ˆŠ Œ ˆ ˆ Œ ƒ Ÿ Ä Œμ Ìμ. ±É- É Ê ± μ Ê É Ò Ê É É, ±É- É Ê, μ Ö

AN RFID INDOOR LOCATION ALGORITHM BASED ON FUZZY NEURAL NETWORK MODEL. J. Sys. Sci. & Math. Scis. 34(12) (2014, 12),

P ²ÒÏ,.. μ μ Š ˆ ˆ Ÿ ˆ

GPU. CUDA GPU GeForce GTX 580 GPU 2.67GHz Intel Core 2 Duo CPU E7300 CUDA. Parallelizing the Number Partitioning Problem for GPUs

tan(2α) = 2tanα 1 tan 2 α

P ˆŸ ˆ Œ Œ ˆ Šˆ. Š ˆ œ ˆ -2Œ

ˆ œ ˆ ˆ ˆ Šˆ Œ ˆ ˆ Š ˆ Ÿ Œˆ ˆ Œˆ ŒŠ Œ ˆ Ÿ

ˆ Œ ˆ Ÿ ˆ ˆŸ Ÿ - ˆ ˆ Šˆ Š ˆŸˆ

Blowup of regular solutions for radial relativistic Euler equations with damping

Ó³ Ÿ º 3[120] Particles and Nuclei, Letters No. 3[120]

P Î,.. Š ²³Ò±μ, Œ.. Œ ϱ,.. ʳ ˆ ˆ ˆ ˆŸ ˆŠ Š Š ˆ Ÿ -200

P É Ô Ô² 1,2,.. Ò± 1,.. ±μ 1,. ƒ. ±μ μ 1,.Š. ±μ μ 1, ˆ.. Ê Ò 1,.. Ê Ò 1 Œˆ ˆŸ. ² μ Ê ² μ Ì μ ÉÓ. É μ ±, Ì μé μ Ò É μ Ò ² μ Ö

Ó³ Ÿ , º 7(163).. 798Ä802 ˆ ˆŠ ˆ ˆŠ Š ˆ. .. Ëμ μ. Î ± É ÉÊÉ ³..., Œμ ±

STUDY ON CYCLIC OXIDATION RESISTANCE OF HIGH NIOBIUM CONTAINING TiAl BASE ALLOY WITH ERBIUM

Τεχνικές βασισμένες στα Δίκτυα Αναμονής Εισαγωγικά Επιχειρησιακοί νόμοι

P ² ± μ. œ Š ƒ Š Ÿƒ ˆŸ Œ œ Œ ƒˆ. μ²μ μ Œ Ê μ μ ±μ Ë Í μ É Í ±μ ³μ²μ (RUSGRAV-13), Œμ ±, Õ Ó 2008.

Ανώτερα Μαθηματικά ΙI

1-6 Ð Ï Te (mass%) 0% 0.3% 0.5% 0.8% 1.0% 2.0% 2 Î 1 6

Ó³ Ÿ , º 4Ä5(174Ä175).. 682Ä688 ˆ ˆŠ ˆ ˆŠ Š ˆ

Ó³ Ÿ , º 7(156).. 62Ä69. Š Œ œ ƒˆˆ ˆ ˆŠ. .. ŠÊ²Ö μ 1,. ƒ. ²ÓÖ μ 2. μ ± Ê É É Ê Ò μ μ, Œμ ±

Reverse Ball-Barthe inequality

Ó³ Ÿ , º 3(194).. 673Ä677. Š Œ œ ƒˆˆ ˆ ˆŠ. ˆ.. ³ Ì μ, ƒ.. Š ³ÒÏ,ˆ..Š Ö, Ÿ. ʲ ±μ ±

Š ˆ œ Ÿ ˆ œ Œ Œ ƒ ˆ Œ Œ LEPTA

ˆŒ œ ƒ ƒ ˆ ˆŸ ˆ Š ˆ 137 Cs Š ˆ Œ.

ƒ Š ˆ Šˆ Š Œˆ Šˆ Š ˆŒ PAMELA ˆ AMS-02

.. ƒ²μ É, Œ. Œ Ï,. Š. μé ±μ,..,.. ³ μ μ, ƒ.. ÒÌ

Ó³ Ÿ , º 7(205) Ä1540 ˆ ˆŠ ˆ ˆŠ Š ˆ. .. ŠÊ Íμ,.. Ê ±μ,.. ² μ 1. Ñ Ò É ÉÊÉ Ö ÒÌ ² μ, Ê

ÅÊ NEAR (Near-Earth Asteroid Rendezvous) Hayabusa

Õâñéäéóìüò. Ðïéá åßíáé ç áíüãêç åéóáãùãþò ôçò Ýííïéáò ôïõ õâñéäéóìïý. Ðïéá åßíáé ôá âáóéêüôåñá åßäç õâñéäéóìïý

Προσομοίωση Δημιουργία τυχαίων αριθμών

Ó³ Ÿ , º 4Ä5(174Ä175).. 629Ä634 ˆ ˆŠ ˆ ˆŠ Š ˆ. .. Ëμ μ,.. μ, Š.. ±μ. Î ± É ÉÊÉ ³..., Œμ ±

ΟπτικόςΠρογραμματισ μός. ΙωάννηςΓºΤσ ούλος

RELATIONSHIP BETWEEN MECHANICAL PROPERTIES AND LAMELLAR ORIENTATION OF PST CRYSTALS IN Ti 45Al 8Nb ALLOY

Ανώτερα Μαθηματικά ΙI

ˆ ˆŠ Œ ˆ ˆ Œ ƒ Ÿ Ä664

Ó³ Ÿ , º 7(163).. 793Ä797 ˆ ˆŠ ˆ ˆŠ Š ˆ. .. Ëμ μ. Î ± É ÉÊÉ ³..., Œμ ±

2011 Đ 3 Ñ ACTA METALLURGICA SINICA Mar pp

Ó³ Ÿ , º 4(181).. 501Ä510

þÿ ÀÌ Ä º± µä À ¹ ¼ ½

ˆ ˆ ˆ ˆˆ γ-ˆ ˆŸ ˆ Š Œ ˆ Œ œ Š ˆˆ

Μαθηματικά ΙΙΙ. Ανοικτά Ακαδημαϊκά Μαθήματα. Ενότητα 4: Διανυσματικές Συναρτήσεις μιας Μεταβλητής. Αθανάσιος Μπράτσος

Μαθηματικά ΙΙΙ. Ανοικτά Ακαδημαϊκά Μαθήματα. Ενότητα 11: SPLINES. Αθανάσιος Μπράτσος. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας ΤΕ

Œ.. ÉÊ Í± 1,.. Ö Õ²Ö 1,.. Šμ Î ±μ,.. Š Îʱ,.. ŠÊÎ ±,..Œμ Î,.. ³ μ,.. μ³êéμ,. A. Ìμ ± 1

N i. D i (x) = 1 N i. D(x, x ik ). (3, 1), (3, 0.9), (3, 0.8), (3, 0.8) (4, 0), (4, 0.1), (4, 0.2). k=1. j=1

arxiv: v1 [math.dg] 3 Sep 2007

Š Ÿ Š Ÿ Ÿ ˆ Œ ˆŠ -280

Ó³ Ÿ , º 6(155).. 805Ä813 ˆ ˆŠ ˆ ˆŠ Š ˆ. ˆ.. ³ Ì μ, ƒ.. Š ³ÒÏ, ˆ.. Š Ö. Ñ Ò É ÉÊÉ Ö ÒÌ ² μ, Ê. Ÿ. ʲ ±μ ±

Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών Εθνικό Μετσόβιο Πολυτεχνείο. Επίδοση Υπολογιστικών Συστημάτων. Α.-Γ. Σταφυλοπάτης.

ZZ (*) 4l. H γ γ. Covered by LEP GeV

CORROSION BEHAVIOR OF X70 PIPELINE STEEL IN SIMULATED KU ERLE SOIL SOLUTION WITH CO 2

Ανώτερα Μαθηματικά ΙI

Δυναμική διαχείριση μνήμης

plants d perennials_flowers

Pilloras, Panagiotis. Neapolis University. þÿ À¹ÃÄ ¼Î½, ±½µÀ¹ÃÄ ¼¹ µ À»¹Â Æ Å

Š Œ -Ÿ Š ˆŸ Ÿ Œˆ ˆ Œˆ.ˆ. Ê ÉÒ²Ó ±

Εισαγωγή στις Φυσικές Επιστήμες (ΦΥΕ14) Περίοδος ΕΡΓΑΣΙΑ 1 η. Τότε r r b c. και ( )

Transcript:

2017 12 31 4 Dec. 2017 Communication on Applied Mathematics and Computation Vol.31 No.4 DOI 10.3969/j.issn.1006-6330.2017.04.005 Æ ÛÝ ¹ÐĐÝÆßÌ ĐĐ 1, 1, 2 (1. ÆÆ ÆÆ 200444; 2. ¾Æ Æ ¾ 330031) ѺÛÏÖ Æº m Ó ÐÆÑ Ø Ù 1 Æ ³ÌÙÓ ÂÓ Ò Ø ÅÓ± Æ Öº±¾Ç±Ó Ð ¾ÛÏÖ Ó 2010 ű 90C27 Çű O223 ÈÏÚÕ» A ÈÒÙ 1006-6330(2017)04-0471-16 Minimizing makespan in a three-stage flexible flowshop with identical machines and two batch processors HUANG Huanhuan 1, HE Longmin 1, LUO Runzi 2 (1. College of Sciences, Shanghai University, Shanghai 200444, China; 2. School of Sciences, Nanchang University, Nanchang 330031, China) Abstract A three-stage flexible flowshop scheduling problem that consists of m identical machines at stage 1, one batch processor at stage 2 and the other batch processor at stage 3 is considered. The objective is to minimize the makespan. When all jobs have the same processing time at all the three stages, an algorithm for general situation and some algorithms for some special situations are provided. Key words scheduling; flexible flowshop; identical machines; batch processor 2010 Mathematics Subject Classification 90C27 Chinese Library Classification O223 «2015-03-20; 2015-08-08 ¾ «¹± Æ Æ (11632008, 11372170, 11571221, 11361043) ÅÓ Þ ³ÌÖ ÚÑ E-mail: hlm@shu.edu.cn

472 31 1 1.1 ØÙ ÞßÕÒ Ñ Ï½ÚÎÕ ÏÆß ½ÚÎÕ (flexible flow shop, FFS) (burn in, BI) ÂÊÏÑ ß FFS ÊÀ n Ð ¼Ê k Ð i m i ( 1) (i = 1, 2,, k), ¹ Á ÐÁ¹ ²ÂÒÒÅ Éߨ Fk(m 1, m 2,, m k ) C max. FFS ß µ²ëõ F2(m 1, m 2 ) C max ßÌ k = 1 ߨ Pm C max, Garey Johnson [1] ÌÎß Pm C max Ø NP- Ì m 1 = 1 m 2 = 1 ߨ F2(1, 1) C max, ¼ß F2 C max, Johnson [2] ĐÕß F2 C max Ï ¹Ð²ÃÕÒ ÌÎ SPT-LPT ÒØÒÒß F2(m 1, m 2 ) C max ϲËÕÒÅ Âß Pm C max ß F2 C max Ï ÂÐ BI ÊÀ n Ð µá¹³ m (> 1) ¹Ò B ( 1) Ð Ï ØÛ ÒÏ Ï Å³ÛÒÍà J j ÏÍà r j (j = 1, 2,, n), Á Ä ½³Ï ²ÂÒÒŠߨ 1 B C max ³ m B C max. Ï 2 Ð ÒÍÃÏß 1 r j, B C max, Lee Uzsoy [3] ĐÕÛ«Ò¹Ïß 1 r j, B C max, Lee Uzsoy [3] ĐÕÐ O(n log n) ÉÒ¹ ÐÅ O(n log n) ÉÒ ÍÃÏß 1 B C max, Sung Choung [4] ÃÕ SPT-FOE ³ LPT-LOE Ï«Ò ÍÃĐÏß 1 r j, B C max ÃÕ Ï O(nc) Ò (c Ø Ï¼Ó Ò); ¹Ïß 1 r j, B C max ÃÕ O(n log n) O(n log n) max{o(n log n), O(nc)} ÏÐÉÒ 2. Ahmadi [5] F2(B 1, 1) b 1j b 1 C max F2(1, B 2 ) b 2j b 2 C max F2(B 1, B 2 ) b 1j b 1, b 2j b 2 C max ÏÐß µđõ O(n log n) O(n log n) O(n 3 ) ÏÒ Sung Yoon [6] ĐÕß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max Ï O(nB 1 ) Ò Á µ ÒÏ ß Sung [7] ĐÕ O (n m 1 ) c k ÉÒ (m Ø Ë c k Ø k ϼÓ). k=2 Ò Ï½ÚÎÕ ß F2(m, B) C max : Ø Á Ï Ò Ò He [8] µđõ max{o(n log n), O(nB)}(B Ø Ï¼Ó) ÒØ 2 Ï O(n log n) ÉÒ Ø¹ Ü [9] Ì Õ¹ O(n log n) ÃÖ Ø ()NP- NP- Ï ĐÕ O(n log n) O(n) ÏÉÒ

4 ÆÓ Ò ÐÛÏÖ Ó 473 1.2 Îļ ½ FFS BI ÑÅ Úν ÅÉ Õ Î ( À ), Õ ( ÒÏÅÉ Á±À ßÝ ±) ß ÆØÞÏ FFS-BI-BI ß Đ FFS-BI-BI ÊÀ Ϲ½ÚÎÕ n Ð ¼Ñ 1 2 3 Ï 1 m ( 1) Ò {1, 2,, m} J j Á»¹Ò Ï Ø a j (j = 1, 2,, n). 2 3 µø ÒÏÑ M 1 M 2, M 1 M 2 Ê J j Ï µø b 1j b 2j (j = 1, 2,, n), M 1 M 2 Ò ÏØÊ ÏÒÈ M 1 M 2 ¹ µò B 1 ( 1) B 2 ( 1) Ð Đ Ï Ë B 1 B 2 µø M 1 M 2 ϼӵ²¹ÒÏ Õ Ï µ C max ÃÒߨ F3(m, B 1, B 2 ) C max, F3 ÏÚÎÕ m Ï 1 m Ò {1, 2,, m} B 1 B 2 µï 1 2 M 1 M 2 Ï¼Ó ÞÐß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max, 1 2 3 Ï µ Ò 1.3 m: 1 Ï m Ò M 1 : 2 Ï M 2 : 3 Ï n: Ë N: ²Ïº N = {1, 2,, n}. J j : ²Ø j Ï (j N), j (j N), J = {J 1, J 2,, J n }. r j : J j ÏÍà (j N), r = {r 1, r 2,, r n }. a j : J j ÁÒ Ï (j N). b lj : J j Á M l Ï (j N; l = 1, 2). B l : M l Ï¼Ó (l = 1, 2). C j : J j Á M 2 Ï (j N). C max : {J j }(j N) Á M 2 Ï µ C max: {J j }(j N) Á M 2 ÏÒ µ x : x ÏÒÅÊË x : Å x ÏÒÊË arg: ±Óϲ ( argmax{r j j N} {r j }(j N) ÒÅÎϲ j). ERT (earliest release time) Ä ÍÃÍÅ

474 31 SPT (shortest processing time first) Ä ÍÅ LPT (longest processing time first) Ä ÅÍ FOE(first only empty)(n, B) Ä n Ð Ä (n ( n/b 1)B) Ð ( n/b 1) Ø LOE(last only empty)(n, B) Ä n Ð ( n/b 1) Ø «(n ( n/b 1)B) Ð Áº¹ÐßÏ ²Ë Z = f(c 1, C 2,, C n ), D º S ϹРº (D S), ¹Ã s (s S) Ϲ (C 1, C 2,, C n ). S»¹Ðà s, Á D Ç͹Ðà s, ²Î Z = f(c 1, C 2,, C n ) Z Z, Z Âà s Ï ²ËÎ D  S ϹÐÁº [10-11]. 2 Íà F3(m,B 1, B 2 ) a j a, b 1j b 1,b 2j b 2 C max Þ 2.1 ÎÄ F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max µ Ï Ò (a j a, b 1j b 1, b 2j b 2 ), ¼ 1 ¼³ J 1, J 2,, J n ÁÌÒ ÏÒ Ï 1. ³ 1 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ÂÁÉ ÏÁ Ò ÐÏ Î 1, ß Á 1 Ò ÐÏ J j Á 1 Ï j/m a ÃØ J j Íà M 1 Ï r j = j/m a(j N). É ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max. ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max, Sung Yoon [6] ¼ Ï ERT Ä ( 2) ĐÕÒà M 1 M 2 Ï M 2 ¼ FOE(n, B 2 ) Ä²Ò ( 3). M 1 Á M 2 ¼ FOE(n, B 2 ) ÄÏÒµ B 1 B 2, b 1 b 2 B 1 B 2, b 1 b 2 ÏÑÆ µ¼ FOE(n, B 1 ) Ä FOE(n, B 2 ) IJҹ ¼ FOE(n, B 2 ) Ä Ã DP(dynamic program) Ò²Ò Þ [6] ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max ÏÑ Ä ³ 2 [6] ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max, ÂÁ¹ÐÒà ERT Ä ERT ÄÏ ÓØ O(n log n). ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max, Î 1, Þ Ñ 1 Ò ÐÏ Íà 2 Ï M 1 Ï r j = j/m a (j N) 2 Ï ERT Ä

4 ÆÓ Ò ÐÛÏÖ Ó 475 2 Đ 3. ³ 3 [6] ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max, FOE(n, B 2 ) IJ M 2 Ï Ò FOE(n, B 2 ) ÄÏ ÓØ O(n). ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max, ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max, FOE(n, B 2 ) ÄØ M 2 ÏÒ (i) (v), Đß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max Á M 1 Ï DP Ò 4. (i) batching-instant-job Ï Ω Đ n Ð ºÏ¹Ðº j Á M 2 Ï»ÒÒ ¼ j Ω = {z, z + B 2,, n}, z = n ( n/b 2 1)B 2, j ¹Рbatchinginstant-job. (ii) ߹ͺ (a) j Ð {1, 2,, j} Á M 1 j ÂÁͺÏÒ ¹Ð (b) m (m=arg max{r i i Ω, i j},» 1.3 À) Ð Á M 2 FOE(n, B 2 ) Ä m ÂÁ j ÍÒÏ batching-instant-job. (iii) ÐËÏ ĐÊ (ii) Ï¹ÍºØ M 1 ÏÒ«Ø (a) φ 1 (j, b) Á M 1 «ËØ b (b min{b 1, j}) ÏÐ M 1 j Ð ÏÒ «ºØ {j b + 1, j b + 2,, j}; (b) ϕ 1 (j) M 1 j Ð ÏÒ (c) φ 2 (j, b) Á M 1 j Ð («ËØ b) ÏÐ M 2 m Ð ÏÒ (d) ϕ 2 (j) Á M 1 j Ð ÏÐ M 2 m Ð ÏÒ (iv) Õ Ê (iii) Ï (a) (b), φ 1 (j, b) = max{ϕ 1 (j b), r j } + b 1. (1) M 1 «b Ð {j b + 1, j b + 2,, j} Ï» É Ω (M 2 É ÏÍà ), Ê (iii) Ï (c) (d), φ 2 (j, b) = ϕ 2 (j b). (2) M 1 «b Ð {j b + 1, j b + 2,, j} Ϲ É Ω (M 2 ¾ Ï ÍÃÕ), Ê (iii) Ï (a) (c) (d), φ 2 (j, b) = max{φ 1 (j, b), ϕ 2 (j b)} + v(j, b)b 2, (3)

476 31 v(j, b) Á {j b + 1, j b + 2,, j} batching-instant-job ÏË j Ð ÏҹР¼ j (Û Ø jth) j / Ω j Ω ÏÑ Ð (a) j / Ω j Ð Á M 1 Þ Ë M 2 µº Ð M 2 ËÁÉ ¹Ð j Ð Ï Ò M 1 Ï M 2 Ï ¹Ð batching-instant-job Ï (»Ê (iii) Ï (a) (b)) (»Ê (iii) Ï (c) (d)) min ϕ 1 1bj φ1 (j, b), j < B 1, (j) = min φ 1 (j, b), B 1 j n, 1bB 1 ϕ 2 (j) = min{φ 2 (j, b) φ 1 (j, b) = ϕ 1 (j)}. (5) (b) j Ω M 1 M 2 j Ð Ï Ò Ã¹Ò M 2 Ï ϕ 1 (j) ϕ 2 (j) ³ φ 2 (j, b)), (»Ê (iii) Ï (c) (d)) (»Ê (iii) Ï (c) (b)) (v) Ð min ϕ 2 1bj φ2 (j, b), j < B 1, (j) = min φ 2 (j, b), B 1 j n, 1bB 1 ϕ 1 (j) = min{φ 1 (j, b) φ 2 (j, b) = ϕ 2 (j)}. (7) ϕ 1 (0) = ϕ 2 (0) = 0, (8) φ 1 (j, b) = φ 2 (j, b) = ( j > n, ³b > B 1 ). (9) Î (iii) (a) (d) ÏÐ (iv) (v) (1) (9), Đ DP Ò 4. DP Ð ϕ 1 (0) = ϕ 2 (0) = 0. φ 1 (j, b) = φ 2 (j, b) = ( j > n, ³ b > B 1 ). Õ Đ (i) φ 1 (j, b) = max{ϕ 1 (j b), r j } + b 1. (ii) M 1 «b Ð (a)» É Ω, φ 2 (j, b) = ϕ 2 (j b); (b) ÂÁ É Ω, φ 2 (j, b) = max{φ 1 (j, b), ϕ 2 (j b)} + v(j, b)b 2. (iii) jth (j Ð Ï j Ð ): (4) (6)

4 ÆÓ Ò ÐÛÏÖ Ó 477 (a) j / Ω, min φ 1 1bj φ1 (j, b), j < B 1, (j) = min φ 1 (j, b), B 1 j n, 1bB 1 ϕ 2 (j) = min{φ 2 (j, b) φ 1 (j, b) = ϕ 1 (j)}; (b) j Ω, min ϕ 2 1bj φ2 (j, b), j < B 1, (j) = min φ 2 (j, b), B 1 j n, 1bB 1 ϕ 1 (j) = min{φ 1 (j, b) φ 2 (j, b) = ϕ 2 (j)}. DP ÒÏ ÓØ O(nB 1 ). ³ 4 [6] ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max, ¼ M 2 FOE(n, B 2 ) Ä ÏÒ Ã M 1 Ï DP Ò²Ò ÊÐ Đß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ÏÒ Ò m Ò Ð M 1 M 2 Ï µ DP Ò FOE(n, B 2 ) Ä 2.2 ÎÄ F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max H1 2.1 ÀÏÐ ÃÒ H1 1. H1 ¹ 1 Á 1 ¼ÁÌÒ ÏÒ ¹ 2 Á 1 Ï ÃØ Á 2 Ï M 1 ÏÍà M 1 M 2 µ¼ DP Ò FOE(n, B 2 ) Ä Ï DP Ò FOE(n, B 2 ) ÄÏ Ó µø O(nB 1 ) O(n), Ò H1 Ï ÓØ O(nB 1 ). ³ 1 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max Ø O(nB 1 ) à 1 n = 11, m = 5, a = 7, {B 1, B 2 } = {4, 3}, {b 1, b 2 } = {5, 3}. ÎÒ H1 DP Ò FOE(n, B 2 )) Ä µî M 1 M 2 ÏÒ ¼ DP Ò² M 1 ÏÒ 11 Ð Íà M 1 ÏØ r = { 1/5 7, 2/5 7,, 11/5 7} = {7, 7, 7, 7, 7, 14, 14, 14, 14, 14, 21}(r j = j/m a (j N)), batching-instant-jobs Ϻ Ω = {2, 5, 8, 11}, φ 1 (j, b) = φ 1 (1, 1) j = 1, (1) (2) (4) (5) (8)( 1 Ï j = 1), φ 1 (1, 1) = max{ϕ 1 (0), r 1 } + b 1 = max{0, 7} + 5 = 12, φ 2 (1, 1) = φ 2 (0) = 0, ϕ 1 (1) = φ 1 (1, 1) = 12, ϕ 2 (1) = 0.

4 ÆÓ Ò ÐÛÏÖ Ó 479 1 Ï j = 11 M 1 M 2 ÏÒ µ µø ϕ 1 (11) = 26 ϕ 2 (11) = 29. (Ô 1). Ç 1 H1 1 ÜÇ 2 j = 11: ³ (j, b) = (11, 3), 11 Ð «ËØ 3, Î M 1 ÏÒ«{J 9, J 10, J 11 }, Ò µ ϕ 1 (11) = 26. j = 8: 11 Ð «3 Ð Ï 8 Ð ³ (j, b) = (8, 4), Î 8 Ð ÏÒ«{J 5, J 6, J 7, J 8 }, Ò µ ϕ 1 (8) = 19. j = 4: Òß Ï 4 Ð ³ (j, b) = (4, 4), ÎÒÄ {J 1, J 2, J 3, J 4 }, Ò µ ϕ 1 (4) = 12. 26. M 1 ÏÒ Ø {{1, 2, 3, 4)}, {5, 6, 7, 8}, {9, 10, 11}}, Ò µ ϕ 1 (11) = 3 M 2 ¼ 3 Ï FOE(n, B 2 ) =FOE(11, 3) = {{1, 2}, {3, 4, 5}, {6, 7, 8}, {9,10, 11}} Ä C max = φ 2 (11) = 29. 2.3 ÎÄ F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ز ß H2 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max, Ø (i)a m/b 1 b 1 ; (ii) a m/b 1 b 1 ; (iii) m/b 1 b 1 < a < m/b 1 b 1 Æ Ð ( 2 Ï 2),

480 31 Æ µ ÏÒ H2(1) 2 Ò H2(2) 3 Ò H2(3) 4. Á H2(1) (3) ÏÆÒ Ò H2(3)( 2 Ï 2 Ï (3)) B 1 > B 2, b 1 > b 2 (³ B 1 < B 2, b 1 < b 2 ) Ï ÓØ O(nB 1 ), ÏÒ Ó Ø O(n). Î 1, 1 Ï m Ò ÕÐ Ê ( ) (Å) ÏÆ µá M 1 M 2 Ï ÅÐ ( ) a m/b 1 b 1 H2(1) ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max a m/b 1 b 1, 1 ÏÒ m Ð («Ë m) ¼ 2 ¼ LOE(m, B 1 ) Ä À 3. b 1 B 1 /B 2 b 2, n Ð Á M 2 ¼ LOE(B 1, B 2 ) Ä b 1 B 1 /B 2 b 2, Û m Ð Á M 2 Ð (i) a m/b 2 b 2, n Ð Á M 2 ¼ LOE(m, B 2 ) Ä (ii) a m/b 2 b 2, n Ð Á M 2 ¼ LOE(n, B 2 ) ÄÐ (iii) m/b 2 b 2 < a < m/b 2 b 2, n Ð Á M 2 ¼ FOE(n, B 2 ) Ä B 1 /B 2 b 2 < b 1 < B 1 /B 2 b 2, n Ð Á M 2 ¼ FOE(n, B 2 ) Ä Ò H2(1)- Ò H2(1)- Ò H2(1)- (Ò H2(1)- Ò) Ï ÓØ O(n), Ò H2(1) Ï ÓØ O(n). ³ 2 a m/b 1 b 1 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max Á O(n) à Խ Ø a m/b 1 b 1, Ò m Ð M 1 Á a ( M 1 ÄÐ a [a, 2a] LOE(m, B 1 )) Ä Û m Ð Ð ÁÒÈ 3 Ï M 2 b 1 B 1 /B 2 b 2 M 2 ¼ LOE(B 1, B 2 ) Ä Á b 1 ( M 2 ÄÐ b 1 [a+b 1, a+2b 1 ]) B 1 Ð Ð M 2 Á ¹Ï B 1 Ð ÍÃÍ Ò ¹Ï B 1 Ð (Ô 2) n n/m m Cmax = n/m a + b 1 + B 1 n n/m m (n n/m m)/b1 B 1 Ì B 1 B 2, b 1 b 2 (n n/m m (n n/m m)/b 1 B 1 ) < B 1 Ø B 2 C max = n/m a + (n n/m m)/b 1 b 1 + b 2. b 2. b 1 B 1 /B 2 b 2 2 ½ Ï m Ð Á M 2 Ð Ëß n Ð Á M 2 ÂÚ Ð (i) a m/b 2 b 2

4 ÆÓ Ò ÐÛÏÖ Ó 481 a [ ] 2a [ ] [/ ] [ / ] Ç 2 H2(1)- M 2 ¼ LOE(m, B 2 ) Ä Á a ( M 2 ÄÐ a [a + b 1, 2a + b 1 ]) Û m Ð Ð M 2 Á ¹Ï m Ð ÍÃÍ Ò Û m Ð C max = n/m a + b 1 + n n/m m)/b 2 b 2 (Ô 3). [] [ ] [ ] [/ ] [ / ] [/ ] Ç 3 H2(1)- (i) (ii) a m/b 2 b 2 M 2 ¼ LOE(m, B 2 ) Ä Á a ( M 2 ÄÐ a [a + b 1, 2a + b 1 ]) m/b 2 B 2 Ð ¾ n Ð Á M 2 Ð ¼ LOE(n, B 2 ) Ä µ C max Ò C max = a + b 1 + n/b 2 b 2 (Ô 4). (iii) m/b 2 b 2 < a < m/b 2 b 2

482 31 Î 3, ¼ FOE(n, B 2 ) ÄÎ M 2 ÏÒ B 1 /B 2 b 2 < b 1 < B 1 /B 2 b 2 [ ] [ ] [] [] [/ ] [ / ] [/ ] Ç 4 H2(1)- (ii) Î 3, M 2 ¼ FOE(n, B 2 ) Ä ÎÒ (Å) a m/b 1 b 1 H2(2) ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max a m/b 1 b 1, 2 ¼ LOE(n, B 1 ) Ä Î M 1 ÏÒ 3: b 1 B 1 /B 2 b 2, n Ð Á M 2 ¼ LOE(B 1, B 2 ) Ä b 1 B 1 /B 2 b 2, n Ð Á M 2 ¼ LOE(n, B 2 )) ÄÐ B 1 /B 2 b 2 < b 1 < B 1 /B 2 b 2, n Ð Á M 2 ¼ FOE(n, B 2 ) Ä Ò H2(2)- Ï ÓØ O(n), Ò H2(2) Ï ÓØ O(n). ³ 3 a m/b 1 b 1 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max Á O(n) à Խ a m/b 1 b 1, Ò m Ð Û m Ð Í M 1 M 1 Á a ( M 1 ÄÐ a [a, 2a]) m/b 1 B 1 ( m) Ð LOE(n, B 1 ) ÄÎ M 1 ÏÒ ß 3: b 1 B 1 /B 2 b 2 M 2 ¼ LOE(B 1, B 2 ) Ä Á b 1 ( M 2 ÄÐ b 1 [a+b 1, a+2b 1 ]) B 1 Ð M 2 Á ¹Ï B 1 Ð ÍÃÍ Ò ¹Ï B 1 Ð C max = a + n/b 1 b 1 + (n n/b 1 B 1 )/B 2 b 2 (Ô 5). b 1 B 1 /B 2 b 2

4 ÆÓ Ò ÐÛÏÖ Ó 483 M 2 Á b 1 ( M 2 ÄÐ b 1 [a + b 1, a + 2b 1 ]) B 1 /B 2 B 2 ( B 1 ) Ð 2 ½ Ï n Ð Á M 2 ¼ LOE(n, B 2 ) Ä Ð B 1 /B 2 b 2 < b 1 < B 1 /B 2 b 2 C max = a + b 1 + n/b 2 b 2 (Ô 6). Î 3, M 2 ¼ FOE(n, B 2 ) Ä ÎÒ [] [] [ ] [ ] [/ ] [ / ] Ç 5 H2(2)- [] [] [ ] [ ] [/ ] [ / ] Ç 6 H2(2)- («) m/b 1 b 1 < a < m/b 1 b 1 2.1 ÀÏÐ Á M 2 ¼ FOE(n, B 2 ) ÄÏÒµĐ M 1 ÏÒ ²ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max ÏÒ Đ B 1 B 2, b 1 b 2

484 31 B 1 B 2, b 1 b 2 ÑÆ Á M 1 ÏÒ H2(3)-a b 5 6. H2(3)-a B 1 B 2, b 1 b 2 r j = j/m a (j N) ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max Ï M 1 FOE(n, B 1 ) Ä Ò H2(3)-a Ï ÓØ O(n). ³ 5 B 1 B 2, b 1 b 2 r j = j/m a (j N) Ò H2(3)-a ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max Î M 1 Ï O(n) Ò H2(3)-b B 1 B 2, b 1 b 2 r j = j/m a (j N) ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max Ï M 1 FOE(n, B 2 )) Ä Ò H2(3)-b Ï ÓØ O(n). ³ 6 B 1 B 2, b 1 b 2 r j = j/m a (j N) Ò H2(3)-b ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max Î M 1 Ï O(n) Ò ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ß F2(B 1, B 2 ) r j, b 1j b 1, b 2j b 2 C max. ÎÒ H2(3)-a b 2.1 ÀÏÒ H1( DP Ò FOE(n, B 2 ) Ä), Đß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max m/b 1 b 1 < a < m/b 1 b 1 ÏÒÒ H2(3) 4. H2(3) Ä ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max m/b 1 b 1 < a < m/b 1 b 1 : B 1 B 2, b 1 b 2, M 1 M 2 µ FOE(n, B 1 ) Ä FOE(n, B 2 ) Ä B 1 B 2, b 1 b 2, M 1 M 2 FOE(n, B 2 ) Ä B 1 >B 2, b 1 >b 2 (³ B 1 <B 2, b 1 <b 2 ), M 1 M 2 µ DP Ò FOE(n, B 2 ) Ò H2(3)- Ï ÓØ O(n), Ò H2(3)- Ï ÓØ O(nB 1 ), Ò H2(3) Ï ÓØ O(nB 1 ). ³ 4 m/b 1 b 1 < a < m/b 1 b 1 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max B 1 > B 2, b 1 > b 2 (³ B 1 < B 2, b 1 < b 2 ) Á O(nB 1 ) ÃÖ Ø O(n) Ã Ò H2(1) (3) 2 4, µîò H2 5. H2 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max : (i) a m/b 1 b 1, Ò H2(1); (ii) a m/b 1 b 1, Ò H2(2); (iii) m/b 1 b 1 < a < m/b 1 b 1, Ò H2(3). Ò H2(1) (2) Ï ÓØ O(n), Ò H2(3) Ï ÓØ O(nB 1 ), Ò H2 Ï ÓØ O(nB 1 ). ³ 5 ß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max Á O(nB 1 ) Ã

4 ÆÓ Ò ÐÛÏÖ Ó 485 Ò H2 ÎÍÏ 5 Ò H1 ÎÍÏ 1, ÑÒÏ ÓØ O(nB 1 ), ËÒ H2 Ò H1 µ¹ò H2 Âß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ØÆ ( 2 Ï 2: (I) a m/b 1 b 1, (II) a m/b 1 b 1, (III) m/b 1 b 1 < a < m/b 1 b 1 ) À¹Ð Æ m/b 1 b 1 < a < m/b 1 b 1 ÏÑ Ò H2(3)- Ï ÓØ O(n), Ç Ò H2(3)- Ï ÓØ O(nB 1 ). 3 º Þß F3(m, B 1, B 2 ) a j a, b 1j b 1, b 2j b 2 C max ÈÕÐ ĐÕ ¹ Ï O(nB 1 ) Ò½Æ Ï O(n) Ò ( 2). ß F3(m, B 1, B 2 ) b 1j b 1, b 2j b 2 C max F3(m, B 1, B 2 ) a j a, b 1j b 1 C max ¾ ÞÐ Û 2 ËÁ F3(m, B 1, B 2) a j a, b 1j b 1, b 2j b 2 C max Ü (Ú) ¼Í Ô Ô () 1 (») : m, B 1, B 2 ; a j = a, b 1j = b 1, b 2j = b 2 H1 O(nB 1 ) 1 b 1 B 1 /B 2 b 2 H2(1)- (i) a m/b 2 b 2 H2(1)- (i) (I) a m/b 1 b 1 b 1 B 1 (ii) a m/b 2 b 2 H2(1)- (ii) O(n) 2 (H2(1)) /B 2 b 2 (iii) m/b 2 b 2 <a< m/b 2 b 2 H2(1)- (iii) B 1 /B 2 b 2 < b 1 < B 1 /B 2 b 2 H2(1)- 2 (È) b 1 B 1 /B 2 b 2 H2(2)- (II) a m/b 1 b 1 b 1 B 1 /B 2 b 2 H2(2)- O(n) 3 (H2(2)) B 1 /B 2 b 2 < b 1 < B 1 /B 2 b 2 H2(2)- (III) m/b 1 b 1 < a B 1 B 2, b 1 b 2 H2(3)- O(n) < m/b 1 b 1 B 1 B 2, b 1 b 2 H2(3)- 4 (H2(3)) B 1 > B 2, b 1 > b 2 ( B 1 < B 2, b 1 < b 2 ) H2(3)- O(nB 1 ) (): m, B 1, B 2 ; a j = a, b 1j = b 1, b 2j = b 2 H2 O(nB 1 ) 5 Ö ÑܺÑÜ ¹ĐÕ»ÎÏÜ Á Ý ÜÉ (ÔÄ) [1] Garey M R, Johnson D S. Strong NP-completeness results: motivation, examples and implications [J]. Journal of the Association for Computing Machinery, 1978, 25: 499-508. [2] Johnson S M. Optimal two- and three-stage production schedules with setup times included [J]. Naval Research Logistics, 1954, 1: 61-68. [3] Lee C Y, Uzsoy R. Minimizing makespan on a single batch processing machine with dynamic job arrivals [J]. International Journal of Production Research, 1999, 37: 219-236. [4] Sung C S, Choung Y I. Minimizing makespan on a single burn-in oven in semiconductor manufacturing [J]. European Journal of Operational Research, 2000, 120: 559-574.

486 31 [5] Ahmadi J H, Ahmadi R H, Dasu S, Tang C S. Batching and scheduling jobs on batch and discrete processors [J]. Operations Research, 1992, 39: 750-763. [6] Sung C S, Yoon S H. Minimizing maximum completion time in a two-batch-processing-machine flowshop with dynamic arrivals allowed [J]. Engineering Optimization, 1997, 28: 231-243. [7] Sung C S, Kim Y H, Yoon S H. A problem reduction and decomposition approach for scheduling for a flowshop of batch processing machines [J]. European Journal of Operational Research, 2000, 121: 179-192. [8] He L, Sun S, Luo R. A hybrid two-stage flowshop scheduling problem [J]. Asia-Pacific Journal of Operational Research, 2007, 24(1): 45-56. [9] Ý ÆÀÁÆĐ ÇРѾÛÏÖ [J]. Ì Æ2008, 25(5): 829-842. [10] Conway R H, Maxwell W L, Miller L W. Theory of Scheduling [M]. Massachusetts: Addison- Wesley Publishing Company Reading, 1967. [11] ÅÆÆÙÆĐÅÆÙ È [M]. ÆÆ»Æ 2003.