Amir Meimand

*** ****** ****** *****, *** #***, Seattle, WA 98109, 814-***-****, ab6v60@r.postjobfree.com

Education

PhD Industrial Engineering with dual degree in Operations Research Aug 2009–April 2013

The Pennsylvania State University

Thesis Title: Differential Stackelberg Games and Their Application to Supply Chain Optimization, Production

Planning, Network Design, and Logistics.

MASc Industrial Engineering, Tarbiat Modares University, Tehra, Iran Aug 2006–May 2008

BSc Industrial Engineering, Amirkabir University, Tehra, Iran Aug 2001–May 2005

Skills

Optimization and Operations Research: Linear, Nonlinear, Integer, and Dynamic Programming,

Stochastic Process, Optimal Control.

Statistic: Analysis of Variance, Linear Regression, Data Mining, Clustering.

Supply Chain: Network Optimization, Demand Modeling, Inventory Optimization, and Dynamic

Pricing

Computer Skills: FICO Xpress, CPLEX, GMAS, C++, Perl, SQL, R, Matlab

Industry Experience

Operations Research Scientist March 2013 – Present

Amazon.com, Seattle, WA

• Transportation Network Design.

• Transportation Routing Optimization.

• Forecasting.

Co-Op, Data Analyst May 2010 – Dec 2010

NextEra Energy Company, Juno Beach, FL

• Developed a mathematical model for the electrical supply distribution network by linear / integer programming

techniques to optimize the total number of service centers keeping the desired level of reliability.

• Employed advance statistical data analysis techniques to estimate the parameters for the model.

Programmed the model by GAMS/Cplex.

Research Experience

Freight Transport, Logistic and Network Design Jan 2011 – Jun 2011

Research Assistant, Pennsylvania State University, University Park, PA

• Developed a dynamic model to study the interaction between a carrier as the leader and suppliers as

followers who are involving in an oligopolistic competition in a transportation network.

• Employed advanced dynamic game methods to find the best transportation pricing policy of carrier as

well as shipping pattern and demand allocation rate for the suppliers. (GAMS+Matlab)

Strategic Dynamic Pricing and Production Planning Jun 2011-April 2013

Research Assistant, Pennsylvania State University, University Park, PA

• Developed a stochastic dynamic model to study the interaction between supplier and retailers in a supply

chain network when the markets demand is stochastic.

• Employed advanced stochastic differential game techniques to find the optimum pricing policy and

production planning according to the degree of stochasticity. (GAMS+Matlab)

Leadership

President, Iranian Student Association at Pennsylvania State University: Managed cultural activities and

wrote proposals to university administrations and raised money through outside sponsors to provide funding for

club activities

Book Chapters

• Terry L. Friesz, Amir Meimand, Bo Zhang (2013), Dynamic optimization and differential games

as tools for logistic and freight network modeling, In M. Ben-Akiva, H. Meersman and E. Van de

Voorde (Eds), Freight Transport Modeling, pp. 91-118.

• Amir Meimand, Terry L. Friesz (2013), Computable models of static and dynamic spatial

oligopoly, In Fisher M. M., Nijkamp P. (Eds) Handbook of Regional and Science (In Press)

Journal Papers

• H. Qahri Saremi, Babak Abedi, Amir Meimand (2008), Website structure improvement: Quadratic

assignment problem approach and ant colony meta-heuristic technique, Applied Mathematics and

Computation Journal, 195(1), pp. 285-298.

• S.K. Chaharsooghi, Amir Meimand (2008), An effective ant colony optimization algorithm (ACO) for

multi-objective resource allocation problem (MORAP), Applied Mathematics and Computation Journal,

200 (1), pp. 167-177

• Terry L. Friesz, Ke Han, Amir Meimand, Pedro Neto, and Tao Yao (2013), “Dynamic user

equilibrium based on a hydrodynamic model,” Transportation Research B, 47, pp. 102-126.

• T. L. Friesz and A. Meimand (2013), “Dynamic user equilibria with elastic demand,”

Transportmetrica, DOI:10.1080/18128692.2012.751684

• Amir Meimand, Terry L. Friesz, Stochastic dynamic stackelberg game for oligopoly competition

with sticky price: A computational approach, working paper.

• Amir Meimand, Terry L. Friesz, Alberto Berssan, Existence and uniqueness of stochastic

stackelberg equilibrium: A varitional approach, working paper.

Selected Conference Papers:

• Amir Meimand, S.K. Chaharsooghi, Application of mathematical modeling and multi-objective

optimization on the project management process (Case study of oil and gas project), EU/MEeting

Metaheuristics in the Service Industry, October 2007 Stuttgart, Germany.

• Amir Meimand, S.K. Chaharsooghi, An intelligent multi-colony multiobjective ant colony

optimization (ACO) for the 0-1 knapsack and QAP problem, IEEE 2008 Congress on Evolutionary

Computation, June 2008, Hong Kong, China.

• Terry L .Friesz, Ke Han, Amir Meimand, Pedro Neto, Tao Yao, Dynamic user equilibrium based on

the hydrodynamic model, Transportation Research Board 2012 Annual Meeting, January 2012,

Washington D.C.

• Terry L. Friesz, Pedro Neto, Amir Meimand, Fixed point methods for computing within-day dynamic

user equilibrium, 14th International IEEE Conference on Intelligent Transportation Systems, October 5-

7, 2011, Washington D.C.

• Toa Yao, Terry L. Friesz, Bo Zhang, Amir Meimand, A shipper-carrier freight transshipment

network model: A stackelberg game-theoretic approach, INFORMS annual meeting, October 14-17

2012, Phoenix, AZ

• Amir Meimand, Terry Friesz, Terry L. Friesz, Pricing policy in a stackelberg game on supply chain

network with imperfect information, INFORMS annual meeting, October 14-17 2012, Phoenix, AZ

Graduate Level Course

• Optimization: Dynamic Programming, Nonlinear Programming, Linear Programming, Integer

Programming, Dynamic Optimization and Differential Games, Nonlinear Networks and the Price of

Anarchy

• Statistics: Stochastic Process, Regression Model

• Numerical Analysis: Numerical Analysis for Partial Differential Equations.

• Application Courses: Discrete Event Simulation, Operation Research on Supply Chain Management

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