Mohammad Moshref Javadi

Mohammad Moshref Javadi

Teaching Assistant Professor of Business Administration

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Educational Background

  • Ph.D., Industrial Engineering, Purdue University, 2017

Positions Held

  • Teaching Assistant Professor, Business Administration, University of Illinois at Urbana-Champaign, 2021 to present
  • Visiting Assistant Professor, D'amore-McKim School of Business, Northeastern University, 2019-2021
  • Postdoctoral Associate, Center for Transportation and Logistics, Massachusetts Institute of Technology, 2017-2019

Recent Publications

  • Moshref Javadi, M. (2022). A logic-based Benders decomposition method for the multi-trip traveling repairman problem with drones. Computers & Operations Research, 145 105845.
  • Moshref Javadi, M., Hemmati, ., & Winkenbach, . (2021). A Comparative Analysis of Synchronized Truck-and-Drone Delivery Models. Computers & Industrial Engineering, 162 107648.
  • Moshref Javadi, M., & Winkenbach, M. (2021). Applications and Research Avenues for Drone-Based Models in Logistics: A Classification and Review. Expert Systems with Applications, 177 114854.
  • Moshref Javadi, M., Hemmati, A., & Winkenbach, M. (2020). A Truck and Drones Model for Last-mile Delivery: A Mathematical Model and Heuristic Approach. Applied Mathematical Modeling, 80 290-318.
  • Moshref Javadi, M., Lee, S., & Winkenbach, M. (2020). Design and Evaluation of a Multi-Trip Delivery Model with Truck and Drones. Transportation Research Part E: Logistics and Transportation Review, 136 101887.
  • Kitjacharoenchai, P., Ventresca, M., Moshref Javadi, M., Lee, S., Tanchoco, J., & Brunese, P. (2019). Multiple Traveling Salesman Problem with Drones: Mathematical model and heuristic approach. Computers & Industrial Engineering, 129 14-30.

Other Publications


  • Sabouhi, F., Bozorgi-Amiri, A., Moshref Javadi, M., & Heydari, M. (2018). An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: A case study. Annals of Operations Research, 283 643-677.
  • Moshref Javadi, M., & Lee, S. (2016). The Customer-centric, Multi-commodity Vehicle Routing Problem with Split Delivery. Expert Systems with Applications, 56 335-348.
  • Moshref Javadi, M., & Lee, S. (2016). The Latency Location-Routing Problem. European Journal of Operational Research, 255 (2).
  • Moshref Javadi, M., & Lehto, M. (2016). Material Handling Improvement in Warehouses by Parts Clustering. International Journal of Production Research, 54 (14), 4256-4271.
  • Jabal-Ameli, M., & Moshref Javadi, M. (2013). Concurrent Cell Formation and Layout Design Using Scatter Search. International Journal of Advanced Manufacturing Technology, 71 1-22.
  • Jabal-Ameli, M., Moshref Javadi, M., Bankian-Tabrizi, B., & Mohammadi, M. (2013). Cell Formation and Layout Design with Alternative Routing: A Multi-Objective Scatter Search Approach. International Journal of Industrial and Systems Engineering, 14 (3), 269-295.
  • Jabal-Ameli, M., Bankian-Tabrizi, B., & Moshref Javadi, M. (2011). A Simulated Annealing method to solve a generalized maximal covering location problem. International Journal of Industrial Engineering Computations, 2 (2), 439-448.
  • Moshref Javadi, M., Moqri, M., & Yazdian, A. (2011). Supplier selection and order lot sizing using dynamic programming. International Journal of Industrial Engineering Computations, 2 (2), 319-328.


  • Moshref Javadi, M., Hemmati, A., & Winkenbach, M. (2020). Synchronizing truck and drones for last-mile delivery operations. INFORMS Annual Meeting.
  • Moshref Javadi, M., Hemmati, A., & Winkenbach, M. (2018). A Multi-Modal Drone Delivery System for Urban Logistics. INFORMS Annual Meeting.
  • Moshref Javadi, M., Hemmati, A., & Winkenbach, M. (2018). A Heuristic Method for a Customer-centric Drone Delivery System. IISE Conference.
  • Moshref Javadi, M., Lee, S., & Winkenbach, . (2017). The Customer-Centric Drone Delivery System. INFORMS Annual Meeting.
  • Moshref Javadi, M., & Lee, S. (2015). A Memetic Algorithm to Minimize Latency in Location Routing Problems. INFORMS Annual Meeting.
  • Moshref Javadi, M., & Lehto, M. (2014). Material handling improvement by parts clustering. INFORMS Annual Meeting.

Honors and Awards

  • Judith Liebman Outstanding Service Award, INFORMS, 2017 to present
  • Best paper award in Supply Chain and Logistics, Institute for Industrial and Systems Engineers, 2016 to present
  • Outstanding Graduate Research Award, College of Engineering, Purdue University, 2016 to present
  • Tompkins International Honor Scholarship, Material Handling Education Foundation Inc., 2015 to present


  • Reviewer, Production and Operations Management, 2018 to present
  • Reviewer, Transportation Science, 2018 to present
  • Reviewer, Transportation Research Part C: Emerging Technologies, 2018 to present
  • Reviewer, Networks, 2017 to present
  • Reviewer, Transportation Research Part E: Logistics and Transportation Review, 2017 to present
  • Reviewer, IEEE Transactions on Intelligent Transportation Systems, 2017 to present
  • Reviewer, European Journal of Operational Research, 2017 to present
  • Reviewer, Journal of the Operational Research Society, 2017 to present
  • Reviewer, Annals of Operations Research, 2017 to present
  • Session Chair for "Emerging Logistics Models", INFORMS, 2021-2021
  • Session chair for "Autonomous Robots in Delivery Logistics", International Federation for Information Processing (IFIP), 2021-2021
  • Session chair for "Last-mile logistics optimization", INFORMS, 2020-2020
  • Session chair for "Drone for Smart Logistics", INFORMS, 2019-2019

Teaching Interests

Business Analytics, Data Analytics, Decision and Management Sciences, Operations Management, Logistics Management, Supply Chain Management, Optimization, Simulation

Research Interests

Applied operations research, data-driven decision making, and mathematical modeling with applications to production systems, supply chain, and disaster relief operations.

Current Courses

  • Business Analytics II (BADM 211) Builds on the foundation from the Business Analytics I (BADM 210), synthesizes concepts through hands-on application and project-based learning. Focuses on data acquisition, organization, analysis and visualization in a business setting. Expanding on the use of statistics in generating basic inferences to predictive modeling Identify opportunities for improving business decisions using data, conduct relevant analysis of the gathered and cleaned data, and finally, interpret and present analysis outcomes to decision makers. Using statistical tools and software applications to identify business problems, acquire relevant data, and generate analytic solutions using advanced analytics techniques and tools for generating insights. Introduces the students to analyzing, learning, and prediction using advanced analytics techniques and tools for generating business insights. This course will provide a practical introduction to various techniques regarding clustering, text mining, classification and decision trees, and time series analysis. Finally, the course will introduce advanced and emerging topics in predictive analytics.

  • Operations Management (BADM 275) Operations Management is about developing, producing, and delivering goods and services that meet and exceed customer expectations. In this course, students will be introduced to decision making frameworks and techniques for effectively and efficiently managing operations through coordinated efforts across different organizations in a supply chain and across multiple areas within an organization. These multiple areas could be consumer analytics, research and development, finance, engineering, marketing, human resource management, sourcing, information systems, logistics, and accounting.

  • Operations Strategy (BADM 375) Students will learn how organizations can gain and sustain competitive advantage through their operations capabilities. The course content will cover manufacturing and service contexts across industries such as airline, consulting, entertainment, healthcare, hospitality, information technology, and retail. Cases and examples will be used to explore technologies such as blockchain and internet of things (IOT), issues such as supply chain risk and social responsibility, and business models such as alliances and sharing economy.

  • Business Process Modeling (BADM 460) Introduces the identification and analysis of various aspects of business processes. The course defines business processes and provides tools for designing and analyzing them.

  • Process Management (BADM 567) Introductory course in decision-making problems in production; includes the theoretical foundations for production management as well as the applications of decision-making techniques to production problems in the firm; and considers production processes, plant layout, maintenance, scheduling, quality control, and production control in particular.

  • Supply Chain Analytics (BADM 575) The objective of the course is to introduce students to using data analytics for improving decision making in supply chains. With Globalization and digitization of supply chains a large volume of data is getting generated within supply chains. Being able to use the information in the data to improve supply chain functioning is critical to success for many organizations. In this course, students are introduced to data analytic methods such as statistical modeling and machine learning methods for organization, and analysis of large volume of different kind of data that relate to specific aspects of managing and organizing supply chain. This course follows a project based practical learning approach. The course is divided into several modules and students are required to analyze and present data and inferences with respect to these modules. 4 graduate hours. No professional credit. Credit is not given for BADM 575 and BADM 590 (31474) Section SCA.