TEACHING

• University of California, Riverside
  

  MGT 201: Quantitative Analysis (MBA core), Winter 09, Winter 10, Winter 11, Winter 2012 
  MGT 239: Simulation for Business (MBA elective), Spring 09, Fall 09, Fall 10, Fall 11
  MGT 290: Directed Studies (MBA elective), Winter 10, Spring 10, Spring 11
  BUS 125:  Simulation for Business, Fall 10, Fall 11
  

• University of Dayton
  DSC210: Statistics for Business I, Fall 07, Spring 08
• Penn State University
  Lecturer – Introduction to Statistics for Business, 05
  Teaching Assistant – Demand Fulfillment (MBA), 06
  Teaching Assistant – Introduction to Statistics for Business, 04 – 06

• Course homepage:    http://ilearn.ucr.edu
• Office hours:   W 1:40 PM – 3:40 PM, or by appointment
• Class time: MGT 201: MW 3:40 PM -5:00 PM, MW 7:10 PM -8:30 PM, ANDHL118
• Required Textbooks:
  • Statistics for Managers, by Levine, Stephan, Krehbiel, & Berenson, 6th Ed., Prentice-Hall, 2010
  • Management Science: The Art of Modeling with Spreadsheets, by Stephen G. Powell, Kenneth R. Baker, Wiley, 3rd edition, 2010
• Reference: Mathematical Statistics and Data Analysis, by John A. Rice, 3rd Ed., Duxbury Press, 2006
• Prerequisite:  MGT 403 (Calculus, linear algebra, and probability), Excel

SCHEDULE:

1. WEEK 1
1. Introduction, descriptive statistics
2. Assigned Readings: 
   lecture slides 1-3 (iLearn)
   chapters 2, 3
   review Excel (ilearn)
3. Suggested Readings:
   For Today’s Graduate, Just One Word: Statistics (The New York Times pdf)
   The Joy of Stats: 200 years, 200 countries, 4 minutes (BBC)
   Math will rock your word (Businessweek)
4. HW1, due at 12 pm, Jan. 16 th, on iLearn
2. WEEK 2
1. Descriptive statistics, basic probability
2. Assigned readings:
   lecture slides 3, 4 (iLearn)
   chapter 4
3. Suggested Readings:
   The least surprising correlation of all time (follow-up) 
   Peter Donnelly shows how stats fool juries
4. HW2, due on Jan. 23, in class

Anderson Graduate School of Management, University of California, Riverside, CA 92521

Zhaolin Li
Faculty of Economics and Business, The University of Sydney, Australia

This paper studies a supply risk management problem in the export-processing trade model, where the retailer who has a certain targeted purchasing quantity, supplies an appropriate amount of raw materials, and the supplier receives the processing fee for processing and exporting the final products. The supplier faces stochastically proportional yields. We characterize that the equilibrium has a two-threshold structure. The supplier with low and high yields may withhold the information while the supplier with medium yields may disclose depending on various parameters. Sensitivity analysis reveals that higher market potential, lower raw material costs, and lower information costs facilitate information flow, thereby improving the performance of all parties within the supply chain. When negotiating contracts, the retailer should consider not only the direct gain from lowering processing fees, but also the indirect loss from its suppressing effect on the supplier’s informational imperative. Our paper considers endogenous asymmetric supply risk information and rationalizes the supplier’s incentives for voluntarily sharing information when interacting with the retailer’s ordering decision.

Key words: export-processing, supply risk, random yield, information asymmetry, voluntary information sharing

Class of MGT 239, Fall 2011

• Homepage:    http://ilearn.ucr.edu
• Prerequisite:  probability and statistics, Excel, “common sense”
• Required Text:
  MS: Management Science: The Art of Modeling with Spreadsheets, by Stephen G. Powell, Kenneth R. Baker, Wiley, 3rd edition, 2010
  ASM: Applied Simulation Modeling, by Seila, Ceric and Tadikamalla, Duxbury Press, 2003
• References:

  Probability Models for Economic Decisions, by Roger B. Myerson, Duxbury Press, 2005
  Simulation, by Sheldon M. Ross, Academic Press, 4 edition, 2006

SCHEDULE (Chapters 1-4, 10, & Appendix)

1. WEEK 1
   1. Syllabus and the tentative schedule
   2. Introduction of simulation: system, analytical and simulation model (Chapter 1)
   3. HW1 due at 9am, Sept. 28 on iLearn
   4. Excel tutorial: 1, 2, 3 , add-in
   5. Reading:
      Chapter 3 in MS: basic Excel skills
      Chapter 1 in ASM: introduction
      Appendix in ASM (review probability and statistics for week II)
2. Week 2
   1. Review basic statistics and probability
   2. Estimate
   3. The sale promotion model
   4. Team sign-up
      BUS 125 http://tinyurl.com/bus125f11team
      MGT 239 http://tinyurl.com/mgt239f11team
   5. HW2 due on Oct. 4 at 9am on iLearn
   6. Reading:
      Read chapters 2.1, 2.4, 2.5, 2.6 and Appendix in ASM
      Read Chapter 7 in MS: data analysis for modeling
      Add-in: http://home.uchicago.edu/~rmyerson/addins.htm
3. Wee 3
   1. Inverse transform method for generating random variates (chapter 2.5)
   2. Simulate a dinner deal
   3. Sample project topics (iLearn)
   4. HW3 due at 9 am Oct.11, on iLearn
   5. Reading:
      chapters 2.5, 2.6 on ASM
      lecture slides 4
      Add-in: http://home.uchicago.edu/~rmyerson/addins.htm
4. Week 4
   1. Simulate a newsvendor inventory model (chapter 2.6 on ASM)
   2. Simulate an insurance model (chapter 2.9 on ASM)
   3. HW4 due at 9am on Oct. 18, iLearn
   4. Reading:
      chapters 2.6 and 2.9 on ASM for in-class exercise
      chapters 4.1, 3.1, 3.2, 3.3, 3.4 on ASM for next week
5. Week 5
   1. Dynamic simulation (chapter 4.1)
   2. Simulate the evolution of a stock’s price (chapters 3.1, 3.2)
   3. Pricing Asian options (chapters 3.3, 3.4)
   4. Risk management and financial hedging with options
   5. HW5 due at 5am on Oct. 25, iLearn
   6. Reading: Chapter 10 for team project
6. Week 6
   1. Evaluate investment strategies
   2. A dynamic, multiperiod supply chain management problem
   3. HW6, due on Nov. 1, 9am on iLearn
   4. Project proposal due on Nov. 1, 9am on iLearn
7. Week 7
   1. Joint pricing and inventory management in car rental business
   2. Midterm 1, open book/notes in class, 80 minutes
   3. HW7 due on Nov. 8, 9am on iLearn
8. Week 8
   1. Team presentation and project discussion
   2. Revenue Management
   3. HW 8 due on Nov. 17, 9am on iLearn
   4. Project interim report due on Nov. 17, 9am on iLearn
9. Week 9
   1. Nov. 15: Team project meeting
   2. Nov. 17: Auctions and bidding
   3. HW9 due on Nov. 24, 9 am on iLearn
10. Week 10
    1. Nov. 22: Midterm 2
    2. Nov. 24: Holiday
11. Week 11
    1. Dress code for project final presentation
    2. Project presentation
12. Final Week
    1. Final project report due on Dec. 6, 9am on iLearn
    2. Peer review report due on Dec. 6, 9am on ilearn

• Course homepage:    http://ilearn.ucr.edu
• Office hours:   W 1:40 PM – 3:40 PM, or by appointment
• Class time: MGT 201: MW 3:40 PM -5:00 PM, MW 7:10 PM -8:30 PM, ANDHL118
• Textbook: Statistics for Managers, by Levine, Stephan, Krehbiel, & Berenson, 6th Ed., Prentice-Hall, 2010
• Reference: Mathematical Statistics and Data Analysis, by John A. Rice, 3rd Ed., Duxbury Press, 2006
• Prerequisite:  MGT 403 (Calculus, linear algebra, and probability), Excel

SCHEDULE:

1. WEEK 1
   1. Introduction, descriptive statistics
   2. Assigned Readings: 
      lecture slides 1-3 (iLearn)
      chapters 2, 3
      review Excel (ilearn)
   3. Suggested Readings:
      For Today’s Graduate, Just One Word: Statistics (The New York Times pdf)
      The Joy of Stats: 200 years, 200 countries, 4 minutes (BBC)
      Math will rock your word (Businessweek)
   4. HW1, due at 12 pm, Jan. 10th, on iLearn
      
2. WEEK 2
   1. Descriptive statistics, basic probability
   2. Assigned readings:
      lecture slides 3, 4 (iLearn)
      chapter 4
   3. Suggested Readings:
      The least surprising correlation of all time (follow-up) 
      Peter Donnelly shows how stats fool juries
   4. HW2, due on Jan. 19
3. WEEK 3
   1. Discrete probability distributions
   2. Assigned readings:
      lecture slides 5, 6 (iLearn)
      chapter 5
   3. Suggested readings:
      The aftershocks of crime (The Economist)
   4. HW3, due on Jan. 24
4. WEEK 4
   1. Continuous probability distributions
   2. Assigned readings:
      lecture slides 7, 8 (iLearn)
      chapter 6
   3. HW4, due on Jan. 31
5. WEEK 5
   1. Normal and other distributions
   2. Assigned readings:
      lectures 7, 8, 9
      chapters 5, 6
   3. Suggested readings:
      The rise and rise of the cognitive elite
   4. HW5, due on Feb. 7
6. WEEK 6
   1. Review
   2. Midterm, Feb. 9, bring a bluebook, in class, 80 minutes, open book/notes
   3. HW6, due on Feb. 14
7. WEEK 7
   1. Sampling distributions, confidence interval
   2. Assigned readings:
      lecture slides 10, 11
      chapters 7, 8
   3. Suggested readings:
      Lies, damn lies, and job statistics (The New Yorker)
   4. HW7, due on Feb. 21
8. WEEK 8
   1. Confidence interval, hypothesis test
   2. Assigned readings:
      lecture slides 12, 13
      chapters 8, 9
   3. Suggested readings: Chilling consequences
   4. HW8, due on Feb. 28
9. WEEK 9
   1. hypothesis test
   2. Assigned readings:
      lecture slides 13
      chapter 9
   3. Suggested readings: The beautiful and the damned
   4. HW9, due on Mar. 7
10. WEEK 10
    1. linear regression
    2. Assigned readings:
       lecture 14
       chapters 13, 14
    3. Suggested readings: An End to the Quick Fixes
11. FINAL EXAM
    March 14, Anderson 118
    session 1: 3:30 pm- 6:30 pm
    session 2: 7:00 pm – 10:00 pm
    bring UCR bluebook, calculator, open book/notes

Long Gao
Anderson Graduate School of Management, University of California, Riverside, CA 92521, USA

Douglas J. Thomas
Smeal College of Business,  The Pennsylvania State University, University Park, PA 16802, USA

A pre-pack is a collection of items used in retail distribution. By grouping multiple units of one or more stock keeping units (SKU), distribution and handling costs can be reduced; however, ordering flexibility at the retail outlet is limited. This paper studies an inventory system at a retail level where both pre-packs and individual items (at additional handling cost) can be ordered. For the single-SKU, single-period problem,  we show that the optimal policy is to order into a `band’ with as few individual units as possible. For the multi-period problem with uniform demand, the band policy is still optimal; the steady-state distribution of the target inventory possesses semi-uniform structure, which greatly facilitates the computation of the optimal policies and approximations under general demand. The band structure also extends to the multi-SKU case in a multidimensional form. Our numerical results show that pre-pack use tends to be beneficial for stable and complementary demands and when the pre-packs enable substantial handling savings at the distribution center. The cost premium of using simple policies, such as strict base-stock and batch-ordering (pre-packs only), can be substantial for the medium parameter ranges.

Key words: periodic review policies, pre-pack, batch ordering, steady-state distribution, demand correlation.

Michael F. Gorman
School of Business, University of Dayton, Dayton, OH 45469

Zhaolin Li
Faculty of Economics and Business, The University of Sydney, Australia

We study the real-time load acceptance problem in intermodal transportation, where limited inventoriable container capacity is dynamically allocated to customers with different profit levels and fulfillment leadtime requirements. The container supply in real-time, short-term decision horizon is exogenously determined by scheduled arrivals of repositioned containers and accepted loads from other locations in previous cycles. The primary load acceptance decision involves trade-offs between accepting a tendered load now with on hand capacity or rejecting it to reserve capacity for potential future high profit loads. We formulate the problem as a stochastic dynamic programming problem with integrated strategic decisions and network effects in the model. We then characterize the optimal load acceptance policy with simple parameters suitable for industrial-sized applications.  We show that three forces driving the optimal policy are in effect over different time frames and capacity ranges.  Among them, market segmentation and order fulfillment flexibility can significantly improve profitability over all capacity levels, while intertemporal rationing is effective for supply shortages. Based on real world data, we demonstrate that the optimal policy is robust and can significantly improve system profitability and service levels. Our model can be used for short-term local load acceptance execution in conjunction with a central tactical network capacity planning in freight transportation.

Key words : load acceptance process, intermodal freight transportation, revenue management, dynamic programming, rationing

MGT 239, FALL 2010

• Office hours:   R 3:30 PM -5:30 PM , or by appointment, Anderson Hall 221
• Homepage:    http://ilearn.ucr.edu
• Prerequisite:  basic probability and statistics, Excel
• Required text: Applied Simulation Modeling, by Seila, Ceric and Tadikamalla, Duxbury Press, 2003
• References:
  Probability Models for Economic Decisions, by Roger B. Myerson, Duxbury Press, 2005
  Simulation, by Sheldon M. Ross, Academic Press, 4 edition, 2006

SCHEDULE (Chapters 1-4, 10, & Appendix)

1. WEEK I
   1. Syllabus and the tentative schedule
   2. Introduction of simulation: system, analytical and simulation model (Chapter 1)
   3. HW1 due at 9am, Sept. 28 on iLearn.
   4. Excel tutorial: 1, 2, 3 , add-in
   5. Reading:
      Chapter 1 (introduction)
      Appendix (review probability and statistics for week II)
      Math will rock your world

      

2. WEEK II
   1. Review basic probability (Appendix)
   2. Random variates generation (Chapter 2.5)
   3. Compute by simulation (Chapter 2.4)
   4. Simulate a sale promotion model (Chapter 2.2)
   5. HW2 due at 9am, Oct. 5 on iLearn
   6. Project team sign-up: BUS 125, MGT 239
   7. Reading: Chapter 2.2, 2.4, 2.5

       

3. WEEK III
   1. Inverse transform method for generating random variates (chapter 2.5)
   2. Simulate a dinner deal
   3. Sample project topics (iLearn)
   4. HW3 due at 9 am 10/12/2010, on iLearn.
   5. Reading: chapter 2.5, 2.6
      
4. WEEK IV
   1. Newsvendor model (chapter 2.6)
   2. Simulate a one-period inventory model
   3. Simulate an insurance model (chapter 2.9)
   4. HW4, due at 9am on Oct. 19, iLearn.
   5. Reading: chapters 4.1, 3.1, 3.2, 3.3, 3.4
      
5. WEEK V
   1. Dynamic simulation (chapter 4.1)
   2. Simulate the evolution of a stock’s price (chapters 3.1, 3.2)
   3. Pricing Asian options (chapters 3.3, 3.4)
   4. Risk management and financial hedging with options
   5. HW5 due at 9am on Oct. 26, iLearn
   6. Reading: Chapter 10 for team project 

      
       

6. WEEK VI
   1. Evaluating investment strategies
   2. A dynamic, multiperiod supply chain management problem
   3. Project proposal due at 9m on Oct. 28, iLearn
   4. HW6, due on Nov. 2, 9am on iLearn
          
      
7. WEEK VII
   1. Pricing strategies and inventory control
      
       
   2. Nov. 4, Thursday: Midterm 1, open book/notes, 80 minutes
   3. HW7, due on Nov. 16, 9am on iLearn
8. WEEK VIII
   1. Nov. 9, Tuesday: team project meeting
   2. Nov. 11, Thursday: Veteran’s day
9. WEEK IX
   1. Revenue management
   2. Auctions and bidding: auction house
   3. Project interim report due on Nov. 18, 9am on iLearn
   4. HW8, due on Nov. 23, 9am on iLearn 
      
10. WEEK X
    1. Nov. 23, Tuesday: Midterm 2, take home, open book/notes
    2. Nov. 25, Thursday: Thanksgiving holiday
11. WEEK XI
1. Dress code for project final presentation
2. BUS 125 Schedule:
   Investment Portfolio, Jack Gillete, Kevin Lim, Kevin Luangpraseuth, Geremy Mason
   Supply Chain Management as a Function of Reliability and Price, Linda Tong, Rebekah Wong
   Modeling the spread of HIV, Ashlee Yee, Pheobe Nguyen, Daniel Avila
   Airline Overbooking Simulation, Gloria Feng, Gerard Holland
3. MGT 239 Schedule:
   Simulating the Photovoltaic Market in Riverside, Huiqing Ma,Isaac Lee,Meng Mu, James Yang
   Stock Price Forecasting, Danxi (Max) Cao, Yilun Li, Nobuhiro Namba, Sang Eun Park
   Profit and Customer Satisfaction, Matteo Campanerut, Dandan Chen, Sebastian Dombrowski, Ting Luo
   Business Development of start-up Online retail store, Zach STewart, Lucy Nagawkar, Deepak Kumar, Vikranth karmegam
   N-Mac Carrier and Freight Service, Aaron Chang, Lan-Hsin Hsu, Min-Chen Wu, Yasar Numan AKSANYAR
   Black Jack Simulation, Keerthi Madhav Rasenini, Xishi (Florrie) Wang, Firas Al-Sayegh, Melissa Perez
   Black Jack Simulation, Yim (Glenda) Lang, Thao Nguyen, Sheri Staszewski, Cong (Steve) Zhou
   Flower Store Management on Valentine’s Day, Joseph Liao, Haifeng Yao, Minghui Zhong, Yan Zhong
4. Presentation slides
12. FINAL WEEK
    1. Project final report due, Dec. 7, at 9am on iLearn
    2. Peer review report ude, Dec. 7, at 9am on iLearn 

Susan H. Xu
Smeal College of Business, The Pennsylvaina State University, University Park, PA 16802

Michael O. Ball
Robert H. Smith School of Business , University of Maryland, College Park, MD 20742

We study an order promising problem  in a multi-class, Available-to-Promise (ATP) assembly system in the presence of pseudo orders. A pseudo order refers to a  tentative customer order whose attributes, such as the likelihood of an actual order, order quantity and confirmation timing, can  change dynamically over time. Each product is assembled from two major components, with one component requiring one unit of production capacity and another one unit of component inventory. An accepted order must be filled before a  delivery lead time. The underlying order acceptance decisions involve tradeoffs between committing  resources (production capacity and component inventory) to low-reward firm orders and reserving resources in anticipation of  high-reward pseudo orders. We develop a Markov chain model that captures the key characteristics of pseudo orders, including demand lumpiness, non-stationarity and volatility. We then formulate a stochastic dynamic program for the ATP assembly (ATP-A) system that embeds the Markov chain model as a short-term forecast for pseudo orders. We  show that the optimal order acceptance policy is characterized by  class prioritization,  resource imbalance-based (RIB) rationing and capacity-inventory-demand (CID) matching. In particular, the rationing level for each class is determined by a critical value  that depends on the resource imbalance level, defined as the net difference between the component inventory and production capacity level, only through their difference. Extensive   numerical tests underscore the importance of the key properties of the optimal policy, and provide operational and managerial insights on the value of the short-term demand forecast and the robustness of the optimal policy.

Key words: Available-to-promise, pseudo orders, Markov, stochastic dynamic programming, prioritization, resource pairing, resource imbalance-based rationing, short-term and long-term forecasts, robustness

Full text in pdf: http://ssrn.com/abstract=1962800

Anderson Graduate School of Management, University of California, Riverside, CA 92521

Zhaolin Li
Faculty of Economics and Business, The University of Sydney, Australia

Supply interruptions often have severe financial and operational consequences for many companies. This paper investigates the effects of contract coordination and information sharing in mitigating such risk.  We consider a one-supplier-one-retailer inventory system, in which the supplier is subject to random interruptions.  We first show that the retailer’s optimal inventory policy is a supply state-dependent (s,S) policy. We then propose a supply chain contract to coordinate the system so that both parties strive to improve chain’s efficiency. We show that neither a disproportional sharing of the system fixed costs nor the end-of-cycle return contracts are sufficient to coordinate the system. Moreover, both contract coordination and information sharing are implementable in many supply chains. When the coordinating contract is in place, sharing supply information is incentive compatible,  and can significantly alleviate the gravity of supply interruption risk by reducing shortage risk and inventory cost. Our research underscores the importance of jointly using appropriate contracts, acquiring and sharing actionable information to coordinate product, financial, and information flows for managing supply interruptions.

Key words: supply interruption, supply chain contract, information sharing, inventory control, Markov chain.

Section 02:

• Course page: http://ilearn.ucr.edu
  
• Office hours: R 3:30 pm –5:30 pm, or by appointment

• Prerequisite:

  • MGT 403, Calculus, linear algebra, and probability

  • MATLAB: We will use MATLAB throughout the course.

  • Required text: Introduction to Probability and Statistics for Engineers and Scientists, by Sheldon M. Ross, 4th edition, Academic Press, 2009

• References:

  • Essentials of Modern Business Statistics, by David Anderson, 3rd Ed., South-Western, 2007

  • Mathematical Statistics and Data Analysis, by John A. Rice, 3rd edition, Duxbury Press, 2006

  • Getting Started with MATLAB, by Rudra Pratap, Oxford University Press, 2010
    

SCHEDULE: Chapters 3-5, 1-2, 6-11

1. WEEK I
   1. Quiz 1 on the prerequisite, open book/notes
   2. Elements of Probability
   3. Reading: Chapter 3
   4. HW1 due at 2pm,  Jan 12 on iLearn
2. WEEK II
   1. Random Variables and Expectation
   2. Reading: Chapter 4, skip 4.3.2, 4.8; Chapter 5, skip 5.6.1, 5.7, 5.9
   3. HW2 due at 2pm, Jan 19 on iLearn
3. WEEK III
   1. Quiz 2 on Chapters 3 and 4, open book/notes
   2. Special Random Variables
   3. Reading: Chapter 5, skip 5.7, 5.9
   4. HW3 due at 2pm, Jan 26 on iLearn
4. WEEK IV
   1. Special Random Variables
   2. Reading: Chapter 5, skip 5.7, 5.9; Chapter 6
   3. HW4 due at 2pm, Feb. 2 on iLearn
5. WEEK V
   1. Quiz 3 on Chapter 5, open book/notes
   2. Distributions of Sampling Statistics; Descriptive Statistics (MATLAB)
   3. Reading: Chapters 6, 1, 2
6. WEEK VI
   1. Extra Office Hours: 3-5pm, Feb. 8
   2. Midterm exam on Chapters 3, 4, 5, open book/notes, 80 minutes
   3. Distributions of Sampling Statistics (MATLAB)
   4. Reading: Chapters 2, 6, skip 2.6, 6.6
7. WEEK VII
   1. Parameter Estimation
   2. Reading: Chapter 7: 7.1, 7.3, 7.4, 7.5
   3. Quiz 4 on Chapters 2 and 6, open book/notes
8. WEEK VIII
   1. Hypothesis Testing
   2. Reading: Chapter 8, skip 8.6, 8.7
9. WEEK IX
   1. Regression
   2. Reading: Chapter 9, skip 9.8, 9.11
   3. Quiz 5 on Chapters 7 and 8, open book/notes (MATLAB)
10. WEEK X
    1. Analysis of Variance
    2. Reading: Chapter 10: 10.1, 10.2, 10.3
11. FINAL WEEK

    1. Extra Office Hours: 10am -12pm, March 18

    2. Final exam, bring blue book and a calculator, open book/notes,  120 minutes, Thursday, March 18 

       1. session 01: 3:00pm – 5:00pm
       2. session 02: 5:30pm – 7:30pm

• Title:
  Matching Supply and Demand in the Presence of Multiclass Suppliers and Customers
  presented at INFORMS, San Diego, October 13, 2009
  
• Presenting Author:
  Long Gao, Assistant Professor, University of California, Riverside, long.gao@ucr.edu
  
• Co-Author:
  Nan Yang, Assistant Professor, Cornell University, ny38@cornell.edu
  
• Abstract:
  We study a multiperiod supplier selection and order acceptance problem in the presence of multiclass suppliers with random yields and random demands from multiclass Customers. We characterize the structure of the optimal policy and investigate the impact of system parameters. We numerically compare the performance of several widely used policies with that of the optimal policy under different scenarios, and provide recommendations for the management.

MGT 239, SIMULATION FOR BUSINESS, CLASS FALL 2009

• Office hours: Thursdays, 1:30pm -3:30pm, Anderson Hall 221
• Course page: http://ilearn.ucr.edu 
• Prerequisite: MGT 201 and basic probability
• Required text: Applied Simulation Modeling, by Seila, Ceric and Tadikamalla, Duxbury Press, 2003
• References:
  • Simulation, by Sheldon M. Ross, Academic Press, 4 edition, 2006
  • MATLAB: A Practical Introduction to Programming and Problem Solving, by Stormy Attaway, Butterworth-Heinemann, 2009
  • Getting Started with MATLAB 7, by Rudra Pratap, Oxford University Press, 2005

SCHEDULE

1. WEEK I
   1. Syllabus and the tentative shedule
   2. Introduction of simulation: system, analytical and simulation model
   3. Simulate darts game
       
      
2. WEEK II
   1. Review basic probability
   2. Random variates generation
   3. MATLAB tutorial
   4. Build Monte Carlo simulations in MATLAB
      Matlab Getting Started Guide, www.mathworks.com, online
   5. Simulate rolling dices game
       
      
3. WEEK III
   1. The inverse transform method
   2. Statistics Toolbox User’s Guide, www.mathworks.com, online

      Generating Random Data 6-2
      Random Number Generation Functions  6-3
      Direct Methods 6-5
      Inversion Methods  6-7

   3. Simulate the effects of forecast updating in a sales promotion model

      

4. WEEK IV

   1. Sensitivity analysis and simulation optimization

   2. Inventory management and newsvendor model

   3. Insights: tradeoff between shortage and overage, matching supply and demand

   4. Simulate a single period inventory system

       

5. WEEK V

   1. Dynamic simulation

   2. Financial models

   3. Exercise 1: simulate a supply chain management problem

       

   4. Exercise 2: price Asian options with geometric random walk stock price model

       

6. WEEK VI

   1. Risk management and financial hedging

   2. Exercise 3: evaluate investment strategies with/without hedging

      

   3. Introduction to Markov chain models

        

7. WEEK VII

   1. Exercise 4: modeling market share, the effectiveness of advertisement and coupon

      

   2. Midterm 1

       
       

8. WEEK VIII

   1. Modeling the spread of an infectious disease

      

   2. Exercise 5: modeling negotiations in show business

      

   3. Introduction to revenue management
   4. Monte Carlo method for computing the optimal protection levels
      

9. WEEK IX

   1. Exercise 6: revenue management

      

   2. Exercise 7: auctions and bidding 

        
      

10. WEEK X

    1. Midterm 2 

       
       

11. WEEK XI

    1. Dress code for project final presentation

    2. Beer time: ‘Supply’ and ‘Demand’ arm wrestle keeping ‘Market’ in balance

       

    3. Project final presentation
       STOCK STOPS: WHEN TO BUY AND SELL FOR PLAIN JOE, Jason Wirka, Eugene Chin, Joe Chin
       VALUE OF INFORMATION IN SUPPLY CHAIN, Yue Cheng, Jia Jia
       BETTING STRATEGIES IN ROULETTE GAME, Ece kaptanoglu, Saurabh Lal, Vishal Kothari

• In simulation we believe if something talks like a duck, walks like a duck, it is a duck.

COURSE SCHEDULE

1. WEEK I
   1. Syllabus and the tentative schedule
   2. Introduction of simulation: system, analytical and simulation model
   3. A review of probability models:
      1. Key concepts: random variable, probability, frequency, histogram, distribution, and CDF ;
      2. distributions: uniform, Bernoulli, Poisson, normal, exponential, triangle, Erlang, Gamma, etc.
      3. stochastic processes: Poisson, renewal, nonstationary arrival processes
2. WEEK II
   1. Monte Carlo Simulation:
      1. Select inputs: uncertain parameters and probability distributions;
      2. Specify outputs measurements;
      3. Run a simulation for replications N (sample size);
      4. Analyze outputs, draw insights and make recommendations;
   2. random variates generation:
      1. building block (RAND( ))
      2. the inverse transform method
      3. continuous (e.g, NORMSINV(RAND()))
      4. discrete cases (e.g., VLOOKUP( ) )
   3. EXCEL modeling:
      sampling and simulation: iid , ,   ;
      HW 2.13: random sample size (N), indicator function   and binary variable (IF(A,1,0));
      For Excel: hit F1 for help, or click menu ‘Help’ for help, or stop by my office…
3. WEEK III
   1. Inventory management and the newsvendor model:
      1. mathematical formulation: let
         ,
      2. analytical solution satisfies
      3. insights: tradeoff between shortage and overage, matching supply and demand
   2. Spreadsheet simulation optimization: MGT239_NW.XLS on iLearn
   3. Next Monday, April 13: watch @RISK tutorial, prepare financial models (chapter 3)
   4. HW2 due on April 15; reading: chapter 3, 10, and push your project.

4. WEEK IV

   1. Risk management and financial hedging:
      1. risk: adverse consequence and prob; variance; downside risk;
      2. derivatives: forward & future (obligation), options (right), e.g., American, European, Asian;
      3. geometric random walk stock price model: 
      4. pricing European call and put options: , , 
      5. hedging a stock with put options;
   2. Exercise 1: simulate stock price processes; distribution; confidence interval; downside risk;
   3. Exercise 2: evaluate investment strategies: stock v.s. stock with put option hedging;
   4. HW3 due on Wed (iLearn, at 9am); proposal due on Wed (iLearn at 9 am);
   5. Midterm I, May 6, week 6; midterm II, May 27, week 9;
   6. Project meeting: April 24, 4-6pm, 221 Anderson Hall
5. WEEK V
   1. Dynamic simulations:
      1. key concepts: state variable , recursion dynamics ;
      2. sample path ;
      3. nonstationary, fixed and variable time advance mechanisms, etc.
   2. Exotic options:
      1. pricing Asian options over T periods: fair price ,  , ,  , 
         
   3. Multi-period inventory management:
      1. , , , 
         

   4. Exercise 3:  pricing Asian put and call options;
   5. Exercise 4:  evaluate multi-period nonstationary inventory systems under base stock policy;
6. WEEK VI
   1. Monday, May 4th:
      1. No lecture or office hours;
      2. Project meeting;
      3. Prepare midterm 1;
   2. Tuesday, May 5th, Office hours for midterm I, 10-12pm
   3. Wednesday, May 6th, Midterm I: open book/notes, bring a functional Excel, 9:40am to 11:30am;
      1. Solution: 1, 2, 3.
7. WEEK VII
   1. Review midterm I
   2. Service systems:
      1. service processes;
      2. customer integration and process strategies;
      3. techniques for improving service productivity;
   3. Queueing theory:
      1. 3 main parts: arrival, waiting line, service;
      2. queueing system structures;
      3. operating characteristics, analytical models and Little’s Law:
         , , , ,  ;
   4. Discrete-Event simulation;
   5. Exercise 5: simulate a bank lobby system with Arena;
8. WEEK VIII

   1. Exercise 6: simulate a barbershop with cost parameters (Arena);

   2. Auctions and bidding:

      1. 1st- and 2nd-price, sealed-bid auctions, Dutch and English auctions;

      2. Optimal bidding strategies for 1st(Ducth) and 2nd(English) price, sealed-bid auctions;
         , ,  , ,

      3. Revenue equivalence theorem: (i) allocation is increasing in ; (ii) zero valuation has zero expected surplus, then
         , 

   3. Exercise 7: optimal bidding strategies and expected revenues;
      1. Solution;
   4. Project interim report due on Wednesday;

   5. HW6 due on Friday;

9. WEEK IX
   1. Review;
   2. Midterm II
      1. open book/notes;
      2. bring a functional PC with Arena and Excel;
      3. 9:40am to 11:40am;
10. WEEK X
    1. Dress code for project final presentation
    2. Monday, project final presentation schedule (submit slides on iLearn at 9am)
       1. An Application of Monte Carlo Simulation in Retirement Investment Planning, Haiying Chen,  Wei Chen, Sunny Yang;
       2. Human Resource Budgeting Tool, Pei-Chen Hsiao, Chih-Yuan Chen;
       3. Modeling Notebook Service Center, Chieh-Yu Wei, Chia-Yen Kao, Hong-Tao Hsieh;
       4. TV Panel Plant Simulation, Chen-Yi Lee, Wan Wen Hung, and Yenming Lu;
       5. Simulated Fair Valuation of Credit Default Swap (CDS), Kai Chung, Sandy Chang, Claudia Lee;
    3. Wednesday, project final presentation schedule (submit slides on iLearn at 9am)
       1. UPS Air Freight Traffic Simulation for Pacific Gateway Cargo Center, Kamarat Jermsirisakpong, Animat Adesanya;
       2. Valuation of MBA, Mung Fei Hung, Zhi Yang, Jingtao Ye;
       3. A Financial Model for an Apartment Building, Hao Troeung;
       4. Simulation for Optimal Capacity to Maximize Profits at JPR Shipping Corporation, Yang Tang, Zuotao Liu, Chih-Yuan Kang;
       5. Baseball Simulation, Tang Liu, Christopher Lossett;
       6. Bank of America Lobby System, Hui-Chung Huang, Phoebe Pahng, Hsiao-En Tsai;
    4. Project final report due on Friday, June 5 (both hard copy and iLearn);
        

2. Section 2

3. We are ready. Well, statistically…

4. Scared? Come on, it’s not an exam!

• Supply chain management
• Inventory-production systems
• Revenue management
• Probability & simulation
  

PUBLICATIONS

• "Managing an  Available-to-Promise  Assembly System with  Dynamic Short-Term Pseudo Order Forecast" 
  Long Gao, Susan Xu, Michael Ball. Management Science, forthcoming, 2011.[pdf]

• "Joint Demand and Capacity Management in a Restaurant System”
  Johye Hwang, Long Gao, Wooseung Jang, European Journal of Operations Research, 207,  465-472, 2010. [pdf]

• "The Effects of Sharing Upstream Information on Product Rollover" 
  Zhaolin Li, Long Gao, Production & Operations Management, Vol. 17, 522-531, 2008. [pdf]

• "Service Performance Analysis and Improvement for a Ticket Queue With Balking Customers"
  Susan Xu, Long Gao, Jihong Ou, Management Science, Vol. 53, 971-990, 2007. [pdf]

• "An Architecture Design and Realization of the Industrial CT Visualization System" 
  Long Gao, Zheng Li, Nuclear Electronics & Detection Technology, Vol.23, 124-128, 2003.

• "Radiation Physics and Integrity Design of Large Missile CT Inspection System"
  Long Gao, Zheng Li, Nuclear Electronics & Detection Technology, Vol.22, 68-71, 2002.

WORKING PAPERS

• "Optimal Load Acceptance Policies for Intermodal Freight Transportation" 
  Long Gao, Michael F. Gorman, Zhaolin Li. [abstract]
  
• "Managing Supply Interruptions with Contract Coordination and Information Sharing" 
  Long Gao, Zhaolin Li. [abstract]
  
• "Optimal Inventory Control with Retail Pre-packs" 
  Michael Freimer, Long Gao, Doug Thomas. [abstract]
  
• “Information Acquisition and Voluntary Disclosure in an Export-Processing System”
  Long Gao, Zhaolin Li. [abstract]

PRESENTATIONS

• “Managing Supply Disruptions: Procurement Diversification, Demand Rationing and Dynamic Forecast,”
  with Nan Yang, M&SOM conference, Ann Arbor, June 27, 2011.
  
• “Managing an Available-to-promise Assembly System with Dynamic Short-Term Pseudo order Frecast,”
  with Susan Xu, Michael Ball, The University of Sydney, June 10, 2011.
  
• “Managing Supply Disruptions: Procurement Diversification, Demand Rationing and Dynamic Forecast,”
  with Nan Yang, POMS conference, Reno, April 29, 2011.
  
• ”Why Does Venture Capital Performance Persist Over Time? Evidence From A Dynamic Simulation,”
  with David Porter, Richard Smith, Economics Department, UC Riverside, March 28, 2011. [ppt] 
  
• "Order Processing in an Available-to-Promise System with Pseudo Orders,”
  Statistics Department, UC Riverside, March 10, 2010 .
  
• "Matching Supply and Demand in the Presence of Multiclass Suppliers and Customers”
  with Nan Yang, INFORMS Annual Meeting, San Diego, October 14, 2009.
  
• "Optimal Load Tendering and Acceptance Policies for Intermodal Freight Transportation”
  with Michael Gorman, M&SOM Conference, Boston, June 30, 2009.
  
• "Optimal Load Tendering and Acceptance Policies for Intermodal Freight Transportation”
  with Michael Gorman, POMS conference, Orlando, May 3, 2009. [pdf]
  
• "Yield Management in Freight Transportation",
  with Michael Gorman, INFORMS, Washington DC, October, 2008. [pdf]
  
• "An Available-to-Promise Production-Inventory System with Pseudo Orders",
  with Susan Xu, MSOM, University of Maryland, June, 2008. [pdf]
  
• "An Available-to-Promise Production-Inventory System with Pseudo Orders",
  with Susan Xu, Tsinghua University, Beijing, China, May 29, 2008.
  
• "An Available-to-Promise Production-Inventory System with Pseudo Orders",
  with Susan Xu, INFORMS Annual Meeting, Seattle, November 6, 2007.
  
• "The Effects of Sharing Upstream Information on Product Rollover",
  with Zhaolin Li, INFORMS Annual Meeting, Seattle, November 6, 2007. [ppt] 
  
• "Optimal Inventory Control Using Retail Pre-packs"
  with Michael Freimer, Doug Thomas, Purdue University, 2007. [pdf]
  
• "Service Performance Analysis and Improvement for a Ticket Queue With Balking Customers"
  with Susan Xu and Jihong Ou, MSOM Conference, Atlanta, June 19-20, 2006. [pdf]
  
• "Service Performance Analysis and Improvement for a Ticket Queue With Balking Customers"
  with Susan Xu and Jihong Ou, 13th INFORMS Applied Probability Conference, Ottawa, Canada, July 6-8, 2005.

 
Photoed in AGSM, Feb 2009

• 221 AGSM, UC Riverside, CA 92521
• Phone: 951-827-5284
• Email: long.gao@ucr.edu
• URL: http://agsm.ucr.edu/directory/faculty.html?netid=longg
• CV [pdf]

EDUCATION

• Ph.D. in Business Administration & Operations Research, 2002 – 2007
  The Pennsylvania State University

• M.E. in Engineering Physics, 2000 – 2002
  Tsinghua University, China

• B.E. in Engineering Physics, 1996 – 2000
  Tsinghua University, China
  

RESEARCH INTERESTS

• Supply chain management
• Inventory-production systems
• Revenue management
• Probability & simulation

ACADEMIC POSITIONS

• Dept. of Finance and Management Science, UC Riverside
  Assistant Professor 2008.7 – Present

• Dept. of MIS, OM & Decision Sciences, University of Dayton
  Assistant Professor 2007.8 – 2008.5

• Dept. of Supply Chain & Information Systems, Penn State University
  Research Assistant 2003.8 – 2007.7

PUBLICATIONS

• "Managing an  Available-to-Promise  Assembly System with  Dynamic Short-Term Pseudo Order Forecast" 
  Long Gao, Susan Xu, Michael Ball. Management Science, forthcoming, 2011.[pdf]

• "Joint Demand and Capacity Management in a Restaurant System”
  Johye Hwang, Long Gao, Wooseung Jang, European Journal of Operations Research, 207,  465-472, 2010. [pdf]

• "The Effects of Sharing Upstream Information on Product Rollover" 
  Zhaolin Li, Long Gao, Production & Operations Management, Vol. 17, 522-531, 2008. [pdf]

• "Service Performance Analysis and Improvement for a Ticket Queue With Balking Customers"
  Susan Xu, Long Gao, Jihong Ou, Management Science, Vol. 53, 971-990, 2007. [pdf]

• "An Architecture Design and Realization of the Industrial CT Visualization System" 
  Long Gao, Zheng Li, Nuclear Electronics & Detection Technology, Vol.23, 124-128, 2003.

• "Radiation Physics and Integrity Design of Large Missile CT Inspection System"
  Long Gao, Zheng Li, Nuclear Electronics & Detection Technology, Vol.22, 68-71, 2002.

WORKING PAPERS

• "Optimal Load Acceptance Policies for Intermodal Freight Transportation" 
  Long Gao, Michael F. Gorman, Zhaolin Li. [abstract]
  
• "Managing Supply Interruptions with Contract Coordination and Information Sharing" 
  Long Gao, Zhaolin Li. [abstract]
  
• "Optimal Inventory Control with Retail Pre-packs" 
  Michael Freimer, Long Gao, Doug Thomas. [abstract]
  
• “Information Acquisition and Voluntary Disclosure in an Export-Processing System”
  Long Gao, Zhaolin Li. [abstract]

PRESENTATIONS

• “Managing Supply Disruptions: Procurement Diversification, Demand Rationing and Dynamic Forecast,”
  with Nan Yang, M&SOM conference, Ann Arbor, June 27, 2011.
  
• “Managing an Available-to-promise Assembly System with Dynamic Short-Term Pseudo order Frecast,”
  with Susan Xu, Michael Ball, The University of Sydney, June 10, 2011.
  
• “Managing Supply Disruptions: Procurement Diversification, Demand Rationing and Dynamic Forecast,”
  with Nan Yang, POMS conference, Reno, April 29, 2011.
  
• ”Why Does Venture Capital Performance Persist Over Time? Evidence From A Dynamic Simulation,”
  with David Porter, Richard Smith, Economics Department, UC Riverside, March 28, 2011.[ppt]
  
• "Order Processing in an Available-to-Promise System with Pseudo Orders,”
  Statistics Department, UC Riverside, March 10, 2010 .
  
• "Matching Supply and Demand in the Presence of Multiclass Suppliers and Customers”
  with Nan Yang, INFORMS Annual Meeting, San Diego, October 14, 2009.
  
• "Optimal Load Tendering and Acceptance Policies for Intermodal Freight Transportation”
  with Michael Gorman, M&SOM Conference, Boston, June 30, 2009.
  
• "Optimal Load Tendering and Acceptance Policies for Intermodal Freight Transportation”
  with Michael Gorman, POMS conference, Orlando, May 3, 2009. [pdf]
  
• "Yield Management in Freight Transportation",
  with Michael Gorman, INFORMS, Washington DC, October, 2008. [pdf]
  
• "An Available-to-Promise Production-Inventory System with Pseudo Orders",
  with Susan Xu, MSOM, University of Maryland, June, 2008. [pdf]
  
• "An Available-to-Promise Production-Inventory System with Pseudo Orders",
  with Susan Xu, Tsinghua University, Beijing, China, May 29, 2008.
  
• "An Available-to-Promise Production-Inventory System with Pseudo Orders",
  with Susan Xu, INFORMS Annual Meeting, Seattle, November 6, 2007.
  
• "The Effects of Sharing Upstream Information on Product Rollover",
  with Zhaolin Li, INFORMS Annual Meeting, Seattle, November 6, 2007. [ppt]
  
• "Optimal Inventory Control Using Retail Pre-packs"
  with Michael Freimer, Doug Thomas, Purdue University, 2007. [pdf]
  
• "Service Performance Analysis and Improvement for a Ticket Queue With Balking Customers"
  with Susan Xu and Jihong Ou, MSOM Conference, Atlanta, June 19-20, 2006. [pdf]
  
• "Service Performance Analysis and Improvement for a Ticket Queue With Balking Customers"
  with Susan Xu and Jihong Ou, 13th INFORMS Applied Probability Conference, Ottawa, Canada, July 6-8, 2005.

TEACHING

• University of California, Riverside

  MGT 201: Quantitative Analysis (MBA core), Winter 2009, Winter 2010, Winter 2011
  MGT 239: Simulation for Business (MBA elective), Spring 2009, Fall 2009 , Fall 2010
  MGT 290: Directed Studies (MBA elective), Winter 2010, Spring 2010, Winter 2011
  BUS 125: Simulation for Business, Fall 2010

• University of Dayton
  DSC210: Statistics for Business I, Fall 2007, Spring 2008

• Penn State University
  Lecturer – Introduction to Statistics for Business, 2005
  Teaching Assistant – Demand Fulfillment (MBA), 2006
  Teaching Assistant – Introduction to Statistics for Business, 2004 – 2006 

SERVICES AND PROFESSIONAL ACTIVITIES

• Member, The Institute of Operations Research and Management Sciences (INFORMS)
• Member, Manufacturing and Service Operations Management Society (M&SOM)
• Member, Applied Probability Society (APS)
• Vice president, Penn State University INFORMS student chapter 2005 – 2007
• Referee, Productions & Operations Management, Naval Research Logistics, IIE Transactions

1. Section 1

2. Section 2

1. Section 1

2. Section 2