Nima Zaerpour
Nima Zaerpour
Assistant Professor of Operations and Supply Chain Management

Office: Markstein Hall 446
Phone: 760-750-4272
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Dr. Zaerpour is an assistant professor of Operations and Supply Chain Management at the College of Business Administration, California State University San Marcos. He has received his PhD in Operations Management from Rotterdam School of Management, Erasmus University, the Netherlands in 2013. Prior to joining California State University, Dr. Zaerpour was an assistant professor at VU University Amsterdam. In 2012, he was a visiting scholar at the School of Industrial and Systems Engineering, Georgia Tech. Also, in 2011, he was a visiting scholar at the School of Management, University of Science and Technology of China. His research has been published in leading journals among others are Production and Operations Management and Transportation Science. His research interests are Facility Logistics Management, Distribution Logistics Management, Supply Chain Management, and Terminal Operations Management; in particular studying recent innovations in these areas. He serves as a reviewer for top tier journals such as Operations Research, Production and Operations Management, Transportation Science, and Interfaces. He has developed Warehousing Decision Support tools and has served as consultant for Supply Chain and Logistics companies.


Ph.D., Operations Management

  • Rotterdam School of Management, Erasmus University, Rotterdam, the Netherlands, 2013

M.S., Industrial Engineering

  • University of Tehran, Tehran, Iran, 2008

B.S., Industrial Engineering

  • Sharif University of Technology, Tehran, Iran, 2005

Research Interests

  • Facility Logistics Management
  • Distribution Logistics Management
  • Supply Chain Management
  • Terminal Operations Management

Papers in Referred Journals

  • Zaerpour, N., Y. Yu, R. de Koster (2016) “Small is Beautiful: A Framework for Evaluating and Optimizing Live-cube Compact Storage Systems”. Transportation Science,
    http://dx.doi.org/10.1287/trsc.2015.0586.
  • Zaerpour, N., Y. Yu, R. de Koster (2015). “Storing Fresh Produce for Fast Retrieval in an Automated Compact Cross-dock System”. Production and Operations Management 24 (8) 1266–1284.
  • Zaerpour, N., Y. Yu, R. de Koster (2013) “Storage Policies and Optimal Shape of a Storage System”, International Journal of Production Research 51(23-24) 6891-6899.
  • De Koster, R., T. Le-Duc, N. Zaerpour (2012) “Determining the Number of Zones in a Pick-and-sort Order Picking System”,International Journal of Production Research 50(3) 757-771.
  • Zaerpour, N., M. Rabbani, A.H. Gharehgozli, R. Tavakkoli-Moghaddam, (2009) “A comprehensive decision making structure for partitioning of Make-To-Order, Make-To-Stock and Hybrid products”, Soft Computing 13(11) 1035-1054.
  • Gharehgozli, A.H., R. Tavakkoli-Moghaddam, N. Zaerpour, (2009) “A fuzzy-mixed-integer goal programming model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates”, Robotics and Computer-Integrated Manufacturing 25(4-5) 853-859.
  • Zaerpour, N., M. Rabbani, A.H. Gharehgozli, R. Tavakkoli-Moghaddam (2008) “Make-To-Order or Make-To-Stock decision by a novel hybrid approach”,Advanced Engineering Informatics 22(2) 186-201.
  • Gharehgozli, A.H., M. Rabbani, N. Zaerpour, J. Razmi, (2008) “A comprehensive decision making structure for acceptance/rejection of incoming orders in Make-To-Order environments”, International Journal of Advanced Manufacturing Technology 39(9-10) 1016-1032.

Books

  • Rabbani, M., A.Bahredar, A.H. Gharehgozli, N. Zaerpour, N. Manavi Zadeh, Productivity Engineering and Management, ISBN: 978-964-2936-22-9, Iranian Association of Railway Transportation Engineering, Tehran 2007, in Persian.

BUS 324: Introduction to Business Analytics

This course introduces the methods and tools which help to extract not only information but also insights from the data in various business functions such as operations, supply chain, marketing, and finance. The course consists of four distinct parts:

  • Foundations of Business Analytics
  • Descriptive Analytics: descriptive statistics, data visualization, statistical inference
  • Predictive Analytics: regression, time series analysis, forecasting, data mining, spreadsheet modeling
  • Prescriptive Analytics: linear optimization, integer optimization, simulation, decision analysis

BUS 332: Introduction to Data Analytics

This course introduces the methods and tools which help to extract not only information but also insights from the data in various business functions such as operations, supply chain, marketing, and finance. The course consists of three distinct parts:

  • Foundations of Data Analytics
  • Descriptive Analytics: descriptive statistics, data visualization, statistical inference
  • Predictive Analytics: regression, data mining

Journal Referee

  • Operations Research
  • Production and Operations Management
  • Transportation Science
  • Transportation Research-Part E
  • International Journal of Production Economics
  • International Journal of Production Research
  • Omega