cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25491/ye4z-3f28
title: GTEX-WCDI-0426-SM-4GIAL

description: Sample ID: GTEX-WCDI-0426-SM-4GIAL
DOI: 10.25491/yh1z-a349
title: GTEX-S3XE-0006-SM-3K2AA

description: Sample ID: GTEX-S3XE-0006-SM-3K2AA
DOI: 10.25491/ynk5-eh79
title: GTEX-S341-1626-SM-3K2B8

description: Sample ID: GTEX-S341-1626-SM-3K2B8
DOI: 10.25491/yp72-7816
title: GTEX-XMD1-0011-R11A-SM-4AT5J

description: Sample ID: GTEX-XMD1-0011-R11A-SM-4AT5J
DOI: 10.25491/ysmx-zx34
title: GTEX-ZVTK-0326-SM-51MRR

description: Sample ID: GTEX-ZVTK-0326-SM-51MRR
DOI: 10.25491/zenm-b653
title: GTEX-XMD1-0011-R9A-SM-4AT49

description: Sample ID: GTEX-XMD1-0011-R9A-SM-4AT49
DOI: 10.25549/uschist-c104-196036
title: Daily Trojan, vol. 177, no. 37, Oct 19, 2012

description: Daily Trojan, vol. 177, no. 37, Oct 19, 2012.
DOI: 10.25549/uschist-c104-199851
title: Daily Trojan, vol. 171, no. 7, Aug 31, 2010

description: Daily Trojan, vol. 171, no. 7, Aug 31, 2010.
DOI: 10.25549/uschist-c104-205369
title: Daily Trojan, vol. 180, no. 49, Nov 04, 2013

description: Daily Trojan, vol. 180, no. 49, Nov 04, 2013.
DOI: 10.25549/uschist-dt-m173694
title: Daily Trojan, Vol. 159, No. 17, September 14, 2006

description: Daily Trojan, Vol. 159, No. 17, September 14, 2006.
DOI: 10.25549/uschist-dt-m176900
title: The Daily Southern Californian, Vol. 9, No. 62, January 17, 1913

description: The Daily Southern Californian, Vol. 9, No. 62, January 17, 1913.
DOI: 10.25549/uschist-dt-m183667
title: The Southern California Trojan, Vol. 7, No. 36, November 17, 1915

description: The Southern California Trojan, Vol. 7, No. 36, November 17, 1915.
DOI: 10.25549/uschist-dt-m183742
title: The Southern California Trojan, Vol. 7, No. 81, March 07, 1916

description: The Southern California Trojan, Vol. 7, No. 81, March 07, 1916.
DOI: 10.25549/uschist-dt-m183898
title: The Daily Southern Californian, Vol. 5, No. 39, November 24, 1914

description: The Daily Southern Californian, Vol. 5, No. 39, November 24, 1914.
DOI: 10.25549/uschist-dt-m184969
title: Daily Trojan, Vol. 25, No. 145, May 29, 1934

description: Daily Trojan, Vol. 25, No. 144 [sic, i.e. 145], May 29, 1934.
DOI: 10.25549/uschist-dt-m185046
title: Daily Trojan, Vol. 25, No. 67, January 19, 1934

description: Daily Trojan, Vol. 25, No. 67, January 19, 1934.
DOI: 10.25549/uschist-dt-m185207
title: The Daily Southern Californian, Vol. 4, No. 11, February 26, 1914

description: The Daily Southern Californian, Vol. 4, No. 11, February 26, 1914.
DOI: 10.25549/uschist-dt-m187092
title: Daily Trojan, Vol. 151, No. 18, February 09, 2004

description: Daily Trojan, Vol. 145 [sic i.e. 151], No. 18, February 09, 2004.
DOI: 10.25549/uschist-dt-m187378
title: Daily Trojan, Vol. 55, No. 94, April 09, 1964

description: Daily Trojan, Vol. 55, No. 95 [sic, i.e. 94], April 09, 1964.
DOI: 10.25549/uschist-dt-m190124
title: Daily Trojan, Vol. 90, No. 37, March 31, 1981

description: Daily Trojan, Vol. 90, No. 37, March 31, 1981.
DOI: 10.25549/uschist-dt-m190361
title: Daily Trojan, Vol. 61, No. 1, September 11, 1969

description: Daily Trojan, Vol. 61, No. 1, September 11, 1969.
DOI: 10.14457/tu.the.2017.336
title: Theoretical investigation of enantioselective ligand-host binding interactions

description: Enantiomerically pure compounds are highly demanded by the pharmaceutical, agrochemical, food additives, fragrances, and catalysts industries. Their separations require appropriate technique and the problem arise with the selection of appropriate systems of chiral selector-selectand combinations. The identification of suitable selectors for separating enantiomers requires considerable experimentation which is highly demanding with respect to time, material and labor. With the help of molecular modeling, the researchers can visualize three-dimensional structures of any chemical compounds in any kind of simulated environment, allowing them to study their physical and chemical properties. In this study, two chiral systems are studied; the chemical system consisting of beta-cyclodextrin (chiral host) and nicotine enantiomers (chiral guests), and the biological system consisting of wild-type and mutant pfDHFR (chiral hosts) and cycloguanil derivatives enantiomers (chiral ligands). The research aims to study the effect of stereochemistry in chiral hosts on the chiral guests and to predict their enantioselectivity as the potential chiral selector. The findings indicate the potential use of both beta-cyclodextrin and pfDHFR as the chiral selectors in separating nicotine and cycloguanil derivatives enantiomers, respectively.
DOI: 10.14457/tu.the.2017.337
title: Verification and modification of empirical formulas and models for computing shoaling wave heights

description: During the past decades, various existing empirical formulas and existing wave transformation models have been proposed for computing regular shoaling wave heights. There are no papers describing the applicability and accuracy of various existing empirical formulas and existing wave transformation models, based on a wide range of wave conditions. The main objective of this study is to examine the accuracy of ten empirical formulas, together with the often-used formulas (Shuto’s and linear wave formulas), and four simple wave models for computing wave shoaling and modify them or develop a new one. Seventy-one cases of measured shoaling wave heights from six sources of published experimental data were used to examine the formulas (or models). It was found that the Shuto’s formula gives the best predictions, while the linear wave formula gives reasonable overall predictions of shoaling wave heights. The best empirical formula has better accuracy than that of the linear wave formula, but has slightly less accuracy than that of Shuto’s formula. However, the best empirical formula contains an implicit equation, which has to be solved by an iteration method and may not be convenient in practice. To simplify the calculation, the best empirical formula was modified to be explicit equation. The modified formula has slightly better accuracy than that of Shuto’s formula. In addition, the models are classified into 2 groups based on theories used in the development, i.e., linear wave theory, and the combination of Stokes and cnoidal wave theories. The examination of models shows that two combined models have similar accuracy and give overall better prediction than that of the others. For large Ursell parameter, the linear wave model tends to give underestimation while the combined wave models tend to give overestimation. Moreover, the calculation of linear wave model is simpler but it gives the worst overall predictions. To get the good accuracy of linear wave model, it was modified by improving the wave shape factor. It was found that the modified model gives the best predictions than that of the others.
DOI: 10.14457/tu.the.2017.338
title: Experimental and numerical study on cold-formed steel built-up I beam with different span lengths

description: Investigation on the structural behavior of cold-formed steel beam has become more interesting due to its wide utilization in constructional steel application. However, singly-symmetric cold-formed steel section such as lipped channel section cannot carry heavy load because of its weak torsional rigidity. Therefore, cold-formed steel built-up I section has been one of alternatives to overcome this disadvantage. This study intended to investigate the behavior of built-up I beams made of cold-formed steel back-to-back C section under four-point bending test. There were four types of sections: IC10019, IC15015, IC15019, IC15024 and two different span lengths Ls=1.9m, Ls=3.8m. The cold-formed steel built-up I beams were assembled by bolts on flat webs of two identical lipped channel sections with four different connection spacings L/2, L/3, L/4, and L/6 where L is clear span length. The lateral bracing steel frame was installed at each load-bearing plate for preventing the lateral displacement or twist of specimen. The influences of web height, thickness, bolted connection spacing, and span length were examined in this research. From experimental results, it was found that the web height and thickness of cold-formed steel are the key factors to improve load capacity of the cold-formed steel built-up I beam. In addition, the effect of connection spacing on maximum loads in case of 3.8m span length is higher than that in case of 1.9m span length and the effects of span length for the section IC15019 are smaller than that for the section IC10019. Finite element program, Abaqus, is used to model the cold-formed steel built-up I beam and numerical results are compared with experimental results. The failure loads of all specimens obtained from experiment are larger than those from finite element analysis. The failure mode of local or/and distortional buckling is found in both results of experiment and finite element analysis. Hence, finite element method can be used to safely predict the ultimate strength of cold-formed steel built-up I beam.
DOI: 10.14457/tu.the.2017.339
title: Investigation on Cold-Formed Steel Built-Up Box Beams with Large Cross-Section

description: This research performed experimental studies and finite element analyses of cold-formed steel built-up box beams with large cross-section. Torsional rigidity of open single C-section is weak since cold-formed steel cross-section comprises of thin elements, and the single C-section is unsymmetrical. Modes of buckling of cold-formed steel member consist of local, distortional, and lateral-torsional buckling. In this study, flexural tests on built-up box beams assembled by two C-sections were conducted. Twelve built-up specimens with large cross-sections of C20019 and C30024 were selected. Material properties of cold-formed steel are: tensile yield strength   for 1.9 mm thickness and   for 2.4 mm thickness. Stiffening plates and self-drilling screws were used as connection system on both top and bottom flanges of built-up box beams. Four-point bending test was employed to achieve the condition of pure moment. Influences of span length and connection spacing on the behavior of built-up box beams have been discussed, in which span lengths are 2,800 mm and 3,800 mm, and connection spacings are L/2, L/3, L/4, L/6 (L is the clear span length). It was observed that there was no failure of stiffening plate and self-drilling screw. From the results of maximum loads, it was observed that the largest different percentages of maximum load compared with the specimen in case of connection spacing of L/2 in each series test including C20019-3M, C20019-4M, and C30024-4M were 6.77%, 5.29%, and -1.99% respectively. Hence, there was slight difference of maximum load when the connection spacing changed. Two failure modes were found including local buckling and the combination of local buckling and distortional buckling. In additions, when the span length decreased from 3,800 mm to 2,800 mm, the maximum load increased in the range of 32% to 42% with the average increase of 36%. Nonlinear finite element analyses have been conducted by using ABAQUS to replicate the results with the experimental studies in term of maximum loads, failure modes, vertical deflections, and strains. The comparison results between the experimental studies and finite element analyses were found to be in a good agreement. The difference percentage of maximum load between experimental and numerical results were in the range of 0.08% to 12.04%.  The buckling modes and strain values obtained from numerical results appeared to be the same with the experimental results. Thus, the present of finite element analyses are reliable and can be used to predict the maximum load and failure mode with an acceptable error.
DOI: 10.14457/tu.the.2017.340
title: Electrospun nanofiber of poly-l-lactic acid (PLLA)/graphene oxide (GO) blends

description: Graphene oxide(GO) is a material which contains many stacking layers of carbon network decorated by oxygen containing functional groups. It is a highly efficient sorbent for cation removal. However, GO disperses very well in water making it difficult to be separated and disposed after the water treatment process. In this study, GO was successfully immobilized in the PLLA nanofiber using electrospinning. The composite fibers were characterized using SEM, FTIR, and TGA to analyze their morphology and properties, TGA has been used to calculate the ratio of PLLA and GO containing in the composite, where found to be 6.9:1 of GO-microfuidzer(GOM) and 5.1:1 of GO-ultrasonic(GOU). The adsorption of cations onto this composite is a pH-dependent process, with the highest sorption capacity at pH 9, and took about 1 hour to reach equilibrium. The adsorption isotherm of this sorbent is fitted well with Langmuir model with the maximum sorption capacity of about 70 mg Cs/g GOU, 108 mg Cs/g GOM, 110 mg Cd/g GOM and 48 mg Sr/g GOM.
DOI: 10.14457/tu.the.2017.341
title: Taxi demand prediction using ensemble model and taxi GPS data analysis

description: Taxis play an important role in urban transportation. There are also many growing businesses related to taxi service such as Grab taxi, Uber etc. Obtaining the demand for transportation in the future can provide many business advantages. Taxi GPS data can be a good source for mining the demand for transportation. Understanding the taxi demand data in the future gives an opportunity to organize the taxi fleet better. Even, there are some works proposed to predict the demand of taxi but there are few studies that consider the function of areas such as hospital area, department store area, residential area, and tourist attraction. One predictive model may fit with all types of area. In this study, we use a point of interest (POI) to match taxi demand with a place to study the taxi demand in the area with a different function. First, we investigate the best predictive models that can forecast demand of taxi hourly with 7 types of area function which are airport, residential area, department store, education area, hospital, subway and tourist attraction. The models that we selected for the experiment are long short-term memory (LSTM), gated recurrent unit (GRU) and extreme gradient boosting (XGBOOST). Then, we developed an ensemble model that can forecast the demand of taxi well with all types of area function by combining those predictive models. We build the models based on a real-world dataset generated by over 5,000 taxis in Bangkok, Thailand for 4 months. The result shows that the proposed ensemble model can achieve the smallest error rate with sMAPE of 23.72%. it can outperform the other standalone models.
DOI: 10.23725/343r-fb83
title: NWD489951.b38.irc.v1.cram

description: TOPMed: NWD489951.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/3j9v-a570
title: NWD493678.b38.irc.v1.cram

description: TOPMed: NWD493678.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/7n7g-af35
title: NWD492401.b38.irc.v1.cram

description: TOPMed: NWD492401.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/9jr6-kn11
title: NWD490504.b38.irc.v1.cram

description: TOPMed: NWD490504.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/e4ns-2g58
title: NWD493451.b38.irc.v1.cram

description: TOPMed: NWD493451.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/frc9-4696
title: NWD491679.b38.irc.v1.cram

description: TOPMed: NWD491679.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/pstz-6g97
title: NWD488886.b38.irc.v1.cram

description: TOPMed: NWD488886.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/s4pd-8q70
title: NWD488627.b38.irc.v1.cram

description: TOPMed: NWD488627.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.23725/yh6b-zk40
title: NWD488720.b38.irc.v1.cram

description: TOPMed: NWD488720.b38.irc.v1.cram <br>File: CRAM file
DOI: 10.25491/02gr-6245
title: GTEX-WEY5-0726-SM-4LMID

description: Sample ID: GTEX-WEY5-0726-SM-4LMID
DOI: 10.25491/1pdv-8312
title: GTEX-ZVZP-0126-SM-5NQ6Q

description: Sample ID: GTEX-ZVZP-0126-SM-5NQ6Q
DOI: 10.25491/395d-0f13
title: GTEX-XMD1-0826-SM-4AT52

description: Sample ID: GTEX-XMD1-0826-SM-4AT52
DOI: 10.25491/3cwq-t137
title: GTEX-S3XE-1226-SM-4AD4L

description: Sample ID: GTEX-S3XE-1226-SM-4AD4L
DOI: 10.25491/3wdk-pp05
title: GTEX-WEY5-1026-SM-4LMK9

description: Sample ID: GTEX-WEY5-1026-SM-4LMK9
DOI: 10.25491/3whb-3691
title: GTEX-S3XE-0426-SM-3K2AC

description: Sample ID: GTEX-S3XE-0426-SM-3K2AC
DOI: 10.25491/5hrv-ac16
title: GTEX-XMD2-0226-SM-4WWEE

description: Sample ID: GTEX-XMD2-0226-SM-4WWEE
DOI: 10.25491/861a-1a92
title: GTEX-Z93S-2926-SM-57WB9

description: Sample ID: GTEX-Z93S-2926-SM-57WB9
DOI: 10.25491/87gk-qv76
title: GTEX-S3XE-0926-SM-4AD4S

description: Sample ID: GTEX-S3XE-0926-SM-4AD4S
DOI: 10.25491/a3mb-5h35
title: GTEX-WEY5-0826-SM-4LMIH

description: Sample ID: GTEX-WEY5-0826-SM-4LMIH
DOI: 10.25491/a9nb-m435
title: GTEX-Z93S-1726-SM-5HL8G

description: Sample ID: GTEX-Z93S-1726-SM-5HL8G
DOI: 10.25491/as2r-tn62
title: GTEX-XMD2-0326-SM-4YCD6

description: Sample ID: GTEX-XMD2-0326-SM-4YCD6
DOI: 10.25491/cdgv-rj82
title: GTEX-ZVZO-0326-SM-5HL4N

description: Sample ID: GTEX-ZVZO-0326-SM-5HL4N
DOI: 10.25491/cvzv-ye76
title: GTEX-WEY5-0926-SM-4LMI4

description: Sample ID: GTEX-WEY5-0926-SM-4LMI4
DOI: 10.25491/d1p7-h326
title: GTEX-ZVZP-0226-SM-5NQ73

description: Sample ID: GTEX-ZVZP-0226-SM-5NQ73
DOI: 10.25491/dmen-w496
title: GTEX-XMD2-0008-SM-4WWE7

description: Sample ID: GTEX-XMD2-0008-SM-4WWE7
DOI: 10.25491/dsgn-zg81
title: GTEX-WEY5-1326-SM-3GILS

description: Sample ID: GTEX-WEY5-1326-SM-3GILS
DOI: 10.25491/e759-y450
title: GTEX-XMD1-2926-SM-4AT42

description: Sample ID: GTEX-XMD1-2926-SM-4AT42
DOI: 10.25491/ect6-s617
title: GTEX-XMD2-0006-SM-4WWEG

description: Sample ID: GTEX-XMD2-0006-SM-4WWEG
DOI: 10.25491/ercq-dm20
title: GTEX-WEY5-0526-SM-3GIKZ

description: Sample ID: GTEX-WEY5-0526-SM-3GIKZ
DOI: 10.25491/fbgc-zg57
title: GTEX-WEY5-0626-SM-4LMIA

description: Sample ID: GTEX-WEY5-0626-SM-4LMIA
DOI: 10.25491/fk1m-tr09
title: GTEX-ZVZP-0004-SM-51MS8

description: Sample ID: GTEX-ZVZP-0004-SM-51MS8
DOI: 10.25491/ge21-pf59
title: GTEX-ZVZP-0008-SM-51MSX

description: Sample ID: GTEX-ZVZP-0008-SM-51MSX
DOI: 10.25491/gx29-cp14
title: GTEX-S3XE-0526-SM-4AD4G

description: Sample ID: GTEX-S3XE-0526-SM-4AD4G
DOI: 10.25491/htw8-6148
title: GTEX-S3XE-0626-SM-4AD6B

description: Sample ID: GTEX-S3XE-0626-SM-4AD6B
DOI: 10.25491/j0nv-5560
title: GTEX-WEY5-0426-SM-3GIKT

description: Sample ID: GTEX-WEY5-0426-SM-3GIKT
DOI: 10.25491/jc3c-n557
title: GTEX-S3XE-0826-SM-4AD4U

description: Sample ID: GTEX-S3XE-0826-SM-4AD4U
DOI: 10.25491/jnky-zh48
title: GTEX-Z93S-0926-SM-5HL8B

description: Sample ID: GTEX-Z93S-0926-SM-5HL8B
DOI: 10.25491/kf9z-rz90
title: GTEX-Z93S-2626-SM-57WBX

description: Sample ID: GTEX-Z93S-2626-SM-57WBX
DOI: 10.25491/kpn1-nw46
title: GTEX-Z93S-1326-SM-5CVMR

description: Sample ID: GTEX-Z93S-1326-SM-5CVMR
DOI: 10.25491/nj6x-p786
title: GTEX-Z93S-1626-SM-5CVMB

description: Sample ID: GTEX-Z93S-1626-SM-5CVMB
DOI: 10.25491/pd7g-px45
title: GTEX-ZVZP-0006-SM-51MSW

description: Sample ID: GTEX-ZVZP-0006-SM-51MSW
DOI: 10.25491/pfrd-xs74
title: GTEX-ZVZO-0126-SM-5A5L9

description: Sample ID: GTEX-ZVZO-0126-SM-5A5L9
DOI: 10.25491/ptc1-jm12
title: GTEX-Z93S-2426-SM-5HL8N

description: Sample ID: GTEX-Z93S-2426-SM-5HL8N
DOI: 10.25491/r2sc-jw31
title: GTEX-XMD2-0126-SM-4YCDU

description: Sample ID: GTEX-XMD2-0126-SM-4YCDU
DOI: 10.25491/rsnb-fb96
title: GTEX-S3XE-0326-SM-4AD6L

description: Sample ID: GTEX-S3XE-0326-SM-4AD6L
DOI: 10.25491/v9m4-6r76
title: GTEX-WEY5-0008-SM-4LMKC

description: Sample ID: GTEX-WEY5-0008-SM-4LMKC
DOI: 10.25491/vzsa-ss61
title: GTEX-ZVZO-0426-SM-5HL4Z

description: Sample ID: GTEX-ZVZO-0426-SM-5HL4Z
DOI: 10.25491/wcne-hx10
title: GTEX-ZVZO-0526-SM-5A5LB

description: Sample ID: GTEX-ZVZO-0526-SM-5A5LB
DOI: 10.25491/wdnr-tx30
title: GTEX-Z93S-0326-SM-5HL84

description: Sample ID: GTEX-Z93S-0326-SM-5HL84
DOI: 10.25491/wfw7-0c47
title: GTEX-XMD1-2826-SM-4AT5F

description: Sample ID: GTEX-XMD1-2826-SM-4AT5F
DOI: 10.25491/xf78-aa34
title: GTEX-S3XE-0008-SM-3NM8O

description: Sample ID: GTEX-S3XE-0008-SM-3NM8O
DOI: 10.25491/xrgd-vw93
title: GTEX-ZVZP-0626-SM-59HL5

description: Sample ID: GTEX-ZVZP-0626-SM-59HL5
DOI: 10.25491/y600-f573
title: GTEX-XMD2-0526-SM-4YCDI

description: Sample ID: GTEX-XMD2-0526-SM-4YCDI
DOI: 10.25549/uschist-c104-178870
title: Daily Trojan, Vol. 165, No. 26, October 01, 2008

description: Daily Trojan, Vol. 165, No. 26, October 01, 2008.
DOI: 10.25549/uschist-c104-196049
title: Daily Trojan, vol. 177, no. 39, Oct 23, 2012

description: Daily Trojan, vol. 177, no. 39, Oct 23, 2012.
DOI: 10.25549/uschist-c104-199868
title: Daily Trojan, vol. 171, no. 11, Sep 08, 2010

description: Daily Trojan, vol. 171, no. 11, Sep 08, 2010.
DOI: 10.25549/uschist-c104-205382
title: Daily Trojan, vol. 180, no. 51, Nov 06, 2013

description: Daily Trojan, vol. 180, no. 51, Nov 06, 2013.
DOI: 10.14457/tu.the.2017.342
title: Clickbait detection for Thai news headline

description: An automatic feature extraction for classifying the clickbait for Thai news headlines is presented. The first corpus of 132,948 Thai headline news was collected. To transform Thai words into features, Word2Vec is utilized to overcome the ambiguity of the word segmentation. Then, the features are automatically extract using a Convolutional Neural Network (CNN). A number of experiments for CNN have been conducted to find the suitable value of the parameters that achieve the best classification result. We found that using a non-static modelling technique together with 50 dimension of Word2vec feature, {2, 3, 4, 5, 6} window size, and epoch equal to 4 achieves the accuracy of 94.37%. The experimental results also showed that the proposed method achieves the best result as compared to the other classification methods such as Support Vector Machine (SVM) and Naïve Bayes, which achieve 92.05% and 89.70%, respectively.
DOI: 10.14457/tu.the.2017.343
title: Supply chain management model for renewable energy resource from biomass

description: Supply chain management is a key strategy of any business. Biomass supply chain is the key fundamental to develop environmental strategy and energy security of many countries. This study addresses the biomass supply chain planning. Due to uncertainties, large number of facilities and interaction among them, biomass supply chain becomes large and complex. Uncertainty in demand and supplier capacity also affects biomass plant location decision. A mixed integer programming model is formulated to determine the optimal number and locations of biomass plants. The objective is to maximize profit by satisfying customer demand, after considering various costs such as penalty cost for lost demand, factory fixed cost, purchasing cost, transportation cost, and holding cost. A simulation based optimization model is proposed to solve large instance problems. Hybrid model incorporates advantages from both optimization model and simulation model. The connection of OPL Cplex and Matlab Simulink can give a solution in acceptable time based on computer architecture limitation. The model considered in this research is a biomass supply chain planning model based on a case study in Vietnam.  The problem considers 63 provinces which are grouped into 8 sub regions, or 3 bigger regions, and 196 industrial park locations as candidates for biomass plants. The distances between suppliers and biomass factories are based on real data which were calculated via Google map. Information about 12 types of biomass including quantity, seasonal effect, ratio to convert Mwh, and various cost were collected and analyzed.  Demand was forecasted from historical data, provided by Vietnam electricity authority.