IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 ISSN (Onlne): 1694-0784 ISSN (Prnt): 1694-0814 1 A General Smulaton Framework for Supply Chan Modelng: State of the Art and Case Study Antono Cmno 1, Francesco Longo 2 and Govann Mrabell 3 1 Mechancal Department, Unversty of Calabra, Rende (CS), 87036, Italy 2 Mechancal Department, Unversty of Calabra, Rende (CS), 87036, Italy 3 Mechancal Department, Unversty of Calabra, Rende (CS), 87036, Italy Abstract Nowadays there s a large avalablty of dscrete event smulaton software that can be easly used n dfferent domans: from ndustry to supply chan, from healthcare to busness management, from tranng to complex systems desgn. Smulaton engnes of commercal dscrete event smulaton software use specfc rules and logcs for smulaton tme and events management. Dffcultes and lmtatons come up when commercal dscrete event smulaton software are used for modelng complex real world-systems (.e. supply chans, ndustral plants). The objectve of ths paper s twofold: frst a state of the art on commercal dscrete event smulaton software and an overvew on dscrete event smulaton models development by usng general purpose programmng languages are presented; then a Supply Chan Order Performance Smulator (SCOPS, developed n C++) for nvestgatng the nventory management problem along the supply chan under dfferent supply chan scenaros s proposed to readers. Keywords: Dscrete Event Smulaton, Smulaton languages, Supply Chan, Inventory Management. 1. Introducton As reported n [1], dscrete-event smulaton software selecton could be an exceedng dffcult task especally for nexpert users. Smulaton software selecton problem was already known many years ago. A smulaton buyer s gude that dentfes possble features to consder n smulaton software selecton s proposed n [2]. The gude ncludes n the analyss consderatons several aspects such as Input, Processng, Output, Envronment, Vendor and Costs. A survey on users requrements about dscreteevent smulaton software s presented n [3]. The analyss shows that smulaton software wth good vsualzaton/anmaton propertes are easer to use but lmted n case of complex and non-standard problems. Further lmtatons nclude lack of software compatblty, output analyss tools, advanced programmng languages. In [4] and [5] functonaltes and potentaltes of dfferent commercal dscrete-event smulaton software, n order to support users n software selecton, are reported. In ths case the author provdes the reader wth nformaton about software vendor, prmary software applcatons, hardware platform requrements, smulaton anmaton, support, tranng and prcng. Needless to say that Modelng & Smulaton should be used when analytcal approaches do not succeed n dentfyng proper solutons for analyzng complex systems (.e. supply chans, ndustral plants, etc.). For many of these systems, smulaton models must be: () flexble and parametrc (for supportng scenaros evaluaton) () tme effcent (even n correspondence of very complex real-world systems) and () repettve n ther archtectures for scalablty purposes [6]. Let us consder the tradtonal modelng approach proposed by two commercal dscrete event smulaton software, Em-Plant by Semens PLM Software solutons and Anylogc by Xj-Technologes. Both of them propose a typcal object orented modelng approach. Each dscrete event smulaton model s made up by system state varables, enttes and attrbutes, lsts processng, actvtes and delays. Usually complex systems nvolve hgh numbers of resources and enttes flowng wthn the smulaton model. The tme requred for executng a smulaton run depends on the numbers of enttes n the
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 2 smulaton model: the hgher s the number of enttes the hgher s the tme requred for executng a smulaton run. In addton, lbrares objects, whch should be used for modelng statc enttes, very often fall short of recreatng the real system wth satsfactory accuracy. In other words, the tradtonal modelng approach (proposed by em-plant and Anylogc as well as by a number of dscrete event smulaton software), presents two problems: () dffcultes n modelng complex scenaros; () too many enttes could cause computatonal heavy smulaton models. Further nformaton on dscrete event smulaton software can be found n [7]. An alternatve to commercal dscrete event smulaton software s to develop smulaton models based on general purpose programmng languages (.e. C++, Java). The use of general purpose programmng languages allows to develop ad-hoc smulaton models wth class-objects able to recreate carefully the behavor of the real world system. The objectve of ths paper s twofold: frst a state of the art on commercal dscrete event smulaton software and an overvew on dscrete event smulaton models development by usng general purpose programmng languages are presented; then a Supply Chan Order Performance Smulator (SCOPS, developed n C++) for nvestgatng the nventory management problem along the supply chan under dfferent supply chan scenaros s proposed to readers. Before gettng nto detals of the work, n the sequel a bref overvew of paper sectons s reported. Secton 2 provdes the reader wth a detaled descrpton of dfferent commercal dscrete event smulaton software. Secton 3 presents a general overvew of programmng languages and descrbes the man steps to develop a smulaton model based on general purpose programmng languages. Secton 4 presents a three stages supply chan smulaton model (called SCOPS) used for nvestgatng nventory problems along the supply chan. Secton 5 descrbes the smulaton experments carred out by usng the smulaton model. Fnally the last secton reports conclusons and research actvtes stll on gong. 2. Dscrete Event Smulaton Software Table 1 reports the results of a survey on the most wdely used dscrete event smulaton software (conducted on 100 people workng n the smulaton feld). The survey consders among others some crtcal aspects such as domans of applcaton (specfcally manufacturng and logstcs), 3D and vrtual realty potentaltes, smulaton languages, prces, etc. For each aspect and for each software the survey reports a score between 0 and 10. Table 1 help modelers n dscrete event smulaton software selecton. Moreover the followng sectons reports a bref descrpton of all the software of table 1 n terms of domans of applcablty, types of lbrares (.e. modelng lbrares, optmzaton lbrares, etc.), nputoutput functonaltes, anmaton functonaltes, etc. 2.1 Anylogc Anylogc s a Java based smulaton software, by XJ Technologes [8], used for forecastng and strategc plannng, processes analyss and optmzaton, optmal operatonal management, processes vsualzaton. It s wdely used n logstcs, supply chans, manufacturng, healthcare, consumer markets, project management, busness processes and mltary. Anylogc supports Agent Based, Dscrete Event and System Dynamcs modelng and smulaton. The latest Anylogc verson (Anylogc 6) has been released n 2007, t supports both graphcal and flow-chart modelng and provdes the user wth Java code for smulaton models extenson. For nput data analyss, Anylogc provdes the user wth Stat-Ft (a smulaton support software by Geer Mountan Software Corp.) for dstrbutons fttng and statstcs analyss. Output analyss functonaltes are provded by dfferent types of datasets, charts and hstograms (ncludng export functon to text fles or excel spreadsheet). Fnally smulaton optmzaton s performed by usng Optquest, an optmzaton tool ntegrated n Anylogc. 2.2 Arena Table 1: Survey on most wdely used Smulaton software Arena s a smulaton software by Rockwell Corporaton [9] and t s used n dfferent applcaton domans: from manufacturng to supply chan (ncludng logstcs, warehousng and dstrbuton) from customers servce and strateges to nternal busness processes. Arena (as Anylogc) provdes the user wth objects lbrares for systems modelng and wth a doman-specfc smulaton language, SIMAN [10]. Smulaton optmzatons are carred out by usng Optquest. Arena ncludes three modules respectvely called Arena Input Analyzer (for dstrbutons fttng), Arena Output Analyzer (for smulaton output analyss) and Arena Process Analyzer (for smulaton experments desgn). Moreover Arena also provdes the users anmaton at run tme as well as t allows to mport CAD drawngs to enhance anmaton capabltes.
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 ISSN (Onlne): 1694-0784 ISSN (Prnt): 1694-0814 3 Anylogc Arena AutoMod Emplant Promodel Flexsm Wtness Logstc 6.5 7.5 7 7.2 6.5 7 7.5 Manufacturng 6.6 7.5 6.5 7.2 6.7 6.7 7.5 3D Vrtual Realty 6.6 6.9 7.3 6.8 6.7 7.2 7 Smulaton Engne 7 8 7.5 8 7 7.5 8 User Ablty 7 8 6 7 9 7.5 8 User Communty 6.2 9 6.7 6.5 7.5 6.6 8.5 Smulaton Language 6.8 7 6.25 6.5 6.5 6.7 6.5 Runtme 7.5 7 6.5 6.5 7.5 6 7 Analyss tools 6.5 8 6.9 7.1 7.7 6 7.8 Internal Programmng 7.2 7 6 7 6.2 7 6.5 Modular Constructon 6.1 7 6 6.5 7.5 7 7 Prce 7 6 5.6 5.8 7 5.7 6 2.3 Automod Automod s a dscrete event smulaton software, developed by Appled Materals Inc. [11] and t s based on the doman-specfc smulaton language Automod. Typcal domans of applcaton are manufacturng, supply chan, warehousng and dstrbuton, automotve, arports and semconductor. It s strongly focused on transportaton systems ncludng objects such as conveyor, Path Mover, Power & Free, Knematc, Tran Conveyor, AS/RS, Brdge Crane, Tank & Ppe (each one customzable by the user). For nput data analyss, expermental desgn and smulaton output analyss, Automod provdes the user wth AutoStat [12]. Moreover the software ncludes dfferent modules such as AutoVew devoted to support smulaton anmaton wth AVI formats. 2.4 Em-Plant Em-plant s a Semens PLM Software solutons [13], developed for strategc producton decsons. EM-Plant enables users to create well-structured, herarchcal models of producton facltes, lnes and processes. Em-Plant object-orented archtecture and modelng capabltes allow users to create and mantan complex systems, ncludng advanced control mechansms. The Applcaton Object Lbrares support the user n modelng complex scenaros n short tme. Furthermore EM-Plant provdes the user wth a number of mathematcal analyss and statstcs functons for nput dstrbuton fttng and sngle or mult-level factor analyss, hstograms, charts, bottleneck analyzer and Gantt dagram. Experments Desgn functonaltes (wth Experments Manager) are also provded. Smulaton optmzaton s carred out by usng Genetc Algorthms and Artfcal Neural Networks. 2.5 Promodel Promodel s a dscrete event smulaton software developed by Promodel Corporaton [14] and t s used n dfferent applcaton domans: manufacturng, warehousng, logstcs and other operatonal and strategc stuatons. Promodel enables users to buld computer models of real stuatons and experment wth scenaros to fnd the best soluton. The software provdes the users wth an easy to use nterface for creatng models graphcally. Real systems randomness and varablty can be ether recreated by utlzng over 20 statstcal dstrbuton types or drectly mportng users data. Data can be drectly mported and exported wth Mcrosoft Excel and smulaton optmzatons are carred out by usng SmRunner or OptQuest. Moreover, the software technology allows the users to create customzed frontand back-end nterfaces that communcate drectly wth ProModel. 2.6 Flexsm Flexsm s developed by Flexsm Software Products [15] and allows to model, analyze, vsualze, and optmze any knd of real process - from manufacturng to supply chans. The software can be nterfaced wth common spreadsheet and database applcatons to mport and export data. Moreover, Flexsm's powerful 3D graphcs allow nmodel charts and graphs to dynamcally dsplay output statstcs. The tool Flexsm Chart gves the possblty to analyze the smulaton results and smulaton optmzatons can be performed by usng both Optquest as well as a bult-n expermenter tool. Fnally, n addton to the prevous descrbed software, Flexsm allow to create own classes, lbrares, GUIs, or applcatons.
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 4 2.7 Wtness Wtness s developed by Lanner Group Lmted [16]. It allows to represent real world processes n a dynamc anmated computer model and then experment wth what-f alternatve scenaros to dentfy the optmal soluton. The software can be easly lnked wth the most common spreadsheet, database and CAD fles. The smulaton optmzaton s performed by the Wtness Optmzer tool that can be used wth any Wtness model. Fnally the software provdes the user wth a scenaro manager tool for the analyss of the smulaton results. 3. General Purpose and Specfc Smulaton Programmng Languages There are many programmng languages, general purpose or doman-specfc smulaton language (DSL) that can be used for smulaton models development. General purpose languages are usually adopted when the programmng logcs cannot be easly expressed n GUI-based systems or when smulaton results are more mportant than advanced anmaton/vsualzaton [17]. Smulaton models can be developed both by usng dscrete-event smulaton software and general purpose languages, such as C++ or Java [18]. As reported n [1] a smulaton study requres a number of dfferent steps; t starts wth problem formulaton and passes through dfferent and teratve steps: conceptual model defnton, data collecton, smulaton model mplementaton, verfcaton, valdaton and accredtaton, smulaton experments, smulaton results analyss, documentaton and reports. Smulaton model development by usng general purpose programmng languages (.e. C++) requres a deep knowledge of the logcal foundaton of dscrete event smulaton. Among dfferent aspects to be consdered, t s mportant to underlne that dscrete event smulaton model conssts of enttes, resources control elements and operatons [19]. Dynamc enttes flow n the smulaton model (.e. parts n a manufacturng system, products n a supply chan, etc.). Statc enttes usually work as resources (a system part that provdes servces to dynamc enttes). Control elements (such as varables, boolean expressons, specfc programmng code, etc.) support smulaton model states control. Fnally, operatons represent all the actons generated by the flow of dynamc enttes wthn the smulaton model. Durng ts lfe wthn the smulaton model, an entty changes ts state dfferent tmes. There are fve dfferent entty states [19]: Ready state (the entty s ready to be processed), Actve state (the entty s currently beng processed), Tme-delayed state (the entty s delayed untl a predetermned smulaton tme), Condton-delayed state (the entty s delayed untl a specfc condton wll be solved) and Dormant state (n ths case the condton soluton that frees the entty s managed by the modeler). Entty management s supported by dfferent lsts, each one correspondng to an entty state: the CEL, (Current Event Lst for actve state entty), the FEL (Future Event Lst for Tme-delayed enttes), the DL (Delay Lst for condton-delayed enttes) and UML (User-Managed Lsts for dormant enttes). In partcular, Sman and GPSS/H call the CEL lst CEC lst (Current Events Chan), whle ProModel language calls t AL (Acton Lst). The FEL s called FEP (Future Events Heap) and FEC (Future Event Chan) respectvely by Sman and GPSS/H. After enttes states defnton and lsts creaton, the next step s the mplementaton of the phases of a smulaton run: the Intalzaton Phase (IP), the Entty Movement Phases (EMP) and the Clock Update Phase (CUP). A detaled explanaton of the smulaton run anatomy s reported n [19]. 4. A Supply Chan Smulaton Model developed n C++ Accordng to the dea to mplement smulaton models based on general purpose programmng languages, the authors propose a three stage supply chan smulaton model mplemented by usng the Borland C++ Bulder to comple the code (further nformaton on Borland C++ Bulder can be found n [20]). The acronym of the smulaton model s SCOPS (Supply-Chan Order Performance Smulator). SCOPS nvestgates the nventory management problem along a three stages supply chan and allows the user to test dfferent scenaros n terms of demand ntensty, demand varablty and lead tmes. Note that such problem can be also nvestgated by usng dscrete event smulaton software [21], [22], [23] and [24]. The supply chan conceptual model ncludes supplers, dstrbuton centers, stores and fnal customers. In the supply chan conceptual model a sngle network node can be consdered as store, dstrbuton center or suppler. A supply chan begns wth one or more supplers and ends wth one or more stores. Usually stores satsfy fnal customers demand, dstrbuton centers satsfy stores demand and plants satsfy dstrbuton centers demand. By usng these three types of nodes we can model a general supply chan (also ncludng more than three stages). Supplers, dstrbuton centers and stores work 6 days per week, 8 hours per day. Stores receve orders from customers. An order can be completely or partally
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 5 satsfed. At the end of each day, on the bass of an Order- Pont, Order-Up-to-Level (s, S) nventory control polcy, the stores decde whether place an order to the dstrbuton centers or not. Smlarly dstrbuton centers place orders to supplers accordng to the same nventory control polces. Dstrbuton centers select supplers accordng to ther lead tmes (that ncludes producton tmes and transportaton tmes). Accordng to the Order-Pont, Order-Up-to-Level polcy [25], an order s emtted whenever the avalable quantty drops to the order pont (s) or lower. A varable replenshment quantty s ordered to rase the avalable quantty to the order-up-to-level (S). For each tem the order pont s s the safety stock calculated as standard devaton of the lead-tme demand, the order-up to level S s the maxmum number of tems that can be stored n the warehouse space assgned to the tem type consdered. For the -th tem, the evaluaton of the replenshment quantty, Q (t), has to take nto consderaton the quantty avalable (n terms of nventory poston) and the order-up-to-level S. The nventory poston (equaton 1) s the on-hand nventory, plus the quantty already on order, mnus the quantty to be shpped. The calculaton of s j (t) requres the evaluaton of the demand over the lead tme. The lead tme demand of the -th tem (see equaton 2), s evaluated by usng the movng average methodology. Both at stores and dstrbuton centers levels, managers know ther peak and off-peak perods, and they usually use that knowledge to correct manually future estmates based on movng average methodology. They also correct ther future estmates based on trucks capacty and supplers quantty dscounts. Fnally equatons 3 and 4 respectvely express the order condton and calculate the replenshment quantty. P ( t) Oh ( t) Or ( t) Sh ( t) (1) t LT k t 1 Dlt ( t) Df ( k) (2) P ( t) ( s ( t) SS ( t)) (3) Q ( t) S P ( t) (4) where, P (t), nventory poston of the -th tem; Oh (t), on-hand nventory of the -th tem; Or (t), quantty already on order of the -th tem; Sh (t), quantty to be shpped of the -th tem; Dlt (t), lead tme demand of the -th tem; Df (t), demand forecast of the -th tem (evaluated by means of the movng average methodology); LT, lead tme of the -th tem; s (t), order pont at tme t of the -th tem; S, order-up-to-level of the -th tem; SS (t), safety stock at tme t of the -th tem; Q (t), quantty to be ordered at tme t of the -th tem. 4.1 Supply Chan Orders Perfomance Smulator SCOPS translates the supply chan conceptual model recreatng the complex and hgh stochastc envronment of a real supply chan. For each type of product, customers demand to stores s assumed to be Posson wth ndependent arrval processes (n relaton to product types). Quantty requred at stores s based on trangular dstrbutons wth dfferent levels of ntensty and varablty. Partally satsfed orders are recorded at stores and dstrbuton center levels for performance measures calculaton. In our applcaton example ffty stores, three dstrbuton center, ten supplers and thrty dfferent tems defne the supply chan scenaro. Fgure 1 shows the SCOPS user nterface. The SCOPS graphc nterface provdes the user wth many commands as, for nstance, smulaton tme length, start, stop and reset buttons, a check box for unque smulaton experments (that should be used for resettng the random number generator n order to compare dfferent scenaros under the same condtons), supply chan confguratons (number of tems, stores, dstrbuton centers, supplers, nput data, etc.). For each supply chan node a button allows to access the followng nformaton number of orders, arrval tmes, ordered quanttes, receved quanttes, watng tmes, fll rates. SCOPS graphc nterface also allows the user to export smulaton results on txt and excel fles. One of the most mportant features of SCOPS s the flexblty n terms of scenaros defnton. The graphc nterface gves to the user the possblty to carry out a number of dfferent what-f analyss by changng supply chan confguraton and nput parameters (.e. nventory polces, demand forecast methods, demand ntensty and varablty, lead tmes, nter-arrval tmes, number of tems, number of stores, dstrbuton centers and plants, number of supply chan echelons, etc.). Fgure 2 dsplay several SCOPS wndows the user can use for settng supply chan confguraton and nput parameters.
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 6 errors [1]. In ths regards, durng the smulaton model development, the authors tred to fnd the exstence of errors (bugs). The causes of each bug has been correctly dentfed and the model has opportunely been modfed and tested (once agan) for ensurng errors elmnaton as well as for detectng new errors. Fg. 1 SCOPS User Interface. Fg. 2 SCOPS Wndows. 4.2 SCOPS verfcaton, smulaton run length and valdaton Verfcaton and valdaton processes assess the accuracy and the qualty throughout a smulaton study [26]. Verfcaton and Valdaton are defned by the Amercan Department of Defence Drectve 5000.59 as follows: verfcaton s the process of determnng that a model mplementaton accurately represents the developer s conceptual descrpton and specfcatons, whle valdaton s the process of determnng the degree to whch a model s an accurate representaton of the real world from the perspectve of the ntended use of the model. The smulator verfcaton has been carred out by usng the debuggng technque. The debuggng technque s an teratve process whose purpose s to uncover errors or msconceptons that cause the model s falure and to defne and carry out the model changes that correct the Before gong nto detals of smulaton model valdaton, t s mportant to evaluate the optmal smulaton run length. Note that the supply chan s a non-termnatng system and one of the prorty objectves of such type of system s the evaluaton of the smulaton run length [1]. Informaton regardng the length of a smulaton run s used for the valdaton. The length s the correct trade-off between results accuracy and tme requred for executng the smulaton runs. The run length has been correctly determned usng the mean square pure error analyss (MSPE). After the MSPE analyss, the smulaton run length chosen s 390 days. Choosng for each smulaton run the length evaluated by means of MSPE analyss (390 days) the valdaton phase has been conducted by usng the Face Valdaton (nformal technque). For each retaler and for each dstrbuton centre the smulaton results, n terms of fll rate, have been compared wth real results. Note that durng the valdaton process the smulaton model works under dentcal nput condtons of the real supply chan. The Face Valdaton results have been analyzed by several experts; ther analyss revealed that, n ts doman of applcaton, the smulaton model recreates wth satsfactory accuracy the real system. 5. Supply Chan Confguraton and Desgn of Smulaton Experments The authors propose as applcaton example the nvestgaton of 27 dfferent supply chan scenaros. In partcular smulaton experments take nto account three dfferent levels for demand ntensty, demand varablty and lead tmes (mnmum, medum and maxmum respectvely ndcated wth -, 0 and + sgns). Table 1 reports (as example) factors and levels for one of the thrty tems consdered and table 3 reports scenaros descrpton n terms of smulaton experments. Each smulaton run has been replcated three tmes (totally 81 replcatons). Table 2: Factors and levels Mnmum Medum Hgh
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 7 Demand Intensty [nter-arrval tme] Demand Varablty [tem] Lead Tme [days] 3 5 8 [18,22] [16,24] [14,26] 2 3 4 After the defnton of factors levels and scenaros, the next step s the performance measures defnton. SCOPS ncludes, among others, two fll rate performance measures defned as () the rato between the number of satsfed Orders and the total number of orders; () the rato between the lost quantty and the total ordered quantty. Smulaton results, for each supply chan node and for each factors levels combnaton, are expressed n terms of average fll rate (ntended as rato between the number of satsfed Orders and the total number of orders). Table 3: Smulaton experments and supply chan scenaros Run Demand Intensty Demand Varablty Lead Tme 1 - - - 2 - - 0 3 - - + 4-0 - 5-0 0 6-0 + 7 - + - 8 - + 0 9 - + + 10 0 - - 11 0-0 12 0 - + 13 0 0-14 0 0 0 15 0 0 + 16 0 + - 17 0 + 0 18 0 + + 19 + - - 20 + - 0 21 + - + 22 + 0-23 + 0 0 24 + 0 + 25 + + - 26 + + 0 27 + + + 1 - - - 5.1 Supply Chan Scenaros analyss and comparson The huge quantty of smulaton results allows the analyss of a comprehensve set of supply chan operatve scenaros. Let us consder the smulaton results regardng the store #1; we have consdered three dfferent scenaros (low, medum and hgh lead tmes) and, wthn each scenaro, the effects of demand varablty and demand ntensty are nvestgated. Fgure 2 shows the fll rate trend at store #1 n the case of low lead tme. Fll Rate 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Fll Rate - Store 1 - Low Lead Tme Low Varablty Medum Varablty Hgh Varablty Deamand Varablty Fg. 2 Fll rate at store #1, low lead tme. Low Intensty Medum Intensty Hgh Intensty The major effect s due to changes n demand ntensty: as soon as the demand ntensty ncreases there s a strong reducton of the fll rate. A smlar trend can be observed n the case of medum and hgh lead tme (fgure 3 and fgure 4, respectvely). Fll Rate 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Fll Rate - Store 1 - Medum Lead Tme Low Varablty Medum Varablty Hgh Varablty Deamand Varablty Fg. 3 Fll Rate at store # 1, medum lead tme. Low Intensty Medum Intensty Hgh Intensty
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 8 Fll Rate 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Fll Rate - Store 1 - Hgh Lead Tme Low Varablty Medum Varablty Hgh Varablty Deamand Varablty Fg. 4 Fll Rate at store # 1, hgh lead tme. Low Intensty Medum Intensty Hgh Intensty The smultaneous comparson of fgures 2, 3 and 4 shows the effect of dfferent lead tmes on the average fll rate. The only mnor ssue s a small fll rate reducton passng from 2 days lead tme to 3 and 4 days lead tme. As addtonal aspect (not shown n fgures 2, 3, and 4), the hgher s the demand ntensty the hgher s the average on hand nventory. Smlarly the hgher s the demand varablty the hgher s the average on hand nventory. In effect, the demand forecast usually overestmates the ordered quantty n case of hgh demand ntensty and varablty. 6. Conclusons The paper frst presents an overvew on the most wdely used dscrete event smulaton software n terms of domans of applcablty, types of lbrares (.e. modelng lbrares, optmzaton lbrares, etc.), nput-output functonaltes, anmaton functonaltes, etc. In the second part the paper proposes, as alternatve to dscrete event smulaton software, the use of general purpose programmng languages and provdes the reader wth a bref descrpton about how a dscrete event smulaton model works. As applcaton example the authors propose a supply chan smulaton model (SCOPS) developed n C++. SCOPS s a flexble smulator used for nvestgatng dfferent the nventory management problem along a three stages supply chan. SCOPS smulator s currently used for reverse logstcs problems n the large scale retal supply chan. Acknowledgments All the authors gratefully thank Professor A. G. Bruzzone (Unversty of Genoa) for hs valuable support on ths manuscrpt. References [1] J. Banks, Handbook of smulaton, Prncples, Methodology, Advances, Applcaton, and Practce, New York: Wley- Interscence, 1998. [2] J. Banks, and R.G. Gbson, Smulaton software buyer s gude, IIE Soluton, pp 48-54, 1997. [3] V. Hlupc, Dscrete-Event Smulaton Software: What the Users Want, Smulaton, Vol. 73, No. 6, 1999, pp 362-370. [4] J. J. Swan, Gamng Realty: Bennal survey of dscreteevent smulaton software tools, OR/MS Today, Vol. 32, No. 6, 2005, pp. 44-55. [5] J. J. Swan, New Fronters n Smulaton, Bennal survey of dscrete-event smulaton software tools, OR/MS Today, 2007. [6] F. Longo, and G. Mrabell, An Advanced supply chan management tool based on modelng and smulaton, Computer and Industral Engneerng, Vol. 54, No. 3, 2008, pp 570-588. [7] G. S. Fshman, Dscrete-Event Smulaton: Modelng, Programmng, and Analyss. Berln: Sprnger-Verlag, 2001. [8] Anylogc by XjTech, www.xjtech.com. [9] Arena by Rockwell Corporaton, http://www.arenasmulaton.com/. [10] D. J., Hhuente, Crtque of SIMAN as a programmng language, ACM Annual Computer Scence Conference, 1987, pp 385. [11] Automod by Appled Materals Inc., http://www.automod.com/. [12] J. S. Carson, AutoStat: output statstcal analyss for AutoMod users n Proceedngs of the 1997 Wnter Smulaton Conference, 1997, pp. 649-656. [13] Em-plant by Semens PLM Software solutons, http://www.emplant.com/. [14] Promodel by Promodel Corporaton, http://www.promodel.com/products/promodel/. [15] Flexsm by Flexsm Software Products, http://www.flexsm.com/. [16] Wtness by Lanner Group Lmted, http://www.lanner.com/en/wtness.cfm. [17] V. P. Babch and A. S. Bylev, An approach to compler constructon for a general-purpose smulaton language, New York: Sprnger, 1991. [18] M. Pdd, and R. A. Cassel, Usng Java to Develop Dscrete Event Smulatons, The Journal of the Operatonal Research Socety, Vol. 51, No. 4, 2000, pp. 405-412. [19] T. J. Schrber, and D. T. Brunner, How dscrete event smulaton work, n Banks J., Handbook of Smulaton, New York: Wley Interscence, 1998. [20] K. Resdorph, and K. Henderson, Borland C++ Bulder, Apogeo, 2005. [21] G. De Sens, F. Longo, G. Mrabell, Inventory polces analyss under demand patterns and lead tmes constrants n
IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, Issue 2, No 3, March 2010 9 a real supply chan, Internatonal Journal of Producton Research, Vol. 46, No. 24, 2008, pp 6997-7016. [22] F. Longo, and G. Mrabell, An Advanced Supply Chan Management Tool Based On Modelng & Smulaton, Computer and Industral Engneerng, Vol. 54, No. 3, 2008, pp 570-588. [23] D. Curco, F. Longo, Inventory and Internal Logstcs Management as Crtcal Factors Affectng the Supply Chan Performances, Internatonal Journal of Smulaton & Process Modellng, Vol. 5, No 2, 2009, pp 127-137. [24] A. G. Bruzzone, and E. WILLIAMS, Modelng and Smulaton Methodologes for Logstcs and Manufacturng Optmzaton, Smulaton, vol. 80, 2004, pp 119-174. [25] E. Slver, F. D. Pke, R. Peterson, Inventory Management and Producton Plannng and Control, USA: John Wley & Sons, 1998. [26] O. Balc, Verfcaton, valdaton and testng, n Handbook of Smulaton, New York: Wley Interscence, 1998. Antono Cmno took hs degree n Management Engneerng, summa cum Laude, n September 2007 from the Unversty of Calabra. He s currently PhD student at the Mechancal Department of Unversty of Calabra. He has publshed more than 20 papers on nternatonal journals and conferences. Hs research actvtes concern the ntegraton of ergonomc standards, work measurement technques, artfcal ntellgence technques and Modelng & Smulaton tools for the effectve workplace desgn. Francesco Longo receved hs Ph.D. n Mechancal Engneerng from Unversty of Calabra n January 2006. He s currently Assstant Professor at the Mechancal Department of Unversty of Calabra and Drector of the Modellng & Smulaton Center Laboratory of Enterprse Solutons (MSC-LES). He has publshed more than 80 papers on nternatonal journals and conferences. Hs research nterests nclude Modelng & Smulaton tools for tranng procedures n complex envronment, supply chan management and securty. He s Assocate Edtor of the Smulaton: Transacton of the socety for Modelng & Smulaton Internatonal. For the same journal he s Guest Edtor of the specal ssue on Advances of Modelng & Smulaton n Supply Chan and Industry. He s Guest Edtor of the Internatonal Journal of Smulaton and Process Modellng, specal ssue on Industry and Supply Chan: Techncal, Economc and Envronmental Sustanablty. He s Edtor n Chef of the SCS M&S Newsletter and he works as revewer for dfferent nternatonal journals. Govann Mrabell s currently Assstant Professor at the Mechancal Department of Unversty of Calabra. He has publshed more than 60 papers on nternatonal journals and conferences. Hs research nterests nclude ergonomcs, methods and tme measurement n manufacturng systems, producton systems mantenance and relablty, qualty.