Personal Exp.

Education

M.S. Data Analytics Engineering

George Mason University, Fairfax, VA                                               Graduated in May 2017

B.S. Mathematics and information education

National Taipei University of Education                                             Graduated in June 2011

Technical Skills 

EXCEL          

·      Pivot table, LOOPUP function

·      Frontline Solvers projects includes Optimization, Decision Tree, and Monte Carlo.

SQL

·      Logical Component

R programming                                                       

·      Clean data and then input missing values to create statistical models.

·      Preprocessing dataset and create data mining clustering and classification model.

·      Apply on Bayesian Networks.

·      Experience on R shiny and Natural Language Processing package

Power BI &Tableau

·      Familiar with functions in Education version

Python                                                                  

·      Preprocess datasets and use scikit-learn package to create clustering and classification algorism

·      Apply on probability and statistics learning

·      Experience on Natural Language toolkit

Master Courses

Mega Data, Information Processing, Analytics and Decision Analysis, and Data Analytics Final Project

Projects

Google blogger: http://kuanhunglindatascience.blogspot.com/

Analytics and Decision Analysis project: Use EXCEL [1] [2] for Pivot table, LOOPUP function and Frontline Solver to create Optimization [1] [2] [3], Decision Tree, and Monte Carlo

Meta Data project: Processing data by R and then use WEKA to do data mining. Finally, this project use R, Power BI, and Tableau for data visualization.

InformationProcessing Project: Processing data by R for data mining processes and R and Tableau for data visualization.

Data Analytics FinalProject: Data analytics jobs in Virginia by processing datasets by R NLP package and use R, Power BI, and Tableau for data visualization.

Excel and Frontline Solver - INDEX function

Heuristic Methods
INDEX function

problem #6, p. 333 [1] [2]

a.

Data			Amsterdam	Athens	Berlin	Copenhagen	Dublin	Lisbon	London	Luxembourg	Madrid	Paris	Rome	Brussels
	From/To		1	2	3	4	5	6	7	8	9	10	11	12
	Amsterdam	1	0	2166	577	622	712	1889	339	319	1462	430	1297	175
	Athens	2	2166	0	1806	2132	2817	2899	2377	1905	2313	2100	1053	2092
	Berlin	3	577	1806	0	348	1273	2345	912	598	1836	878	1184	653
	Copenhagen	4	622	2132	348	0	1203	2505	942	797	2046	1027	1527	768
	Dublin	5	712	2817	1273	1203	0	1656	440	914	1452	743	1849	732
	Lisbon	6	1889	2899	2345	2505	1656	0	1616	1747	600	1482	1907	1738
	London	7	339	2377	912	942	440	1616	0	475	1259	331	1419	300
	Luxembourg	8	319	1905	598	797	914	1747	475	0	1254	293	987	190
	Madrid	9	1462	2313	1836	2046	1452	600	1259	1254	0	1033	1308	1293
	Paris	10	430	2100	878	1027	743	1482	331	293	1033	0	1108	262
	Rome	11	1297	1053	1184	1527	1849	1907	1419	987	1308	1108	0	1173
	Brussels	12	175	2092	653	768	732	1738	300	190	1293	262	1173	0

Order	1	2	3	4	5	6	7	8	9	10	11	12	1
City	12	1	4	3	2	11	9	6	5	7	10	8	12
Distaance	175	622	348	1806	1053	1308	600	1656	440	331	293	190

Minimum-distance = 8822

        The sequence is Brussels, Amsterdan, Copenhagen, Berlin, Athens, Rome, Madrid, Lisbon, Dublin, London, Paris, Luxembourg, and then Brussels. The length of the minimum-distance tour is 8822.

b.

Data			Amsterdam	Athens	Berlin	Copenhagen	Dublin	Lisbon	London	Luxembourg	Madrid	Paris	Rome	Brussels
	From/To		1	2	3	4	5	6	7	8	9	10	11	12
	Amsterdam	1	0	2166	577	622	712	1889	339	319	1462	430	1297	175
	Athens	2	2166	0	1806	2132	2817	2899	2377	1905	2313	2100	1053	2092
	Berlin	3	577	1806	0	348	1273	2345	912	598	1836	878	1184	653
	Copenhagen	4	622	2132	348	0	1203	2505	942	797	2046	1027	1527	768
	Dublin	5	712	2817	1273	1203	0	1656	440	914	1452	743	1849	732
	Lisbon	6	1889	2899	2345	2505	1656	0	1616	1747	600	1482	1907	1738
	London	7	339	2377	912	942	440	1616	0	475	1259	331	1419	300
	Luxembourg	8	319	1905	598	797	914	1747	475	0	1254	293	987	190
	Madrid	9	1462	2313	1836	2046	1452	600	1259	1254	0	1033	1308	1293
	Paris	10	430	2100	878	1027	743	1482	331	293	1033	0	1108	262
	Rome	11	1297	1053	1184	1527	1849	1907	1419	987	1308	1108	0	1173
	Brussels	12	175	2092	653	768	732	1738	300	190	1293	262	1173	0

Order	1	2	3	4	5	6	7	8	9	10	11	12
City	12	1	4	3	8	10	7	5	6	9	11	2
Distaance	175	622	348	598	293	331	440	1656	600	1308	1053

Minimum-distance = 7424

        The sequence is Brussels, Amsterdan, Copenhagen, Berlin, Luxembourg, Paris, London, Dublin, Lisbon, Madrid, Rome, and Athens.

Additional questions to answer for Part B:

		Amsterdam	Athens	Berlin	Copenhagen	Dublin	Lisbon	London	Luxembourg	Madrid	Paris	Rome	Brussels
From/To		1	2	3	4	5	6	7	8	9	10	11	12
Amsterdam	1	0	2166	577	622	712	1889	339	319	1462	430	1297	175
Athens	2	2166	0	1806	2132	2817	2899	2377	1905	2313	2100	1053	2092
Berlin	3	577	1806	0	348	1273	2345	912	598	1836	878	1184	653
Copenhagen	4	622	2132	348	0	1203	2505	942	797	2046	1027	1527	768
Dublin	5	712	2817	1273	1203	0	1656	440	914	1452	743	1849	732
Lisbon	6	1889	2899	2345	2505	1656	0	1616	1747	600	1482	1907	1738
London	7	339	2377	912	942	440	1616	0	475	1259	331	1419	300
Luxembourg	8	319	1905	598	797	914	1747	475	0	1254	293	987	190
Madrid	9	1462	2313	1836	2046	1452	600	1259	1254	0	1033	1308	1293
Paris	10	430	2100	878	1027	743	1482	331	293	1033	0	1108	262
Rome	11	1297	1053	1184	1527	1849	1907	1419	987	1308	1108	0	1173
Brussels	12	175	2092	653	768	732	1738	300	190	1293	262	1173	0

Order	1	2	3	4	5	6	7	8	9	10	11	12
City	6	9	5	7	10	8	12	1	4	3	11	2
Distaance	600	1452	440	331	293	190	175	622	348	1184	1053

Minimum-distance = 6688

        If the sequence is Athens, Rome, Madrid, Lisbon, Dublin, London, Paris, Luxembourg, Berlin, Copenhagen, Amsterdan, and Brussels, the solution will have same objective value as answer b.

Because the route is same, the distance is same.

        In this sheet, I calculate the length of the minimum-distance tour. If the trip start from Lisbon, the length of the minimum-distance tour is shortest. Therefore, I should tell my decision-maker these solutions are not truely optimal.

        If the trip don't need to go back to Brussels, the  order will change and reduce distance 1398. The solution of answer b is fewer than answer a..

Reference:
[1] Powell, Stephen G. Management Science: The Art Of Modeling With Spreadsheets, 4Th Edition. 1st ed. John Wiley & Sons, 2013. Print.
[2] "Solving Travelling Salesman Problem(TSP) using Excel Solver", YouTube, 2017. [Online]. Available: https://www.youtube.com/watch?v=-E3rSoClgMI. [Accessed: 08- Jun- 2017].