Optimization Research of Batch Order Processing Queue in Internet Consumption Custom Marketing

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1 Jot Iteratoal Socal Scece, Educato, Laguage, Maagemet ad Busess Coferece (JISEM 205 Optmzato Research of Batch Order Processg Queue Iteret Cosumpto Custom Maretg Ru Wag, a, Jao Tag2,b* ad Ye Yag3,c, 2, 3 School of Busess, Schua Uversty, Chegdu, Cha a thumb920@63.com, b @qq.com, c @qq.com Keywords: Bach orders; processg queue; proect maagemet; exteso AHP; multlevel grey comprehesve evaluato Abstract. I recet years, wth the rapd developmet of e- commerce, teret cosumpto custom s creasgly favored by cosumers. So how to hadle a varety of orders effectvely ad orderly s the ma problem ths artcle. We could covert batch order processg to prorty evaluato of order proect by usg the dea of proect maagemet. Frstly, we establsh order evaluato system to cofrm order processg queue, ad the exteso AHP method s used to fd the weght. The, the modelg of multlevel grey comprehesve evaluato s avalable to determe order proect prorty. Fally, we llustrate the optmzato method s feasble through umercal examples. Itroducto Recetly, e-commerce has made rapd developmet Cha. As a result, a growg umber of cosumers wat to fd the commodty whch would reflect ther uque persoalty. Wth the creasg demad ad the wde applcato of bg data aalyss, small ad medum-szed eterprses bega to mplemet the teret cosumpto custom maretg. Before the 980s, customzed maretg s ust a forward-loog vso. Utl 993, B. Joseph Pe II gave a complete descrpto of mass customzato []. After that, the topc of mass customzato has bee abstracted, related research o the oe had, cludg the cocept, classfcato, the sgfcace ad so o [2-4]. O the other had, there are may lteratures o customzato maretg strategy uder the bacgroud of Iteret [5-7]. We ote that the above artcles drectly determe the order prorty. It s ot oly tme-cosumg but also affects the producto ad dstrbuto. By cotrast, we assume that the commodtes are o-seasoal, ad customer umber s relatvely large, so the mass orders are varetes. We ca tegrate orders the same provce to programs, ad desg order processg queue from a strategc perspectve. Ths wll be coducve to mprove customer satsfacto wth goods. The paper s orgazed as follows. I Secto 2, we propose order processg queue after the tegrato of the order proect. Ad the adopt exteso AHP method to fd weghts. I Secto 3, we develop a multlevel grey comprehesve evaluato model as the optmzato of order processg queue. Specfc umercal examples are descrbed secto 4. I secto 5 we summarze the method ad preset suggestos for future research. 2 Order processg queue 2. Idetfyg evaluato dex Accordg to the features of teret cosumpto custom products, we mpose the followg dcators: the facal dex Y embraces total vestmet Y ad average proft Y2; Strategc dex Y2 comprses cty atteto Y2 ad eterprse reputatoy22; Core compettveess dex Y3 s reflected employee satsfacto Y3, staff study ablty Y32 ad staff ovato Y33; Developmet of busess coordato Y4 ad promoto of techology level Y42 are draw eterprse teral operatg dex Y4; The last oe s customers fluece dex Y Determg the dex weght Uder a certa crtero, experts compare the relatve mportace of each dex the same level, ad establsh exteso terval udgmet matrx A (a,... as postve recprocal matrx, 205. The authors - Publshed by Atlats Press 68

2 where a ( a, a s called exteso terval ad a, a are bpolar edpots of exteso terval elemets. ( a a s -9 scale umber that are used to compare AHP. As show table : 2 Table Importace level scorg Scale Importace level Score ad are the same mportace 2 tha s somewhat mportace 3 3 tha s obvously mportace 5 4 ad are strogly mportace 7 5 ad are extremely mportace 9 6 ad are ot somewhat mportace /3 7 ad are ot obvously mportace /5 8 ad are ot strogly mportace /7 9 ad are ot extremely mportace /9 Next, accordg to scores of experts, the weght vector ca be obtaed. If A ( A, A, where A deote the lower ed the exteso terval matrx, cotrast, A deote the upper ed 2 oe, the the result wll be (... N a a a a the case of N experts. Recevg the weght N vector the cosstet codto ca follow below steps: Computg feature vectors: The largest egevalue of A, A correspodg to the ormalzed feature vectors x, x whch have a postve compoet. 2 Calculatg ad m: m ( ( a ( (2 a 3Judgg the cosstecy of matrx: If 0 m, t shows that the cosstecy of exteso terval udgmet matrx s good. Otherwse, t eeds to rega the udgemet matrx utl t passes the specto. 4Coutg the weght vector: S ( S, S,... S ( x, mx (3 2 5Rag: Frst, sgle order. If there exts,, ad mag V ( S S 0, so p. 2 ( S S p V ( S S (4 ( S S ( S S wth,,2... ad. So p ( p, p2,... p represets a sgle sequece whch each elemet a certa level assocates wth some elemet the upper oe. The, the level of total order, accordg to the herarchcal structure, ca be obtaed from the top T to the bottom. Calculatg matrx whe ph ( p h, p2h,..., p h, h,2..., whch s the layer level ad h s the elemet h. If the weght vector belogg to the whole goal s T w ( w, w2,... w the - layer, w p w s the value layer. Further, the weght of each dex ca be foud. 3 Optmzg order processg queue I the cosumpto custom maretg, eterprses are famlar wth orders, order type ad geographc locato, but they do ot ow or ow lttle whether customers wll mprove eterprse 69

3 reputato ad ehace the eterprse core compettve ablty or ot. The codto suts the grey system [8], so we choose a mult-level grey comprehesve evaluato method [9]. 3. Creatg the scorg matrx We assume that there s related to perso gvg order tems scores accordg to a score level. Fally, we obta scorg matrxes about evaluato dex. d d D = (5 d d 3.2 Determg the evaluato of grey type To determe grey type evaluato, we eed to determe ra umber, grey umber ad defte weghted fucto. I ths paper, we adopt three types: hgh, mddle ad low level. That s: Hgh level: Grey type s [8, ], ad whte fucto f s d 8 d 0,8 f d 8, Mddle level:grey type s 2 [0,5,0], ad whte fucto f 2 s d 5 d 0,5 f2 d 0 (0 d 5 d 5,0 Low level:grey type s 3 [0,,4], ad whte fucto f 3 s d 0, f3 (8 (4 d 3 d,4 3.3 Coutg the grey evaluato coeffcet The evaluato coeffcets of order proect are all revewers gvg about evaluato dex. Total evaluato coeffcet: h h f ( d.. m, h,2,3 (9 3 h h f ( d.. m (0 Therefore, we ca get grey evaluato weght vector of order proect relatg to evaluato dex. r ( r, r 2... rh ( /, 2 / h / ( All grey evaluato weght vectors could form assessmet weght matrxes. r r h R = rmh r mh 3.4 Comprehesve assessmet The maxmum grey evaluato weght ca be obtaed by R. *( (6 (7 (2 r max( r h,2,3 (3 I the same way, All the evaluato weghts for evaluato dex follow t: *( *( *( *( r ( r r2... r m So, evaluato weght matrxes are: h * *( *(2 *( K r ( r r... r 70 T (4 (5

4 * The, we calculate the comprehesve score wr to determe the prorty of the order proect. 4 Numercal examples Assumg that a compay receves fve proect orders durg the plag perod, the compay eeds to optmze the order processg queue to allocate resources reasoably. The above method ad model are appled to sort order proect prorty. 4. Idetfyg the dex weght There are two experts gvg scores for evaluato dex, as show Table 2 ad Table 3. By calculatg the weght of Y, Y2, Y3, Y4, Y5 as example, the solvg steps are followgs. Table 2 Score of the relatve mportace by oe expert core Facal strategc compettveess dex dex dex Iteral operatg dex Customers fluece dex Facal dex (.00,.00 (4.50,5.50 (2.50,3.50 (2.50,3.50 (.00,.00 strategc dex (0.8,0.22 (.00,.00 (.00,.00 (3.50,4.50 (0.29,0.40 core compettveess dex (0.29,0.40 (.00,.00 (.00,.00 (3.50,4.50 (0.37,0.75 Iteral operatg dex (0.29,0.40 (0.8,0.22 (0.8,0.22 (.00,.00 (0.27,0.43 Customers fluece dex (.00,.00 (2.50,3.50 (.33,2.67 (2.33,3.70 (.00,.00 Table 3 Score of the relatve mportace by aother expert core Facal strategc compettveess dex dex dex Iteral operatg dex Customers fluece dex Facal dex (.00,.00 (4.50,5.50 (3.50,4.50 (3.50,4.50 (2.50,3.50 strategc dex (0.8,0.22 (.00,.00 (2.50,3.50 (.50,2.50 (.50,2.50 core compettveess dex (0.22,0.29 (0.29,0.40 (.00,.00 (3.50,4.50 (0.38,0.7 Iteral operatg dex (0.22,0.29 (0.40,0.67 (0.22,0.29 (.00,.00 (0.20,0.33 Customers fluece dex (0.29,0.40 (0.40,0.67 (.40,2.6 (3.00,5.00 (.00,.00 Combed wth evaluato model, we ca have ormalzed feature vectors x ( , x ( The from Eq. ad 2, t follows that =0.9043,m=.000, 0 m, so the cosstecy of udgmet matrx s good. By Eq.3 ad 4, we have P=25.99, P2=7.4, P3=8.28, P4=, P5=9.48. After the ormalzato of vectors, we ca get P=( ,amely, the weghts are Y=0.4983,Y2=0.42,Y3=0.587,Y4=0.092,Y5=0.87. Smlarly to calculate the weght of each evaluato dex, Y=0.4877,Y2=0.523,Y2=0.7068,Y22=0.2932,Y3=0.003, Y32=0.564,Y33=0.4283,Y4=0.834,Y42= Buldg the scorg matrx Now eterprse has vted 4 relatve persos to grade 9 evaluato dexes, the score of whch rages from oe to te pots. We ca acqure specfc matrxes through Eq D D D D D D D D D Calculatg grey evaluato coeffcets We tae evaluato coeffcet of the order proect for a example. The from Eq.6 to 9, t follows that: 7

5 whe h, 3.875,whe h 2, 2.6,whe h 3, 3 0 Therefore, the total evaluato coeffcet of order proect regard to evaluato dex s The grey evaluato weght vector of order proect assocated wth evaluato dex s r ( The grey evaluato weght vectors of else order proects wth respect to evaluato dex ca be computed smlarly. r ( , r ( , r ( , r ( Comprehesve evaluato Because w ( So the comprehesve score s * wr ( That s to say, the prorty of order proect s order proect > order proect 3> order proect2> order proect 4> order proect 5. 5 Coclusos I ths paper, we have coverted bul order processg to prorty evaluato of a varety of order proect through the applcato of proect maagemet. I partcular, we buld the evaluato dex system ad multlevel grey comprehesve evaluato model strategcally. About the solutos, the exteso AHP method s reasoable. Fally, we have tae fve orders as a example detal. Although the evaluato model could sorted order proect prorty successfully, t oly was the bass of reasoable allocato of resources proect maagemet. Future studes also could be avalable from extra cost ad customer satsfacto, mag the ucerta models to solve each order tem delvery tme, the amout of resources ad huma resource allocato problem. Refereces [] B. Joseph Pe II, Mass Customzato: The New Froter Busess Competto, Bosto: Harvard Busess School, 993. [2] D. Berhardt, Q.H. Lu ad K. Serfes, Product customzato, Europea Ecoomc Revew, 5( [3] J. Wd ad A. Ragaswamy, Customzato: The ext revoluto mass customzato, Joural of Iteractve Maretg, 5( [4] C. Hart, Mass customzato: Coceptual uderpgs, opportutes ad lmts, Iteratoal Joural of Servce Idustry Maagemet, 6( [5] A. Kumar, Mass Customzato: Metrcs ad Modularty, Iteratoal Joural of Flexble Maufacturg Systems, 6( [6] R. Duray, P. T. Ward, G. W. Mllga ad W. L. Berry, Approaches to mass customzato: cofguratos ad emprcal valdato, Joural of Operatos Maagemet, 8( [7] B.B. L, L. J. He, C. X. L ad Y. Lu, Study o process capablty dces (PCI for mass customzato, 2005 Iteratoal Coferece o Servces Systems ad Servces Maagemet, Proceedgs of ICSSSM'05, 2005, pp [8] Sfeg Lu ad Y L, Grey Iformato: theory ad practcal applcatos, Sprger Lodo, [9] D. F. L, Mult-attrbute decso mag models ad methods usg tutostc fuzzy sets, Joural of Computer ad System Sceces, 70 (