INTEGRATED OPERATIONAL LOGISTICS NETWORK (IOLN) DESIGN (CASE STUDY: IRAN KHODRO AUTOMOTIVE CO)

Transcription

1 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 INTEGRATED OPERATIONAL LOGISTICS NETWORK (IOLN) DESIGN (CASE STUDY: IRAN KHODRO AUTOMOTIVE CO) Javad khamsabad* Faculty of Management, Islamc Azad Unversty, Tehran, Iran (Honorary Professor at Logstcs Engneerng) (Board Member at Young Scentsts Scentfc Center, Kharkov, Ukrane) (Member of Industral Engneerng and Operaton Management Socety, Washngton, USA) (Board Member at Entrepreneurshp Scentfc Center, Moscow, Russa) (Board Member at Management Scentfc Center, WBS Torun Unversty, Poland) (Board Member at Internatonal Assocaton of Engneers, London, UK) (Board Member at World Academy of Scence and Technology, Las Vegas, USA) (Board Member at Supply chan management Scentfc Center, Sandy Bay, Australa) Mahmood Mad Drector of Logstcs Engneerng, IKCO, Tehran, Iran Javad Shokoufeh Pour Logstcs Technology & Methods Integraton Unt, IKCO, Tehran, Iran Mohammad Reza Motamed IKCO-Peugeot CEO, Tehran, Iran Seyed Ahmad Ha Khall Deputy of Engneerng, IKCO, Tehran, Iran Babak Rahman Deputy of TPS, IKCO, Tehran, Iran Abstract Logstcs Engneerng has transcended over the decades nto an approach for compettve benefts n organsatonal performance and logstcs effcency. Most ndustres are recognsng that sgnfcant savngs are avalable to companes that are able to ordnate and mprove wthn ther logstcs operatons. Companes today face great challenges because the successful supply of many products and servces needs to the effectve ntegraton of logstcs actvtes across a prolongaton supply chan and an ncreasng geographcal separaton. Moreover, logstcs ntegraton approach nvolves both nternal ntegraton for an ordnated, unfed process as well as relatonshps to react flexbly, changeablty, and responsblty to customer's demands. In ths study, by usng of Fuzzy Vkor (F-Vkor), the best combnaton of RIR was selected and RIR Locatng was done. The man am of ths paper s survey and desgn of the ntegrated operatonal logstcs network (IOLN) and also, a proposed IOLN to ntegraton of the Iran Khodro Co Supply Network. Keywords: Logstcs, Supply Chan, Integrated, Fuzzy Vkor, Network, Iran Khodro Co by the author(s). Dstrbuted under a Creatve Commons CC BY lcense.

2 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 1. Introducton Logstcs ntegraton s known to mprove effcences, performance and compettveness. The ntegraton approach nvolves both nternal and external ntegraton. Internal ntegraton nvolves the ordnated, coacton and ntegraton of logstcs operatons wth other operatonal areas n a company; whereas external ntegraton needs to the ntegraton of logstcs operatons wth those of ther Demanders and supplers n the supply chan (Boon & Wang., 2011). Most defntons of supply chan management explctly recognze the exstence of two flows through the chan; there s a flow of goods and an equally mportant flow of nformaton (Ca et al., 2010). Supply chan ntegraton must thus comprse both nformaton and materal, and cannot restrct tself to only one. Hgher levels of ntegraton are characterzed by ncreased logstcs-related communcaton, greater coordnaton of the frm s logstcs actvtes wth those of ts supplers and customers, and more blurred organzatonal dstnctons between the logstcs actvtes of the frm and those of ts supplers and customers (Cao et al., 2015). Logstcs ntegraton refers to specfc logstcs practces and operatonal actvtes that coordnate the flow of materals from supplers to customers throughout the value stream (Khamsabad and Mad, 2013). Logstcs provdes ndustral frms wth tme and space utltes, by provdng the necessary quantty of goods be n the rght place at the rght tme (Drong et al., 2004). Grounded on earler research, the theoretcal construct of logstc ntegraton s derved to nclude the seamless ntegraton of the logstcs functon of the varous supply chan partners (Flynn et al., 2010). 2. Lterature Revew Supply Chan A supply chan s a system of organzatons, people, operatons, nformaton, and resources nvolved n movng a seres of Materals, Goods, Fnal Products or servces from supplers to end users. Supply chan actvtes nvolve the transformaton of natural resources, raw materals, nto a fnal product that s delvered to the customer (Chen et al., 2011). Logstcs It s the functon that ensures the flow of materals from supplers nto an organsaton, through operatons wthn an organsaton and then outward to customers. Products can be ether physcal, tangble goods or nvsble servces e.g. the Namban Dares delvers tangble goods such as mlk, cheese and cream to the country. Cell one and Telecom provde nvsble servces to ther customers or a combnaton of goods and servces (Khamsabad and Mad, 2013). The Role of Logstcs The role of logstcs s to ensure the smooth and effcent flow of materals from the pont of orgn through the manufacturng process (operatons) rght up to the end user. Movng materals nto the organsaton from source supplers s the actvty known as nbound logstcs (nward logstcs). Movng materals wthn the organsaton s the actvty we call materals management. On the other hand, movng materals out to customers s the actvty known as outbound logstcs (outward logstcs). In practce, logstcs s used to move both tangble and ntangble products such as nformaton, messages, programmes transmsson, entertanment, fnshed products, work n progress, people, paperwork, energy, money and mnerals (Flyn et al., 2010). Inbound Logstcs (nward logstcs) Whch nvolve relatonshps wth supplers and nclude all the actvtes requred to receve, store and dssemnate nputs. Outbound Logstcs (outward logstcs) Whch nvolve relatonshps wth customers and nclude all the actvtes requred to collect, store and dstrbute the output. (Flyn et al., 2010). 2

3 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 Operatons Are all the actvtes requred to transform nputs nto outputs (products and servces). (Khamsabad and Mad, 2013) Push Strateges A push-model logstcs s one where proected demand determnes what enters the process. For example, warm ackets get pushed to clothng retalers as summer ends and the fall and wnter seasons start. Under a push system, companes have predctablty n ther supply chans snce they know what wll come when long before t actually arrves. Ths also allows them to plan producton to meet ther needs and gves them tme to prepare a place to store the stock they receve. Pull Strateges A pull strategy s related to the ust n tme n nventory management that mnmzes stock on hand, focusng on last-second delveres. Under these strateges, products and goods enter the supply chan when customer demand ustfes t. One example of an ndustry that operates under ths strategy s a drect computer seller that wats untl t receves an order to actually buld a custom computer for the consumer. Wth a pull strategy, companes avod the cost of carryng nventory that may not sell. The rsk s that they mght not have enough nventory to meet demand f they cannot ramp up producton quckly enough. Fg 1. Pull and Push Supply Chan Supply Chan and Logstcs Integraton Integraton can be defned as the combnng and coordnatng of separate parts or elements nto a unfed whole. The unfed control of a number of successve or smlar economc or especally ndustral processes formerly carred on ndependently. They further llustrate that frms who are not nternally ntegrated comprse logstcs actvtes as fragmented and often uncoordnated, and spread throughout varous organzatonal functons wth each ndvdual functon havng ts own budget and ts own set of prortes and measurements (Wengarten et al., 2014). Internal Supply Chan Integraton The phrase nternal (or mcro) supply chan has appeared n the lterature to descrbe work amed at breakng down the barrers between functons wthn organsatons. To establsh a framework for descrbng the key functons of a typcal nternal supply chan, SCM revolves around the buyng, makng, movng and sellng of stuff s qute nstructve. It s n lne wth the buy make move sell model of product supply chans ntroduced earler. For the purposes of ths secton the author has added a ffth element, namely the store actvty. Ths has been done to ensure that all actvtes assocated wth the desgn and management of warehouses and other storage locatons s gven due recognton n the framework. Warehouse management has long been regarded 3

4 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 as an ntegral element of the logstcs actvty of frms and a sgnfcant amount of specalst knowledge and expertse n ths area has been developed over the years. Essentally, move has been dsaggregated nto separate move and store elements, reflectng the specfc characterstcs of each of these actvtes. The man elements of nternal supply chan are: buy, make, store, move and sell. (Cao et al., 2015) Inbound-Outbound Integraton Means unfyng functons and processes wthn the frm especally n the areas of warehousng, transportaton, nventory management, purchasng, demand plannng and producton. In order to ntegrate nternal operatons, frms need to have cross-functonal structures because cross-functonal nputs necesstate the consderaton of how coordnaton and ntegraton can be sustan across ths ntra-frm relatonshp. Ths can be acheve wth an approprate organzatonal structure wth fewer formaltes, more empowerment and work teams. (Zhao et al., 2011) The nature of logstcs s such that t nvolves ntrcacy, extensve documentaton and detaled management. There s a need to streamlne operatons and redesgn work routnes and processes to elmnate redundancy of work. Ths allows savngs of cost and tme, and ncreases the qualty of servces, and ultmately value to customers (Jayarm et al., 2010). Stages n Logstcs Integraton Seven stages towards ntegraton of logstcs operatons. Integraton wthn an organsaton typcally goes the followng stages: Stage1- separate logstcs functons are not gven much attenton or consdered mportant. Stage2- recognzng that the separate functons of logstcs are mportant for the success of the company. Stage3- makng mprovements of the separate functons, makng sure that each one s effcent as possble. Stage4- nternal ntegraton (recognzng the benefts of the nternal co-operaton and combnng the separate operatons, nto one). Stage5- developng and mprovng a Logstcs Plannng, to set the long-term drecton of logstcs. Stage6- benchmarkng, comparng logstcs performance wth other Organzatons, learnng from ther experences, dentfyng areas that need mprovements and developments and fndng ways of achevng ths. Stage7- contnuous mprovement and development, acceptng that further changes are nevtable and always searchng for better ways of organzng logstcs. (Stonebraker and Lao, 2006) Benefts of Logstcs Integraton 1. Genune co-operaton between all partes of supply chan wth shared nformaton and resources. 2. Lower total logstcs costs due to balanced operatons, lower stocks, less expedtng, economcs of scale, and elmnaton of acts, whch waste tme and do not add value. 3. Improved performance due to more accurate, better plannng, hgher productvty and prortzaton. 4. Improved materal and goods flow and handlng, wth better co-ordnaton gvng faster and more relable movements. 5. Better customer servce wth shorter lead tmes, faster delveres and more customzaton. 6. More flexblty wth organzatons reactng faster to changng condtons. 7. More standardzed procedures, becomng routne and well-practced wth less duplcaton of effort, nformaton and plannng. 8. Relable qualty and few nspectons, wth ntegrated qualty management programs. (Boon and Wong, 2011) 4

5 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 3. Problem Statement In order to acheve effcent supply chan ntegraton for the processes or actvtes; the organzatons should recognze and understand all the ntegraton challenges of supply chan. Our goal n ths paper s to answer the followng queston, what are the challenges of supply chan ntegraton? The reason behnd ntroducng those challenges s the absence of a sngle source n the lterature able to present all those challenges n an organzed and complete manner. Ths work needs to ntegrate all supply chan ntegraton challenges research fndngs. One of the maor challenges Iran Khodro Co, whch s actve n the automotve ndustry n Iran, s the ntegraton of supply chan and logstcs networks. There are many goals n ths regard, but most mportant are creaton of an ntegrated engneerng system for the delvery of raw materals, producton of products and dstrbuton to the fnal customer. Consderng the geographcal scope of Iran and dsperson of manufacturng stes and supplers, n ths research, we attempt to create and desgn a comprehensve logstcs network; through creaton of hubs, cross-dock and Pool Ponts, materal flow Prmary and products across the logstcs network wll shft the exstng push logstcs system to the pull logstcs system. We should gradually move towards Pull-Push logstcs system to ncreasng flexblty n market demand, connect wth more supplers, consder alternatve supplers and dependence on specfc supplers, streamlne nformaton across the supply chan, ncrease coordnaton and ntegraton The supply chan components are created through creaton of a logcal loop, as well as creaton of return relatonshps related to the flow of unloaded pallets and parts. For nstance, a company may choose to stockple fnshed product at ts dstrbuton centers to wat for orders that pull them to stores. Manufacturers mght choose to buld up nventores of raw materals especally those that go up n prce knowng that they wll be able to use them for future producton. The mportant specfcatons of Pull-Push (Hybrd) logstcs system are: 1. Producton Precson 2. Optmze Materal Flow 3. Inventory and Stock Reducton 4. Waste Reducton 5. Create better nformaton flow loop 6. Close relatonshp and more coordnaton between elements of supply chan 7. Small lots 8. Optmze pallets flow and trackng 9. Extra trps reducton 10. Improve Warehousng and the ablty to successful mplement of warehousng systems, ncludng kttng system 11. Create and desgn an effcent ntegrated supply chan Fg 2. Integrated Logstcs Glance 5

6 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 Fg 3. The operatonal steps to desgn of IOLN 6

7 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 4. Methodology At ths stage of the research, frstly, the dentfcaton of the stes and supplers of Iran Khodro Co. Then the geographc coordnates of each of them wll be specfed on the map. Then the flow of nventory wll be determned between each of the manufacturng stes and supplers. Then bottlenecks are dentfed at the supply chan level, and then the man ndcators for choosng the RIR and ther locaton wll be deducted and usng the decson makng methods n management, ther calculatons wll be carred out. Then, the type and mode of communcaton between each RIR to ntegrate the logstcs network wll be determned based on network analyss technques. In the next step, the ntegrated logstcs network wll be desgned and created. In the fnal step, the evaluaton of the desgn of the desgn chan wll be based on Key Performance Indcator (KPI), and then t wll try to contnuously mprove the ntegrated logstcs network. The operatonal steps to desgn of IOLN are shown n Fg3. What s RIR? These nclude varous types of logstc operatonal centers at the level of the supply chan, each of whch has dfferent capabltes are based on varous ndcators such as operatng cost, avalable space, flexblty, employee characterstcs, and energy. The lst of RIR and RIR combnaton that consdered as alternatves s shown n Table1. Gven the need to create a central hub to ntegrate the logstcs network, the hub as a fxed member, s consder n varous RIR combnatons. Abbrevaton RIR Alternatve Combnaton CRD Cross Dock A1 Hub, CRD, DC DC Dstrbuton Center A2 Hub, CRD, SKFW SKFW Stock Keepng-Forward Warehouse A3 Hub, CRD, PP RPFR Regonal Pallet Flow Regulator A4 Hub, CRD, BBW LPFR Local Pallet Flow Regulator A5 Hub, DC, SKFW BBW Break Bulk Warehouse A6 Hub, DC, PP RW Regonal Warehouse A7 Hub, DC, BBW RPP Regonal Pool Pont A8 Hub, PP, SKFW CPP Central Pool Pont A9 Hub, PP, BBW RH Regonal Hub A10 Hub, SKFW, BBW CH PP-RPP Central Hub Pool Pont-Regonal Pool Pont Table1. The lst of RIR and Alternatves Manufacturng Stes IKCO-Tehran IKCO-Khorasan IKCO-Tabrz IKCO-Kermanshah IKCO-Mazandaran Khodrosaz Farda IKCO-Fars Locaton Tehran Bnaloud Tabrz Sahneh Babol Semnan Shraz Table2. The lst of Manufacturng Stes and ther Locatons 7

8 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 Fg 4. IKCO Manufacturng Stes Glance Alternatves Canddate n LOZ-2 Canddate n C-LOZ Center and North Secton South Secton Canddates n LOZ-3 A1 Kangavar Garmsar Qom Sabzevar A2 Takestan Robat Karm Kashan Bardeskan A3 Zanan Varamn Isfahan Neyshabur A4 Mahneshan Aradan Shahreza Jallabad A5 Maneh Babol Abadeh Shahrud A6 Bostanabad Fruzkuh Marvdasht Mayamey Fuzzy Set Theory Table3. Alternatves Ctes to choose RIR n each Logstcs Operatonal Zone (LOZ) Zadeh (1975) frst developed fuzzy set theory when he was attemptng to solve fuzzy phenomenon problems, ncludng problems wth uncertan, ncomplete, unspecfc, or fuzzy stuatons. Fuzzy set theory s more advantageous than tradtonal set theory when descrbng set concepts n human language. It allows us to address unspecfc and fuzzy characterstcs by usng a membershp functon that parttons a fuzzy set nto subsets of members that ncompletely belong to or ncompletely do not belong to a gven subset (Chen & Chen, 2012). Ths theory provdes a dfferent way to vew the problem of modelng uncertanty and offers a wde range of computatonal tools to ad decson-makng (Karmaker & Saha, 2015). A maor contrbuton of fuzzy set theory s ts capablty of representng vague data. The theory also allows mathematcal operators and programmng apply to the fuzzy doman. A fuzzy set s a class of obects wth a contnuum of grades of membershp. Such a set s characterzed by a membershp (characterstc) functon, whch assgns to each obect a grade of membershp rangng between zero and one (Kahraman et al, 2003). 3.3 Fuzzy number and Fuzzy lngustc varable We order the unverse of dscourse such that U s a collecton of targets, where each target n the unverse of dscourse s called an element. A fuzzy number A s mapped onto U such that a random U x s apponted a ( x ) 0,1. If another element n U s greater than x, we call that element under A. real number, A 8

9 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 The unverse of real numbers R s a trangular fuzzy number, A, whch means that for x R, ( x) 0,1 and ( x L) / ( M L), L x M, ( x) ( U x) / ( U M ), M x U, A 0, otherwse, Note that A ( L, M, U), where L and U represent fuzzy probablty between the lower and upper boundares, respectvely, as n Fg. 5. A Fg. 5. Trangular fuzzy number (Chen & Chen, 2010) Fg. 6. Lngustc scale for relatve mportance (Kahraman et al., 2006) Assume two fuzzy numbers A 1 ( L1, M1, U1) and A ( L, M, U ) ; then: (1) A 1 A 2 ( L1, M1, U1) ( L2, M 2, U2 ) ( L1 L2, M1 M 2, U1 U2 ) (2) A 1 A 2 ( L1, M1, U1) ( L2, M 2, U2 ) ( L1 L2, M1M2, U1U 2 ), L 0, M 0, U 0 (3) A 1 A 2 ( L1, M1, U1) ( L2, M 2, U2 ) ( L1 L2, M1 M 2, U1 U2 ) (2) A A ( L, M, U ) ( L, M, U ) ( L / L, M / M, U / U ), L 0, M 0, U A ( L, M, U ) (1/ U,1/ M,1/ L ), L 0, M 0, U 0 The fuzzy lngustc varable s a varable that reflects dfferent aspects of human language. Its value represents the range from natural to artfcal language. When the values or meanngs of a lngustc factor are beng reflected, the resultng varable must also reflect approprate modes of change for that lngustc factor. Moreover, varables descrbng a human word or sentence can be dvded nto numerous lngustc crtera, such as equally mportant, moderately mportant, strongly mportant, very strongly mportant, and extremely mportant, as shown n Fg. 6; defntons and descrptons are shown n Table 4. For the purposes of the present study, the 6-pont scale (ust equal, equally mportant, moderately mportant, strongly mportant, very strongly mportant and extremely mportant) proposed by Kahraman et al. (2006) s used. 9

10 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 Lngustc scale for mportance Trangular fuzzy scale Trangular fuzzy recprocal scale Just equal (1, 1,1) (1, 1,1) Equally mportant (EI) (1/2, 1,3/2) (2/3, 1, 2) Weakly more mportant (WMI) (1,3/2, 2) (1/2,2/3,1) Strongly more mportant (SMI) (3/2, 2,5/2) (2/5, 1/2, 2/3) Very strongly more mportant (VSMI) (2, 5/2, 3) (1/3, 2/5, 1/2) Absolutely more mportant (AMI) (5/2, 3,7/2) (2/7, 1/3, 2/5) Table 4. Lngustc scales for mportance (Kahraman et al, 2006) The Fuzzy VIKOR Method Mult Crtera Optmzaton and Compromse Soluton methods (or VIKOR) has been developed for mult-crtera optmzaton n a complex system. It determnes the compromse soluton and best soluton from a set of alternatves. Compromse soluton wll be presented by comparng the degree of closeness to the deal alternatve and each alternatve can be evaluated by each crteron functon. A systematc approach of a fuzzy VIKOR method for mult crtera n fuzzness envronment s gven n ths secton. Accordng Tzeng et al. ths approach ams to fnd the best compromse soluton between decson-makers to be consstent wth the obectves of human cognton. VIKOR algorthm based on modfed fuzzy numbers stated as follows: Step 1: Expressed mult crtera decson-makng problem n the matrx format. There are m alternatves can defned as A ( 1, 2,..., m) whch wll be evaluated based on the crtera selected that s C ( 1, 2,..., n) each crtera has fve grade achevement g 1, 2,...,5. Subectve evaluaton s done to determne the decson matrx g X x, 1, 2,..., m; 1, 2,..., n; g 1, 2,...,5 Usng lngustc varable as shown n Table 1. Decson matrx can be expressed as follows: c c c 1 2 n A1 x11 x12 x1n A2 x21 x22 x2n X, 1, 2,..., m; 1, 2,..., n Am xm1 xm2 X mn W w1, w2,..., wn Where A1, A2,..., A m are the alternatves to be chosen, C1, C2,..., C m are the evaluaton crtera, x s the ratng of alternatve A wth respect to C, w s the mportance weght of the crteron holds. Step 2: Construct a fuzzy decson matrx. The aggregated fuzzy ratng x g of alternatves wth respect to trapezodal fuzzy numbers s modfcaton from the method of arthmetc weghted average and calculated usng the followng equaton: x 11 x 12 x 1n m n 5 x 21 x 22 x 2n X xg TzFN X mn 1 1 g1 x m1 x m2 x mn W w 1, w 2,..., w n Ths method s most often used an aggregaton process because of smple and flexble operatons and fts well wth the goals of the study. x and w are lngustc varables denoted by trapezodal fuzzy number where x s the ratng of alternatve A wth respect to C, w s the mportance weght of the crteron. A trapezodal fuzzy number can be defned as x ( a, b, c, d ) 10

11 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 Step 3: Evaluate the fuzzy mportance weght of crtera. The fuzzy weghted values for each crteron wll be determned based on the mportance of each crteron. Degree of mportance of each crteron depends on the burden borne by each school. Relatve value s drectly proportonal to the number of canddates sttng for specfc subects. Ths clearly shows the value of a hgher weght should be gven to the crtera that have more number of canddates because t brngs an addtonal burden to ensure that each canddate can understand the subect well. Therefore, f the number of canddates takng the subect, then the fuzzy mportance of subects s gven as specfed by Dakoulak et al.: n w s s 1 s s the standard devaton value for the crteron M 1 s ( x x ) mn n M m 1 wth M m x x mn, 0 w 1 and M = Total number of alternatve. M Step 4: Determne the fuzzy best value ( x ) and fuzzy worst value ( ) * x x max x, mn x. * C n.standard devaton s s gven as follows: Step 5: Compute the normalzed fuzzy decson matrx. The normalzed fuzzy decson matrx s calculated to ensure that each crteron value between 0 and 1, so that all the crtera are the standard and are comparable wth each other. In ths stuaton, VIKOR method usng lnear normalzaton to stablze. Lnear normalzaton formula ndcated by the score s and R as follows: n * x x S w and * 1 x x Step 6: Compute the ndex VIKOR Q S S R R Q v 1 v S S R R S max S, S mn S Where R max R, R mn R * x x R Max w x * x v s ntroduced as the weght n strategy of the maxmum group utlty. From the lterature, t has been nferred that the VIKOR ndex value s mostly taken as v 0.5. Step 7: Sortng the value S, R and Q n descendng order. The best alternatve n order of Q s the maxmum possble value of Q based on mert ponts that was done n ths study and symbolzed x (1) A. Wth the second largest (2) alternatve referred to A and so on untl an alternatve wth the smallest value of Q ( m) s expressed as A. Step 8: (1) The alternatves A that are n the best poston wth the maxmum value of Q wll be proposed as the best alternatves n provdng a compromse soluton f and only f satsfy two condtons: C 1: Acceptable advantage. (1) The alternatve A accepted as the best advantages when the dfference ndex VIKOR Q between alternatve (2) A and (1) A must be greater than or equal to the value of DQ, or n other words Q ( (2) Q A ) ( A (1) DQ wth 1 DQ ) M 1 C 2: Acceptable stablty n decson-makng. 11

12 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 ( ) Alternatve A must also be n the best ranked by S or R. When one of the condtons s not satsfed, a set of compromse soluton wll be proposed as follows:. If C 1 s not satsfed: (1) ( 2) ( m) ( m) Then the alternatve set A, A,..., A consdered together wth ts best A determned by the relatonshp Q Q DQ. ( A ( m) ) ( A (1) ). If C2 s not satsfed: (1) (2) Thus, both alternatve A and A are recommended as the best opton poston or n other words, the two alternatve can be descrbed as the best alternatve. R software 2:15 was used to analyses data usng fuzzy VIKOR method. Case Study In ths study, tred to use real data of IKCO Manufacturng stes for selecton of the best RIR combnaton and ther locatons. It s am to evaluate and rank the RIR and ther locatons contanng multple conflctng crtera subect. Based on the results obtaned by usng F-VIKOR Technque are shown n Tables 5-9: RIR Combnaton Alternatves Q ( ndex V IKOR ) Rankng A A A A A A A A A A Table 5. Results for RIR Combnaton Selecton RIR Locatng n LOZ-2 CRD Locatng n LOZ-2 Alternatves Q ( ndex V IKOR ) Rankng Alternatves Q ( ndex V IKOR ) Rankng A A A A A A A A A A A A Table 6. Results for RIR & CRD Locatng n LOZ-2 12

13 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 RIR Locatng n C-LOZ (Tehran-Babol) HUB Locatng n C-LOZ Alternatves Q ( ndex V IKOR ) Rankng Alternatves Q ( ndex V IKOR ) Rankng A A A A A A A A A A A A Table 7. Results for RIR (Tehran-Babol) & HUB Locatng n C-LOZ RIR Locatng n C-LOZ (Tehran-Fars) Alternatves Q ( ndex V IKOR ) Rankng A A A A A A Table 8. Results for RIR Locatng n C-LOZ (Tehran-Fars) RIR Locatng n LOZ-3 CRD Locatng n LOZ-3 Alternatves Q ( ndex V IKOR ) Rankng Alternatves Q ( ndex V IKOR ) Rankng A A A A A A A A A A A A Table 9. Results for RIR & CRD Locatng n LOZ-3 13

14 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 IOLN Desgn Accordng to results obtaned by analyzng the data by usng F-Vkor Technque, The optmal combnaton of RIR was selected as follows: HUB, CRD and RPP (Table 5). In addton, the most optmal locatons were chosen, n the followng, the proposed desgn for IOLN s shown n Fg.7, 8: Fg. 7. IOLN Desgn (Operatonal Glance) Fg 8. IOLN Desgn (Clustered Glance) 14

15 Preprnts ( NOT PEER-REVIEWED Posted: 26 June 2018 do: /preprnts v2 Symbols Freght Shppng Servce (FSS) TL TL LTL TL TL LTL TL TL TL Explanaton Symbols Explanaton Symbols Explanaton Transportaton of Consumable Parts (TCP) TCP TCP Transportaton of Unloaded Pallets (TUP) TUP TUP CRS-to-CRS TUP TUP Table 10. IOLN Legend Central Ste (CS) Regonal Ste (RS) Central Suppler (CSU) Regonal Suppler (RSU) Regonal Pool Pont (RPP) Regonal Cross Dock (RCRD) Central Cross Dock (CCRD) Regonal Ste (RS) Central Ste (CS) Legend Regonal Suppler (RSU) Hub CRD RPP Conclusons and Dscussons F-VIKOR method s an effectve technque for analysng varous types of ndex, and t has been wdely used n the rank of alternatves n the feld of management. The provded case study has demonstrated the capablty of the proposed fuzzy MCDM model to effectvely soluton for the best RIR combnaton selecton and rankng problem under a fuzzy envronment. In ths method, the rankng of the optmal combnaton of RIR locatons were chosen are assessed n lngustc varable by trapezodal fuzzy numbers and the mportance weghts of ndex are also n fuzzy number. Ths study used fuzzy VIKOR method to determne the prorty ranks of the performance for ten schools. By usng the suggested ntegrated operatonal logstcs network desgn wth novel and practcal approach. Based on the results obtaned, the analyzng the data by usng F-Vkor Technque, shows that the optmal combnaton of RIR was selected as follows: HUB, CRD and RPP. In addton, the most optmal locatons were chosen I each logstcs operatonal zone (LOZ), as follows: LOZ-2: Shahrud (RPP) and Mayamey (CRD) C-LOZ (Center and North Secton): Garmsar (RPP) C-LOZ: Robat Karm (HUB) C-LOZ (Center and South Secton): Kashan (RPP) LOZ-3: Bostanabad (RPP) and Zanan (CRD) In addton, the results of ths study could provde a reference pont for other Industres n ther efforts to mprove ther Logstcs network and move to contnues optmzaton of supply chan process. IOLN can help logstcs engneers and managers to have a comprehensve logstcs network and nsght n all over the supply chan. They can wth contnues analyss IOLN, have a good and enough readness to responsblty to demand fluctuatons and flexblty aganst the challenges and barrers to ust n tme supply and dentfcaton of crtcal routes and areas for logstcs operatons. 15

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