Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.

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1 Proceedngs of the 2010 Wnter Smulaton Conference B. Johansson, S. Jan, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. A SIMULATION METHODOLOGY FOR ONLINE PROCESS CONTROL OF HOT MIX ASPHALT (HMA) PRODUCTION Ozgur Kabadurmus Onkar Pathak Jeffrey S. Smth Alce E. Smth Auburn Unversty Department of Industral and Systems Engneerng Auburn, Alabama 36849, USA Haluk Yapcoglu Anadolu Unversty Department of Industral Engneerng Esksehr, 26480, TURKEY ABSTRACT The qualty of hot mx asphalt (HMA) s drectly related to the qualty of the nput aggregates and the control of the producton process. Many factors such as aggregate gradaton and mosture level affect the qualty of hot mx asphalt. As state agences dctate certan standards on qualty of the product, some qualty assurance technques have been used n HMA plants. In the current practce, a producton sample s taken and analyzed n the lab. The lab analyss takes approxmately two hours, makng t dffcult to quckly correct producton mx problems. In ths paper, a new onlne process control of asphalt producton system desgned to overcome ths problem s descrbed. In the proposed system, an mage processng system contnuously analyzes mages of the samples and the requred correctve acton s taken nstantly by a computerzed optmzaton system. In ths paper, the smulaton model of the proposed onlne process control system s presented and the results are dscussed. 1 INTRODUCTION Hot Mx Asphalt (HMA) producton s generally subcontracted to the HMA producers by state DOT (departments of transportaton) and the Federal Hghway Agency (FHWA). As a control procedure, these government nsttutons nvestgate the performance of the fnal asphalt product and determne the pay factor (Russell et al. 2001), whch ultmately determnes the amount of payment to the subcontractor. Not surprsngly, one of the man elements of the pay factor determnaton s the qualty of the asphalt. Schmtt et al. (1997) report that qualty control costs of a typcal asphalt manufacturer are approxmately 2% of total HMA constructon costs. Nevertheless, as nferor qualty asphalt products may lead to repavng of a road, the actual qualty costs would be hgher where mxng, truckng, and pavng costs correspond to 38% of total HMA constructon costs. a drum plant (Fgure 1 depcts ts man elements). Aggregates are temporarly stored at the cold feed bns (to be used n the producton), and are released onto the man conveyor n specfc amounts as requred by the ob mx formula (JMF). In general, the JMF s dctated by the state agences based on the type of proect. The aggregate mx s then conveyed to the drum where the mx s heated and btumen and the other requred components are added. Lastly, the fnshed hot mx asphalt s stored n slos before t s transferred to pavng ste. An asphalt producer bascally works on a proect bass and each proect can have dfferent specfcatons. For example, hghway roads and roads n downtown areas requre dfferent asphalt specfcatons. To meet these specfcatons, a dfferent JMF s developed for each dfferent product /10/$ IEEE 1522

2 requrement. The man concerns for assurng a good qualty product are the asphalt content and gradaton requrements. Gradaton s calculated as the percent passng values for each seve. Accordng to ndustry standards, eght dfferent seve szes are used for the JMF requrements. The qualty of the asphalt product s affected by the qualty of nputs (aggregates) and the producton process. Among those, the consstency of the aggregate gradatons and the fluctuatons n the mosture levels of aggregates are the most crtcal. Note that aggregates nclude sand, stone and other elements that are fed to the system from the cold feed bns. Agan, the prmary ssue for the aggregates s the hgh varaton n ther gradaton values. Obvously, low varaton on the gradaton s a desred property for the aggregates. The levels of varaton n the gradaton of an aggregate vary from suppler to suppler, and even from one lot of aggregates to another of the same suppler. More nterestngly, dfferent batches of the same aggregate from the same lot can be dfferent from each other due to storage and pck up condtons. As aggregates are stored (generally) n an open area, weather condtons of the storage area, such as humdty and ran, affect the mosture and gradaton of aggregates. Also, pckng up the aggregates from the stockples (typcally huge stockples) causes heterogeneous mxtures and varaton n gradaton Of aggregates. Fnshed product slo Man conveyor belt Drum Control room Cold feed bns Fgure 1: A typcal drum asphalt plant (Pavement Tools Consortum, 2010) The mportant pont s that there s no easy way to change nput aggregate qualty. A typcal quarry (aggregate provder) does not crush ther aggregates, rather aggregates are sold as they are. One soluton s to crush all aggregates nto specfc percentages of gradaton ples usng crusher machnes. However, ths would be a very costly and tme consumng process for both the quarry and the asphalt company. Therefore, varaton n aggregate gradaton wll reman as a problem n the asphalt ndustry and the most logcal opton s to admt the varaton and mprove qualty by usng another method. To overcome the above mentoned qualty problems, the authors of ths paper are currently workng on mplementng an onlne process control system that contnuously montors the asphalt producton system. In ths paper, the smulaton component of ths onlne process control system s descrbed. As gradaton s the man element for the qualty of the asphalt, ths paper consders only gradaton control. The proposed method s to frequently estmate gradaton values va an mage analyss system and to use a 1523

3 blendng model to determne the proportons of aggregate to use from each bn. Image analyss s a powerful computer-based method for gatherng nformaton of aggregate propertes (Kuo et al. 1996). Although t s not a common practce n the HMA producton ndustry, some researchers have worked on magng systems. Kuo et al. (1996) proposed a method to mprove the accuracy of gradaton estmaton by mage analyss n HMA producton. However, ther research s very lmted to the sze of the partcles; fne partcles cannot be easly detected (especally #200 seve, the fnest aggregates). Al-Rousan et al. (2005) propose smlar method for mage analyss; however, the aggregates are processed separately n ths work. West (2005) proposed state-of-the-art equpment that can be used n montorng HMA producton such as, mcrowave probes (for mosture). In hs study, vdeo magng technques are mentoned as near future technology. In ths paper, the man assumpton s that varaton on the gradaton of the nput aggregates cannot be elmnated. Nevertheless, the varaton on the gradaton of the fnal asphalt producton can be mnmzed by fndng the optmal percentage contrbuton from each bn usng a blendng mx model to mprove qualty. Here, a new onlne process control system for HMA producton s proposed and a smulaton model of ths system s presented. The paper s organzed as follows: Secton 2 defnes the problem, Secton 3 presents the proposed method, Secton 4 gves the detal of the smulaton model and Secton 5 summarzes the optmzaton model. Expermental results are provded n Secton 6, followed by concluson and future work n the last secton. 2 PROBLEM DEFINITION The current practce n ndustry for assurng the qualty of asphalt uses offlne qualty control methods. A sample s taken from producton, and then t s analyzed n the lab to assess the gradaton of the product, whch should be consstent wth the requrements specfed n JMF. The analyss process n the lab typcally lasts for two hours. If the results of the lab analyss pont to a problem, correctve acton s taken by an operator. Ths long processng tme sometmes results n product wth poor qualty. Not surprsngly, the asphalt company faces some costs related to ths poor qualty product. These costs can be ether drect penalty by state agences, or mllng (removng the asphalt) and re-pavng the road wth requred qualty asphalt. In addton, when an asphalt producer makes a superor qualty asphalt, state agences pay a premum to the company (opposte of a penalty). There are other drect costs as well. For nstance, consder that a typcal asphalt producer has a producton rate of 300 tons/hr. Due to the offlne nature of the current qualty assurance methods, by the tme a qualty problem s detected, 600 tons of producton could have already been made and all ths product s potental waste. Asphalt aggregates are melted n a a man element of the overall manufacturng cost. There s another cost, whch s related to transportaton of the asphalt and pavng the road. The asphalt product should be drectly used to pave the road, otherwse the requred temperature cannot be met and qualty of the road wll be decreased. For these reasons, the two hours of analyss tme can cause a sgnfcant loss to the company. A common practce n ndustry s that even though lab results show poor qualty, the asphalt s paved on the road. The motvaton behnd ths s that the state agences check the product of the asphalt usng samplng technques, and there s a chance that they cannot detect the poor qualty asphalt product wth 100% accuracy. In short, there are mportant costs due to poor qualty asphalt and current systems have a sgnfcant tme lag between occurrence and detecton of the problem. 3 PROPOSED METHOD In ths paper, a new process control method s proposed. Ths method s desgned to elmnate the long analyss tme n the lab. By dong so, the requred correctve acton can be taken early. 1524

4 Fgure 2 explans ths new process control method. In ths method, a new and quck mage analyss technque s proposed as opposed to tme consumng lab analyss. On the conveyor belt, mages of the aggregates are taken contnuously (e.g., n every 10 seconds). Then a computerzed mage process algorthm estmates the gradaton of those aggregates before producton. Recall that aggregate gradatons cannot be changed (those are the producton nputs) and they are hghly varable. Overall qualty of the asphalt s drectly related to those gradaton values. If the gradaton levels of the asphalt are not wthn the specfcaton lmts as dctated by the JMF, the system s out of control. In ths case, correctve acton s taken to mprove qualty by changng the percentage of aggregates n the overall mx. The percentages of the aggregates comng from dfferent bns are changed by the optmzaton blendng model (explaned n subsequent sectons). By contnuous re-blendng of the mx, the gradaton requrements of the fnal product wll be kept as close as possble to the specfcatons dctated by the JMF. Note that there s no consderable tme lag between the calculaton of the nput gradatons, re-blendng of the mx and takng correctve acton. All of the operatons are computerzed and there s no human nteracton requred. On the physcal part, after the new blend mx s calculated, ths nformaton wll be passed to the control software of the asphalt producton system. Then the mx wll be changed usng the physcal aggregate bn openngs and ther conveyor belt speeds. Fgure 2: Integraton of physcal system wth Excel Solver 4 SIMULATION MODEL In ths paper, a smulaton model of the proposed onlne process control system s descrbed. The man obectve of the smulaton model s to set parameter values of the actual system. As known, changng system parameters n real practce can be costly. Nevertheless, smulaton s a perfect tool for evaluatng parameter values. The robustness of the system can be tested usng dfferent scenaros, what-f type analyses can be done and fne tunng of parameters can easly be made. The other beneft of the smulaton model s to valdate that the proposed onlne process control system s better than tradtonal practce. Addtonally, ths model s a superor way to convnce ndustry to swtch ther current qualty control practces to the proposed onlne process control system. The smulaton s modeled n Arena Addtonally, the model works wth an MS Excel fle. In ths fle, the nputs of the smulaton model are contaned. Those nput parameters are the mean and standard devaton of the gradaton (percent passng) of each seves for all aggregates, and JMF constrants. One of the smplfyng assumptons of the model s that only four seves are taken nto account nstead of all (eght) seves. These four seves are the most nfluental ones (ncludng #200 seve) as revealed by the prelmnary analyss (regresson analyss usng real data from HMA producers). The seves consdered are: 3/8 nch, No.8, No.30 and No.200. Although the JMF requrements (constrants) 1525

5 wll be explaned n the next secton, they are (a) upper/lower lmts of percentages of overall blend weght comng from each bn, (b) upper/lower lmts on seve gradatons, and (c) % crushed, frcton, and natural sand constrants. The ntal values of the percentages of overall blend weght from each bn are read from the Excel fle, along wth mean & standard devaton of gradaton values of aggregates, and JMF constrants. Note that HMA producton s a contnuous producton n real world. However, the smulaton of the system s modeled as a dscrete event model. Many applcatons of the dscretzaton of contnuous systems are presented n the lterature. Among them, Foron et al. (2007) dscretzed an ore conveyor transport system. Ther system s very smlar to the man conveyor belt whch s used n hot mx asphalt her weght by usng the dstance covered on the conveyor. The velocty of the conveyor s consdered n ther case. In our paper, the dea of dscretzng the contnuous flow on the conveyor s adopted. The man reason of ths s that modelng the system contnually needs certan equatons of flow and they are not easy to extract. On the other hand, usng a dscretzed model of the system allows more flexblty n the model. Note that the system s dscretzed n tme (for materal flow and processes), and hypothetcally f the dscretzaton tme unts are small enough (approachng zero) the model resoluton wll be hgher and t wll behave close to the contnuous model. In ths study, one mnute has been chosen as the dscretzaton tme unt. Dscretzaton works as the dscretzed chunks of aggregates are created ndvdually as enttes to be combned and bult up to a dscretzed fnal product chunk. To llustrate, f the dscretzaton tme s 15 seconds, weghts for 15 seconds of the aggregate are created as the enttes. The fnal product wll be weghed accordngly. As the general framework (Fgure 3) suggests, frst the nput data s read from the Excel fle. Then the smulaton model starts to run, and at ths pont a trend functon starts to work. The trend functon s an mportant tool used n the smulaton, and t helps to mmc the real world condtons. In ths model, the gradaton levels of aggregate 1 stay at the orgnal values (as read from the nput fle), then ncrease, and then decrease to a certan mean. Ths represents the varaton of the gradaton of the aggregate. As explaned before, ths stuaton can be caused by varatons n stockple, dfferent sources of aggregates, or weather condtons. Ths trend functon makes the model behave smlarly to the real world. The nput gradatons are read from the Excel fle and gradatons are sampled from a Normal dstrbuton (wth a specfed mean and varance). Ths samplng process represents the mage analyss system, and the data generated from samplng s the gradatons of the aggregates. The assumpton made here s that the gradaton values obtaned by the mage process are 100% correct. Ths assumpton must be relaxed n a real lfe applcaton, but for smulaton purposes gradaton values are assumed to be precsely correct. Is control polcy used n the smulaton. There are two dfferent control polces used n the smulaton (besdes the no control polcy case): Control 1: If any overall gradaton of any seve goes beyond the control lmts (specfed by the JMF) the % of aggregate contrbutons of bns are re-optmzed. Control 2: If any overall gradaton of at least two seves go beyond the control lmts (specfed by the JMF), the % of aggregate contrbutons of bns are re-optmzed. These control polces work accordng to the movng average of the seve gradatons. As varablty n the nputs can cause natural spkes n the gradaton, detecton of a sudden and temporary shft may not be the best opton. By usng movng average the persstent shfts n the process can be detected easly. In ths paper, the movng average of the last four values (ncludng the current value) s used. Ths number s selected accordng to the prelmnary analyss results. Nevertheless, many other control polces can be ntroduced and these three are only representatve. As the enttes are created accordng to the predefned dscretzaton tme nterval, the fnal product s checked for the control rule whch s appled 1526

6 roduct s out of control, then correctve acton s taken. The correctve acton s to re-optmze the blend. Recall that the control rule operates on the movng average of seve gradatons. As an nput, actual gradaton values of each seve of the aggregates are read from Arena and wrtten to Excel as nputs for the optmzaton model. Upon obtanng the new % values of aggregates usng the optmzaton model, those new values (.e., the blend) are read from Excel and wrtten back to Arena, and the smulaton run contnues wth the new blend values. The smulaton contnues to run untl the specfed termnaton crteron s met, whch s 10 hours of smulaton tme n ths paper. Fgure 3: Integraton of smulaton model wth Excel solver In the followng paragraph, the tme lag concept of the smulaton s explaned (Fgure 4). Recall that all aggregates are stored n bns, and they are poured to the man conveyor from those bns. From both a real lfe applcaton and a smulaton perspectve, conveyor speed s mportant for combnaton of the aggregates. Suppose the dstance between any two bns s the same; then, travel tme from bn to bn (+1) s exactly same (where = 1,2,3,4). The problem s; f a fnal product (dscretzed unt) s detected as out of control and the percentage values of the aggregates are re-optmzed, then the ssue to be addressed? at once, varatons n the overall weght of the fnal product wll result. Assume a scenaro n whch the blend s re-optmzed at tme T. If all percentages are changed at the same tme, at tme T only the bn 5 percentage wll be the new one, and bns 1 to 4 wll reman the old percentages (weghts). Therefore, the total weght of the fnal product can no longer be stablzed to 300 tons/hr (a predefned parameter, producton capacty). Even more mportantly, the percentages of the aggregates wll be nether the old percentages nor the new percentages, because overall weght s changed. To overcome ths problem, the blend must be changed sequentally. Frst, bn 1 must be changed (because t s farthest to the drum), after t tme unts bn 2 s changed and met wth the aggregate 1 on the conveyor. Bns 3 and 4 are changed n smlar fashon; bn 5 must be changed 4t tme unts after the change of bn 1. Therefore, n the smulaton ths tme lag concept s appled and t s selected as 15 seconds. In smulaton, another mportant concept of tme lag s handled. Obvously, 4t tme unts are requred for changes to take effect after re-optmzaton and durng that tme any out of control pont has already been corrected wth the optmzaton. In that, durng ths 4t tme nterval Solver s not ntated to prevent redundant aggregate mx changes. The Arena smulaton model conssts of four man parts: samplng of nput varable values, seve gradaton calculatons, optmzaton, actual bn-seve gradaton read/wrte and bn openng values 1527

7 calculaton. In the frst part, the nput gradatons are sampled from the mean and varance values that are read from the Excel fle. Then seve gradatons are calculated by combnng the ndvdual bn/seve gradatons. Actual bn/seve gradaton values are wrtten to Excel before the Solver optmzaton. After optmzaton, new blend mx values are wrtten to the smulaton model and bn openng values are calculated n the smulaton. Those man parts are presented n Fgure 5 below. Fgure 4: Tme lag demonstraton for blend re-optmzaton Fgure 5: The man parts of the smulaton In addton to the above man parts, there s a control logc part n the smulaton model. Ths control logc decdes the out of control ponts n the process and ntates the Solver. User nput s requred at the begnnng of the smulaton to select a control polcy, and the control logc s appled accordng to that selecton. Ths part of the smulaton s depcted n Fgure 6. The typcal anmaton of the smulaton model s gven below n Fgure 7. On the left sde of Fgure 7, a control chart of movng average of seve gradaton s shown ncludng the upper and lower control lmts. In addton, the fgure shows the nput gradaton of Bn1Seve1 and % weght of bn 1. On the rght sde, percentage of weght of bn 1 s presented. These fgures are the maor anmatons n the smulaton. As these fgures explan the varablty and the effect of control polcy, t s very helpful to demonstrate the benefts of the proposed system. 1528

8 Fgure 6: Control logc of the smulaton Fgure 7: The typcal anmaton of the smulaton 5 OPTIMIZATION MODEL As explaned n the prevous secton, the smulaton model s ntegrated wth an optmzaton model. Ths optmzaton model s ntated when the system s out of control and t optmzes the percentages of aggregates n the overall mx. In ths study, Excel solver s used for the optmzer. The man elements of the optmzaton model are presented below. The decson varable n the model s: Percentages of overall blend weght comng from each bn (x ) 1529

9 The parameters are: Gradaton measurements from bns (g ) Target levels (by JMF) for % passng the seves (n ) Upper and lower spec lmts for % passng the seves (r mn, r max ) Upper and lower lmts for % weght comng from each bn (b mn, b max ) Mnmum and maxmum lmts on % crushed, frcton and natural sand c mn p, c max p ; p=% (crushed, frcton and natural sand) Aggregate propertes for each bn: % crushed, frcton and natural sand a p ; p=% (crushed, frcton and natural sand) The model s formally stated as: mn t (1) s.t. t r b c mn mn mn p n x g max mn r r / 2 x g r (3) max max x b (4) x a c (5) p max p x 1 (6) x 0 (7) The obectve of the model s to mnmze total devaton from target gradatons over all seves. Equaton (2) calculates the normalzed devaton for each seve. Normalzed devaton s the devaton of gradaton from the target as a percentage of the half range of spec lmts. Constrant (3) s the constrant of upper and lower control lmts of the gradatons of seves. Upper and lower lmts of the bn percentages are stated n constrant (4). % crushed, frcton and natural sand constrants are stated n constrant (5). The summaton of all the aggregate percentages must be 1, and each of them must be nonnegatve as are stated n (6) and (7). Obvously, more loose constrants allow the model to fnd better solutons. In contrast, tghter constrants make the model to fnd poor solutons. Recall that all constrants depend on the ob mx formula requrements. Nevertheless, loose constrants enable more robust solutons to the natural varatons of nputs, and tght constrants are more prone to the effects of the natural varatons of the nputs. Obvously, hgh shfts n the gradaton (such as a trend functon n the smulaton) wll affect both cases, but the latter case s expected to be affected more. 6 EXPERIMENTAL RESULTS The smulaton model s tested by varous scenaros as gven below: Low varaton n nput gradaton (Base case scenaro) Hgh varaton n nput gradaton Tght constrants (wth low varaton n nput gradaton) (2) 1530

10 Tght constrants (wth hgh varaton n nput gradaton) The reason that these scenaros are selected s that the model robustness depends on the varaton of nput gradaton and the tghtness of the JMF requrements (constrants). All values of nput parameters are selected arbtrarly. Results are obtaned by sngle run of 10 hours, whch corresponds to one workng day of a typcal asphalt manufacturer. Only one replcaton per scenaro s consdered for these prelmnary results, and more runs wll be performed as a future study. The results are summarzed n Table 1. Table 1: Number of tmes that the blend s re-optmzed (number of off target products) No Control Control 1 Control 2 Low Varaton 410 (0-0) 2 (2 0) 410 (0-0) * Hgh Varaton 376 (0-0) 2 (2-0) 376 (0-0) Tght Constrants 418 (0-0) 4 (4-0) 361 (1-0) (wth low varaton) Tght Constrants 593 (0-0) 42 (42-6) 82 (4 0) (wth hgh varaton) * # out of specfcaton lmts (# Solver ntated - # no soluton) Total cumulatve devaton from all seves In Table 1, umber of out of specfcaton lmts corresponds to tme,.e. how long (n mnutes) the system was n out of specfcaton lmts where the maxmum s 600 mnutes (10 hours of smulaton tme). umber of solver ntated ndcates that the number of nstances where the system was out of control accordng to the selected control polcy. umber of no soluton gves the number of nstances out of total number of solver ntatons n whch the optmzaton model could not fnd a feasble soluton. Total cumulatve devaton from all seves s the summaton of devaton values (over tme) of all seves where Equaton (3) defnes devaton as a percentage devatng from target gradaton value of the correspondng seve. The second observaton s that the varaton n the nput gradatons and tght requrements of JMF reduces the qualty. Not surprsngly, the Another observaton s that control 1 has nterrupted the process and re-optmzed the mx more than control 2. The reason for ths s obvous; control 2 wats for at least two seves to be out of control, whereas control 1 re-optmzes the mx when one out of control seve s observed. The fnal remark s that the tghter constrants wth hgh varaton scenaro yelds the worst results. Accordngly, n 6 out of 42 optmzaton model ntatons, Solver could not fnd any feasble solutons, and for all control polces the total normalzed devaton values are dramatcally hgher than the other scenaros. The reason of ths result s that ths scenaro has both hgh nput gradaton varaton and tght JMF constrants, so t s not surprsng to get nferor qualty. A typcal anmaton of the smulaton for the low varaton control 1 case s shown n Fgure 8. In ths fgure, all results of the smulaton are presented: how many tmes the mx was out of the specfcaton lmts, how many tmes the blend has changed, how many tmes there was no feasble soluton, normalzed devaton of each seve and the total of all the seves. 7 CONCLUSIONS AND FUTURE WORK In ths study, an alternatve to the current practce of asphalt qualty control s presented. The current practce takes approxmately two hours to detect out of control stuatons and take correctve acton. In the 1531

11 proposed onlne process control system, the system state s contnuously montored and correctve acton s taken mmedately. To do ths, an mage analyss system s proposed and an optmzaton model used to change the optmal percentages analyss system estmates the gradatons of each aggregate and sends those data to the computer. If the gradatons are wthn the specfed lmts, there s no need to change the aggregate mx. Otherwse, the mx s re-optmzed and the system contnues to produce wth the new mx values. Fgure 8: Screen shot of smulaton output for low varaton control 1 scenaro The smulaton model s desgned to mmc ths onlne process control system. It s a tool to convnce asphalt producers of the benefts of ths new system. In addton, dfferent scenaros can easly be tested and parameter values can be set to optmum values. Partcularly, dfferent control polces can be tested and the most approprate one can be selected. The results of the experments show that varaton and tght ob mx formula requrements reduce system performance. However, the effectveness of the proposed method s also shown by the number of off target (poor qualty) products. Whle the results are case dependent, the mportant pont s that the proposed system keeps producton n control and reduces the amount of poor qualty products. As future work, the benefts of the proposed system n terms of monetary value wll be calculated. Ths s essental to show the benefts of the onlne process control system to ndustry. Even more mportantly, the proposed system wll be mplemented n a real asphalt producton system. Implementaton s a challengng process because t also ncludes the valdaton of the proposed system. Durng the mplementaton process some mnor modfcatons n the onlne process control system are expected, such as mplementaton of a dfferent control polcy, or a dfferent nterval tme of mage processng. ACKNOWLEDGMENTS Ths study was supported by a grant from the Federal Hghway Admnstraton: DTFH61-05-H AU 8-D3-ISE; Dr. Jeffrey S. Smth from Dept. of Industral and Systems Engneerng and Dr. Mchael Hetzman from NCAT (Natonal Center for Asphalt Technology) at Auburn Unversty are the PIs on the grant. REFERENCES Al-Rousan, T., E. Masad, L. Myers, and C. Spegelman New methodology for shape classfcaton of aggregates. Journal of the Transportaton Research Board 1913:

12 Foron, M. M., L.A.G. Franzese, C.E. Zann, J. Fúra, L.T. Perfett, D. Leonardo, and N.L. Slva Smulaton of contnuous behavor usng dscrete tools: ore conveyor transport. In Proceedngs of the 2007 Wnter Smulaton Conference, ed. S. G. Henderson, B. Bller, M.-H. Hseh, J. Shortle, J. D. Tew, and R. R. Barton, Pscataway, New Jersey: Insttute of Electrcal and Electroncs Engneers, Inc. Kuo, C.Y., J.D. Frost, J.S. La, L.B. Wang Three-dmensonal mage analyss of aggregate partcles from orthogonal proectons. Journal of the Transportaton Research Board 1526: Pavement Tools Consortum (PTC). Interactve Pavement Gude. Avalable va < [accessed January 5, 2010]. Russell, J.S., A.S. Hanna, E.V. Nordhem, and R.L. Schmtt Testng and nspecton levels for hotmx asphaltc concrete overlays. Natonal Cooperatve Hghway Research Program NCHRP REPORT: 447. Schmtt, R.L., A.S. Hanna, and J.S. Russell Improvng asphalt pavng productvty. Journal of the Transportaton Research Board 1575: West, R Development of rapd QC procedures for evaluaton of HMA propertes durng producton. NCAT Report AUTHOR BIOGRAPHIES OZGUR KABADURMUS s a Research Assstant and Ph.D. student at Dept. of Industral and Systems Engneerng, Auburn Unversty. He receved the B.S. and M.S. n Industral Engneerng from Istanbul Techncal Unversty, Turkey n 2005 and 2008, respectvely. Hs man research areas are the analyss and desgn of producton systems, smulaton, and appled operatons research/metaheurstc optmzaton. HALUK YAPICIOGLU s an Assstant Professor n the Dept. of Industral Engneerng at Anadolu Unversty, Esksehr Turkey. He receved hs B.S. n Industral Engneerng from Anadolu Unversty, Esksehr, Turkey n 1997, M.S. n Industral Engneerng from Mddle East Techncal Unversty, Ankara, Turkey n 2001, and Ph.D. n Industral Engneerng from Auburn Unversty n Dr. Yapcoglu's research nterests nclude faclty layout and locaton, the use of evolutonary computaton technques n modelng and optmzaton and smulaton modelng and analyss of manufacturng systems. ONKAR PATHAK s a Research and Teachng Assstant and Master Vshwakarma Insttute of Technology, Pune, Inda n He wshes to work n the feld of process mprovement. JEFFREY S. SMITH s a Professor at Dept. of Industral and Systems Engneerng, Auburn Unversty. He receved the B.S. n Industral Engneerng from Auburn Unversty n 1986 and the M.S. and Ph.D. degrees n Industral Engneerng from Penn State Unversty n 1990 and 1992, respectvely. Professor Smth's research nterests nvolve the modelng and analyss of manufacturng systems, applcatons of dscrete event smulaton, and general modelng and analyss. ALICE E. SMITH s a Professor at Dept. of Industral and Systems Engneerng, Auburn Unversty. Professor Smth's research nterests nvolve the modelng, analyss and optmzaton of complex manufacturng and engneerng desgn systems usng computatonal ntellgence (artfcal neural networks, meta-heurstcs and fuzzy systems) combned wth technques from probablty and statstcs and from operatons research. Prmary applcaton areas nclude manufacturng process control, advanced materals mcrostructure, desgn for relable networks, facltes desgn and economc modelng. 1533