Canan Gunes Corlu
Associate Professor, Administrative Sciences Coordinator, Supply Chain Management Codirector, Decision Sciences Research Laboratory
Dr. Canan Gunes Corlu is an associate professor and the faculty coordinator for the Supply Chain Management programs at BU’s Metropolitan College, and serves as codirector of BU MET’s Decision Sciences Research Laboratory. She has both face-to-face and online teaching experience, with courses in operations management and supply chain management. Dr. Corlu’s primary research interest is in the area of design and analysis of stochastic simulations under input uncertainty and applications of data analytics and simulation-based optimization in operations management and supply chain management, including computational transportation and logistics. She also utilizes simulation to address problems in manufacturing and service supply chains. Lately, she has been investigating emerging risks, including disruption risk and climate change risk, and their impact on decision-making in supply chains. Her research has appeared in a variety of journals, including Operations Research, IISE Transactions, Journal of Simulation, and International Journal of Production Research. Her work on the representation of input risk on inventory simulations was recognized by the INFORMS Minority Issues Forum Best Paper Competition in 2017 and 2018.
Dr. Corlu is the recipient of the INFORMS 2021 Volunteer Service Award and 2009 INFORMS Simulation Society Committee for Underrepresented Minorities and Women (CUMW) award. She is currently serving as INFORMS Simulation Society’s (I-Sim) treasurer. She was I-Sim’s communications editor from 2018 to 2020. She served as the treasurer of the INFORMS Junior Faculty Interest Group (JFIG) from 2016 to 2020. She has been a program committee member for the INFORMS Winter Simulation Conference Logistics, Supply Chain Management, and Transportation track, as well as Modeling Methodology track since 2011. She cochaired the Model Uncertainty and Robust Simulation track in 2020 and 2021. She is an associate editor for the Journal of Simulation and an editorial board member of the Journal of Business Analytics.
View Dr. Corlu’s Curriculum Vitae 2021.
View Dr. Corlu’s Google Scholar Page.
View the Decision Sciences Research Laboratory Website.
Research Interests
- Methodology: Business Analytics, Bayesian Statistics, Computer Simulation, Simulation Optimization, Uncertainty Modeling, High Dimensional Dependence Modeling
- Applications: Inventory Management, Transportation and Logistics, Manufacturing and Service Supply Chains, Supply Chain Risk and Resilience, Finance
Courses
- MET AD 510 – Mathematics & Statistics in Management
- MET AD 605 – Operations Management: Business Process Fundamentals
- MET AD 616 – Enterprise Risk Analytics
- MET AD 680 – Global Supply Chains
- MET AD 804 – Capstone Project for Supply Chain Management
Scholarly Works
Peer-Reviewed Journal Articles
De la Torre, R., C. G. Corlu, J. Faulin, S. Onggo, and A. A. Juan. “Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications.” Sustainability 13, no. 3 (2021): 1551.
De la Torre, R., S. Onggo, C. G. Corlu, and A. A. Juan. “The role of simulation and serious games in teaching concepts on circular economy and sustainable energy.” Invited paper in SI Toward the Circular Economy in the Energy Sector: The Role of Higher Education. Energies 14, no. 4 (2021): 1138.
Martins, L., R. de la Torre, C. G. Corlu, A. A. Juan, and M. Masmoudi. “Ride-sharing in smart sustainable cities: Review, open challenges, and the need for agile optimisation.” Computers & Industrial Engineering 153, article 107080 (2020).
Corlu, C. G., A. Goyal, D. Lopez-Lopez, R. de la Torre, and A. A. Juan. “Ranking enterprise reputation in the digital age: a survey of traditional methods and the need for more agile approaches.” International Journal of Data Analysis Techniques and Strategies (2020, forthcoming).
Corlu, C. G., A. Akcay, and W. Xie. “Stochastic simulation under input uncertainty: A review.” Operations Research Perspectives 7, article 100162 (2020).
Onggo, B. S., C. G. Corlu, A. A. Juan, T. Monks, and R. de la Torre. “Combining enterprise data storage systems with symbiotic simulation systems for real-time decision-making.” Enterprise Information Systems 15, no. 2 (2020): 230–247.
Corlu, C. G., R. de la Torre, A. Serrano-Hernandez, A. A. Juan, and J. Faulin. “Optimizing energy consumption in transportation: Literature review, insights, and research opportunities.” Energies 13, no. 5 (2020): 1115.
Maleyeff, J., and C. G. Corlu. “Stickley Adhesives.” INFORMS Transactions on Education 21, no. 2 (2020): 101–107.
Juan, A. A., C. G. Corlu, R. F. Tordecialla, R. de la Torre, and A. Ferrer. “On the use of biased-randomized algorithms for solving non-smooth optimization problems.” Algorithms 13, no. 1 (2020): 8.
Onggo, B. S., J. Panadero, C. G. Corlu, and A. A. Juan. “Agri-food supply chains with stochastic demands: A multi-period inventory routing problem with perishable products.” Simulation Modeling Practice and Theory 97, article 101970 (2019).
Corlu, C. G., B. Biller, and S. Tayur. “Driving inventory system simulations with limited demand data: Insights from the newsvendor problem.” Journal of Simulation 13, no. 2 (2018): 152–162.
Corlu, C. G., B. Biller, and S. Tayur. “Demand fulfillment probability in a multi-item inventory system with limited historical data.” IISE Transactions 49, no. 12 (2017): 1087–1100. Finalist for INFORMS 2018 MIF (Minority Issues Forum) Best Paper Competition.
Akcay, A., and C. G. Corlu. “Simulation of inventory systems with unknown input models: A data-driven approach.” International Journal of Production Research 55, no. 19 (2017): 5826–5840. DOI: 10.1080/00207543.2017.1343503. Finalist for INFORMS 2017 MIF (Minority Issues Forum) Best Paper Competition.
Corlu, C. G., M. Meterelliyoz, and M. Tinic. “Empirical distributions of daily equity index returns: A comparison.” Expert Systems with Applications 54 (2016): 170–192.
Corlu, C. G., and A. Corlu. “Modeling exchange rate returns: Which flexible distribution to use?” Quantitative Finance 15, no. 11 (2015): 1851–1864.
Corlu, C. G., and M. Meterelliyoz. “Estimating the parameters of the Generalized Lambda Distribution: Which method performs best?” Communications in Statistics: Simulation and Computation 45, no. 7 (2014): 2276–2296.
Biller, B., and C. G. Corlu. “Copula-based multivariate input modeling.” Surveys in Operations Research and Management Science (incorporated into Computers & Operations Research) 17, no. 2 (2012): 69–84.
Biller, B., and C. G. Corlu. “Accounting for parameter uncertainty in large-scale stochastic simulations with correlated inputs.” Operations Research 59, no. 3 (2011): 661–673.
Peer-Reviewed Conference Proceedings
Martins, L., M. Torres, E. Perez, C. G. Corlu, A. Juan, and J. Faulin. “Solving an urban ridesharing problem with stochastic traveling times: A simheuristic approach.” In Proceedings of the 2021 Winter Simulation Conference (to appear 2021).
Lu, Danqi, J. Maleyeff, and C. G. Corlu. “Knee optimization for queuing systems: A customized approach.” In Proceedings of the 51st Annual Conference of the Decision Support Institute (to appear 2021).
Corlu, C. G., J. Maleyeff, C. Yang, T. Ma, and Y. Shen. “Decision support system with simulation-based optimization for healthcare capacity planning.” In Proceedings of the 2021 Simulation Workshop of the Operational Research Society (2020): 277–286.
Maleyeff, J., C. G. Corlu, and X. Wang. “Simulation metamodeling to support hospital capacity planning.” Poster abstract in Proceedings of the 2020 Winter Simulation Conference (2020).
Maleyeff, J., D. Lu, and C. G. Corlu. “Using lean to improve the customer experience in call centers: A meta-analysis approach.” In Proceedings of the 51st Annual Conference of the Decision Sciences Institute (November 2020): 256–273.
Corlu, C. G., J. Panadero, B. B. Onggo, and A. A. Juan. “On the scarcity of observations when modeling random inputs and the quality of solutions to stochastic optimization problems.” In Proceedings of the 2020 Winter Simulation Conference, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2020): 2105–2113.
Corlu, C. G., J. Maleyeff, J. Yang, K. Yip, and J. Farris. “Real-time nurse dispatching using dynamic priority decision framework.”: In Proceedings of the 2020 Winter Simulation Conference, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2020): 782–793.
Ghorpade, T., and C. G. Corlu. “Selective pick-up and delivery traveling salesman problem: A simheuristics approach.” In Proceedings of the 2020 Winter Simulation Conference, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2020): 1468–1479.
Doddavaram, R., and C. G. Corlu. “Teaching risk analytics using R.” In Proceedings of the 2020 Winter Simulation Conference, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2020): 3272–3281.
Corlu, C. G., B. Biller, E. Wolf, and E. Yucesan. “Inventory management with disruption risk.” In Proceedings of the 2020 Winter Simulation Conference, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2020): 2625–2636.
Maleyeff, J., and C. G. Corlu. “Monte Carlo simulations to teach the effect of lean methods to improve business processes.” In Proceedings of the 2019 Winter Simulation Conference, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2019): 3356–3367.
Onggo, B. S., A. A. Juan, J. Panadero, C. G. Corlu, and A. Agustin. “Inventory-routing problem with stochastic demand and stock-out: A solution and risk analysis using simheuristic.” Accepted for publication in the Proceedings of the 2019 Winter Simulation Conference.
Wang, B., W. Xei, T. Martagan, A. Akcay, and C. G. Corlu. “Stochastic simulation model development for biopharmaceutical production process risk analysis and stability control.” Accepted for publication in the Proceedings of the 2019 Winter Simulation Conference.
Panadero, J., A. Juan, C. G. Corlu, J. M. Mozos, and B. S. Onggo. “Agent-based simheuristics: Extending simulation-optimization algorithms via distributed and parallel computing.” In Proceedings of the 2018 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2018): 869–880.
Akcay, A., T. Martagan, and C. G. Corlu. “Risk assessment in pharmaceutical supply chains under unknown input-model parameters.” In Proceedings of the 2018 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2018): 3132–3143.
Biller, B., S. Biller, C. G. Corlu, and O. Dulgeroglu. “The role of learning on industrial simulation design and analysis.” In Proceedings of the 2017 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2017): 3287–3298.
Biller, B., O. Dulgeroglu, C. G. Corlu, M. Hartig, R.J. Olson, P. Sandvik, and G. Trant. “Semi-conductor manufacturing simulation design and analysis with limited data.” In Proceedings of the 2017 Advanced Semiconductor Manufacturing Conference (ASMC) (2017): 298–304.
Corlu, C. G., B. Biller, and S. Tayur. “Demand fulfillment probability under parameter uncertainty.” In Proceedings of the 2016 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2016): 2316–2325.
Corlu, C. G., and B. Biller. “Subset selection for simulations accounting for input uncertainty.” In Proceedings of the 2015 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2015): 437–446.
Biller, B., A. Akcay, C. G. Corlu, and S. Tayur. “A simulation-based support tool for data-driven decision making: Operational testing for dependence modeling.” In Proceedings of the 2014 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2014): 899–909.
Corlu, C. G., and B. Biller. “A subset selection procedure under input parameter uncertainty.” In Proceedings of the 2013 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2013): 463–473.
Biller, B., and C. Gunes. “Capturing parameter uncertainty in simulations with correlated inputs.” In Proceedings of the 2010 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2010): 1167–1177.
Biller, B., and C. Gunes. “Tutorial: Introduction to simulation input modeling.” In Proceedings of the 2010 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (2010): 49–58.
Gunes, C., W-J van Hoeve, and S. Tayur. “Vehicle routing for food rescue programs: A comparison of different approaches.” In Proceedings of the 2010 International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR), Lecture Notes in Computer Science 6140 (Springer 2010): 287–291.
Working Papers
Juan, A. A., C. G. Corlu, M. Nogal, N. Campos, and C. Caliz. “Worldwide interdisciplinary education: Teaching computer simulation and data analytics to students with heterogeneous backgrounds” (submitted to Journal of Simulation).
Ghorpade, T., and C. G. Corlu. “Simheuristic approach for the stochastic one commodity pick-up and delivery traveling salesman problem” (submitted to Journal of Simulation).
“Inventory management under catastrophic risk.” Working paper with B. Biller, E. Wolf, and E. Yucesan.
“Measuring the impact of limited input data on the outcome of stochastic optimization problems.” Working paper with S. B. Onggo, J. Panadero, and A. A. Juan.
“Inventory budget-optimization with heavy-tailed demand.” Working paper with B. Harris.
Invited Seminars
“Simulation of inventory systems with unknown input models.” Northeastern University, Mechanical and Industrial Engineering Department, Boston, Mass., October 2018.
“Simulation of inventory systems with unknown input models.” McKinsey & Company, Boston, Mass., June 2018.
“The role of input risk on industrial simulation design and analysis.” McKinsey & Company, Boston, Mass., February 2018.
“Simulation of inventory systems with unknown input models: A data-driven approach.” Naval Postgraduate School, Monterey, Calif., December 2017.
“Analytics for service-estimation in inventory systems with unknown demand models.” Boston University Metropolitan College, November 2017.
“Development of a new graduate program in supply chain management” (joint work with E. Sonmez, J. Maleyeff, and V. Zlatev). Boston University Metropolitan College, Fall 2016 Research Colloquium Series, 2016.
“Preparatory hands-on laboratories as prerequisites and skills test centers for selected graduate programs and courses” (joint work with V. Zlatev, C. Corlu, I. Vodenska, and MET ETI Group). Boston University Metropolitan College, Fall 2016 Research Colloquium Series, 2016.
“A new decision support tool for data-driven inventory control” (joint work with B. Biller, A. Akcay, and S. Tayur). Boston University Metropolitan College, 2015.
“Quantification of demand parameter uncertainty in inventory simulations” (joint work with B. Biller). Boston University Metropolitan College, Middle East Technical University, UMASS Lowell, 2012.
“Capturing parameter uncertainty in simulations with correlated inputs.” Bilkent University, 2011.
“A Bayesian model for representing parameter uncertainty in simulations with correlated inputs.” Koc University, 2009.
Invited Conference Presentations
“Inventory management with disruption risk.” Winter Simulation Conference, virtual, 2020.
“Measuring the impact of limited input data on the outcome of stochastic optimization problems.” IISE Conference, New Orleans, La., 2020.
“Inventory routing problem with stochastic demand and stock-out: A solution and risk analysis using simheuristic.” Winter Simulation Conference, National Harbor, Md., 2019.
“Stochastic simulation model development for biopharmaceutical production process risk analysis and stability control.” Winter Simulation Conference, National Harbor, Md., 2019.
“Driving inventory system simulations with unknown demand models.” POMS Annual Meeting, Washington, DC, May 2–6, 2019.
“Maximizing demand fulfillment probability under input uncertainty.” POMS Annual Meeting, Washington, DC, May 2–6, 2019.
“Demand fulfillment probability in a multi-item inventory system with limited historical data.” INFORMS Annual Meeting, Phoenix, Ariz., November 4–7, 2018.
“Supply failure probability in pharmaceutical supply chains under input-model uncertainty.” INFORMS Annual Meeting, Phoenix, Ariz., November 4–7, 2018.
“The role of learning on industrial simulation design and analysis.” 2017 Winter Simulation Conference, Las Vegas, Nev., December 3–6, 2017.
“Simulation of inventory systems with unknown input models.” INFORMS Annual Meeting, Houston, Tex., October 22–25, 2017.
“Subset selection for simulations accounting for input uncertainty.” Winter Simulation Conference, Huntington Beach, Calif., December 6–9, 2015.
“Demand fulfillment probability under parameter uncertainty.” INFORMS Annual Meeting, San Francisco, Calif., November 9–12, 2014.
“Food Banks can improve their operations with OR tools.” INFORMS Annual Meeting, Phoenix, Ariz., October 14–17, 2012.
“Food banks can improve their operations with OR tools: A pilot study on Pittsburgh food Bank.” INFORMS Annual Meeting, San Diego, Calif., October 11–14, 2009.
“Accounting for multivariate parameter uncertainty in multi-product inventory simulations.” INFORMS Annual Meeting, San Diego, Calif., October 11–14, 2009.
“A Bayesian model for sampling NORTA-J parameters.” CORS/INFORMS Conference, Toronto, Canada, June 13–16, 2009.
“Accounting for multivariate parameter uncertainty in large-scale simulations.” INFORMS Annual Meeting, Washington, DC, October 11–14, 2008.
Contributed Conference Presentations
“Managing inventory under disruption risk.” INFORMS Annual Meeting, virtual, 2021.
“Solving an urban ridesharing problem with stochastic traveling times: A simheuristic approach.” Accepted for presentation at Winter Simulation Conference, 2021.
“Knee optimization for queuing systems: A customized approach.” Accepted for presentation at Decision Sciences Institute Annual Meeting, 2021.
“Decision support system with simulation-based optimization for healthcare capacity planning.” 10th Simulation Workshop of the Operational Research Society, virtual, 2021.
“Comparison of visual and mathematical approaches for capacity planning: Evidence from surveys.” POMS Annual Meeting, virtual, 2021.
“Inventory management under catastrophic risk.” POMS Annual Meeting, virtual, 2021.
“The impact of COVID-19 on the working environment of professional employees: Implications for educators.” NEDSI Annual Meeting, virtual, 2021.
“Simulation metamodeling to support hospital capacity planning.” Poster presentation, Winter Simulation Conference, virtual, 2020.
“Using lean to improve the customer experience in call centers: A meta-analysis approach.” 51st Annual Conference of the Decision Sciences Institute, virtual, 2020.
“On the scarcity of observations when modeling random inputs and the quality of solutions to stochastic optimization problems.” Winter Simulation Conference, virtual, 2020.
“Real-time nurse dispatching using dynamic priority decision framework.” Winter Simulation Conference, virtual, 2020.
“Selective pick-up and delivery traveling salesman problem: A simheuristics approach.” Winter Simulation Conference, virtual, 2020.
“Teaching risk analytics using R.” Winter Simulation Conference, virtual, 2020.
“Monte Carlo simulations to teach the effect of lean methods to improve business processes.” Winter Simulation Conference, National Harbor, Md., 2019.
“A simulation-based decision framework for stable, flexible, and efficient biomanufacturing development.” INFORMS Annual Meeting, Seattle, Wash., 2019.
“Dynamic learning and pricing with time-inconsistent customer behavior.” INFORMS Annual Meeting, Seattle, Wash., 2019.
“Analytics for service-estimation in inventory systems with unknown input model.” The International Society for Business and Industrial Statistics (ISBIS) Conference. IBM T.J. Watson Research Center, Yorktown Heights, N.Y., June 7, 2017 (poster presentation).
“Comparing simulated system designs under input parameter uncertainty.” INFORMS Annual Meeting, San Francisco, Calif., November 9–12, 2014.
“On the price of correlation parameter uncertainty in simulation optimization.” INFORMS Annual Meeting, San Francisco, Calif., November 9–12, 2014.
“A simulation-based support tool for data-driven decision making: Operational testing for dependence modeling.” Winter Simulation Conference, Savannah, Ga., December 7–10, 2014 (after critical review).
“Accounting for parameter uncertainty in subset selection for simulation.” INFORMS Annual Meeting, Minneapolis, Minn., October 6–9, 2013.
“A subset selection procedure under input parameter uncertainty.” Winter Simulation Conference, Washington, DC, December 8–11, 2013 (after critical review).
“Representing demand parameter uncertainty in inventory simulations.” INFORMS Annual Meeting, Chapel Hill, N.C., November 13–16, 2011.
“Capturing multivariate parameter uncertainty in stochastic simulations.” Winter Simulation Conference, Baltimore, Md., December 5–8, 2010. (after critical review).
“A Bayesian model for the accurate simulation of multi-product inventory systems.” YAEM Conference, Istanbul, Turkey, 2010.
“Improving the design and analysis of multi-product inventory systems.” POMS Annual Meeting, Vancouver, Canada, May 7–10, 2010.
“Vehicle routing for food rescue programs.” Humanitarian Logistics Conference, Atlanta, Ga., March 4–5, 2010 (poster presentation).
“A Bayesian model for sampling correlated inputs.” INFORMS Annual Meeting, San Diego, Calif., October 11–14, 2009.
“Accounting for multivariate parameter uncertainty in large-scale stochastic simulations.” Winter Simulation Conference, Austin, Tex., December 13–16, 2009 (poster presentation).
“A Bayesian model for representing parameter uncertainty in simulations with correlated inputs.” Winter Simulation Conference, Austin, Tex., December 13–16, 2009 (poster presentation).
“An analysis of Greater Pittsburgh Community Food Bank.” Humanitarian Logistics Conference, Atlanta, Ga., February 19–20, 2009 (poster presentation).
“Representing multivariate demand uncertainty in multi-product inventory simulations.” MSOM Conference, Boston, Mass., June 28–30, 2009 (after critical review).
“A Bayesian model for simulations with correlated inputs.” INFORMS Applied Probability Society Conference, Ithaca, N.Y., July 12–15, 2009.
Editorial Service
Proceedings Editor, INFORMS 2022 Winter Simulation Conference
Lead Proceeding Editor, INFORMS 2023 Winter Simulation Conference
Topic Editor, Sustainability (2020–present)
Associate Editor, Journal of Simulation (2020–present)
Editorial Board Member, Journal of Business Analytics (2018–present)
Conference and Session Organizer
INFORMS Winter Simulation Conference Track Coordinator, Robust Simulation Track (2020, 2021)
INFORMS Winter Simulation Conference Program Committee Member
- Logistics, SCM, and Transportation Track Program Committee (2016–2021)
- Analysis and Methodology Track Program Committee (2014, 2016, 2018–2021)
- Uncertainty Quantification and Robust Simulation Track Program Committee (2019)
INFORMS Annual Meeting Session Organizer (2013 – Present)
INFORMS Winter Simulation Conference Session Organizer (2010 – Present)
Scientific Journal Reviewer
Ad hoc referee for journals: Management Science, Operations Research, Informs Journal on Computing, Naval Research Logistics, Decision Sciences, TOMACS, Annals of Operations Research, Mathematics of Operations Research, IISE Transactions, Computers and Operations Research, European Journal of Operational Research, Journal of Simulation, and Journal of Business Analytics.
Ad hoc referee for conference proceedings: Winter Simulation Conference Proceedings, Analysis and Methodology Track (2014, 2016, 2018, 2019, 2020); Winter Simulation Conference Proceedings, Logistics, SCM, and Transportation Track (2016–present); Winter Simulation Conference Proceedings, Uncertainty Quantification and Robust Simulation Track (2019); Northeast Decision Sciences Institute (2018); 51st Hawaii International Conference on System Sciences (2017).
Elected Positions
Treasurer, INFORMS Simulation Society (2020–present)
Communications Editor, INFORMS Simulation Society (2018–2020)
Treasurer, INFORMS Junior Faculty Interest Group (2016–2020)
Other Professional Service
Mentor, INFORMS WORMS Mentorship program (2015, 2021)
Judge, INFORMS Minority Issues Forum Poster Competition (2021)
Grants
International Partner, “Efficient and sustainable transport systems in smart cities: Internet of things, transport logistics, and agile algorithms (trans analytics).” Funded by the Spanish Ministry of Science (PID2019-111100RB-C21), 2020–2023 (200,000 Euro).
Principle Investigator, “Comparison of fitting methods for the generalized lambda distribution and development of improved fitting methods.” Funded by the Scientific and Technological Research Council of Turkey–ARDEB 1002, November 2011–2012 (8000YTL).
“How much data are needed: Estimation of inventory service levels with limited historical demand data.” Funded by the Scientific Research Society Sigma Xi, Grants-in-Aid-of-Research Program, 2010 ($1000).
Honors and Awards
Recipient of the INFORMS Volunteer Service Award, INFORMS, 2021.
Recipient of the Chadwick Fellowship Award, Metropolitan College, Boston University, 2017.
Recipient of Committee on Underrepresented Minorities and Women (CUMW) award of INFORMS Simulation Society, 2009.
Scholarship to attend Humanitarian Logistics Conferences, Atlanta, Ga., 2009 and 2010.
Recipient of William Larimer Mellon Fellowship for doctoral studies at Carnegie Mellon University, Tepper School of Business, 2006-2010.
Recipient of Werner von Siemens Excellence Award for Science and Innovation, Koc University, in recognition of excellence in BS Industrial Engineering program, 2006.
Faculty Q&A
What is your area of expertise?
My research is in the area of the design and analysis of stochastic computer simulations, with applications in several areas including finance, inventory management, agri-food supply chains, pharmaceutical supply chains, and bio-manufacturing. In today’s uncertain world, simulation is an indispensable tool that is widely used to model complex business problems. I specifically look at how uncertainty in the inputs for the computer simulations affects the decision-making process in the aforementioned industries.
What courses do you teach at MET?
I teach Operations Management: Business Process Fundamentals (MET AD 605), Global Supply Chains (MET AD 680), and Enterprise Risk Analytics (MET AD 616). I also supervise the Capstone Project for Supply Chain Management (MET AD 804).
Is there a particular project within the courses you teach that most interests your students? What is it and why?
One of the main topics covered in the Enterprise Risk Analytics course is how to build a simulation model of a business problem to gain deeper insights about the problem, as well as to identify risks associated with a particular decision made by the decision-maker. Students enjoy using simulation as a technique as it allows them to model a complex system without relying on assumptions.
In the Operations Management and Global Supply Chains courses, students enjoy the presentation of analytical methods in the context of forecasting, inventory management, capacity management, and waiting line management, among other topics. These methods are then reinforced with real-life case studies.
The common thing that interests students in all these three courses is the applicability of the material in their workplaces. Students often share with me how they are using what they are learning in their courses in their daily work.
What initially drew you to Boston University? How did you connect with BU MET?
I was looking for an organization that supports junior faculty members as they grow as researchers and teachers, and also one that would benefit from my specific area of expertise. What first drew me to MET was the opportunity to teach a course in the supply chain management graduate program. My conversations with the faculty proved to me that this would be the right place for me to grow as both a researcher and a teacher. Looking back, I am glad that I made the decision to join BU MET.
I was able to establish myself as a researcher with expertise in simulations with applications in several areas. As an instructor, I have had the opportunity to develop and teach several courses related to my area of expertise. I also had the privilege of developing BU MET’s Master of Science in Supply Chain Management program and contributing to the Master of Science in Applied Business Analytics program.
In your opinion, what are the distinguishing characteristics of BU MET’s graduate programs in supply chain management? What sets MET apart from the competition?
We give our students the flexibility to choose one of three concentrations based on the area of supply chain management that interests them most: Global Business, Logistics Management, or Analytics. No matter which concentration the student pursues, our first priority is to teach industry-relevant concepts in class. In fact, we have an industry advisory board that I consult with regularly to understand the trends in the market and to make sure that those trends are covered in our courses.
We also make sure that we allow enough time for hands-on exercises in our classes. Another priority of the program is to help our students position themselves in the job market. To this end, we host panel sessions and other career-related events that enable students to expand their networks and hear from industry experts about needs in the market. Last fall, students were invited to two career panel sessions: “Career Opportunities for Supply Chain Management Students” and “Career Opportunities in Pharmaceutical Supply Chains.”
Finally, our capstone course—one of the mandatory courses in the Supply Chain Management master’s program—gives our students an edge in the job market by allowing them to work on a real problem selected by either the students or by industry experts. In fact, this semester, one of our students is working with me and an industry expert in the analysis of the logistic approaches of a large Boston-area furniture company.
How do the concepts students learn in MET’s supply chain management graduate programs apply in practice?
We have a nice blend of theory and practical approaches in our courses. We teach our students what is relevant in the business world so that they can apply what they’ve learned in class immediately in their jobs. Most of my students (who are also working professionals) share with me that the material that we cover in class is useful and practical—this confirms that the concepts that we teach in class apply directly in practice.
Have you contributed to any research or publications recently? What was the essence of the project?
My recently published works illustrate how to simulate inventory systems with unknown input models and parameters. The two papers that resulted from this research were selected as finalists in the best paper competition of the INFORMS Minority Issues Forum.
Most recently, I have been expanding my work to agri-food supply chains and the bio-manufacturing industry by collaborating with a group of researchers in Spain, England, the Netherlands, and the U.S. More specifically, our paper in the area of agri-food supply chains integrates transportation and inventory decisions in an agri-food supply chain having stochastic customer demands and perishable items. Our goal is to design routes that will minimize the costs related to transportation, inventory, and food waste while adhering to inherent constraints in the problem. In a related study, we are working on understanding how stochastic inputs can affect the performance of different agri-food supply chain configurations.
Our work in bio-manufacturing industry aims to build a simulation model for the bio-manufacturing production process to support decision-making in this industry. This study is in collaboration with one of the largest animal health companies in the world. After validating the model with input from the company, our goal is to go deeper and study the effect of uncertainties in the inputs of the simulation on the decision-making process of the company.
As coordinator for BU MET’s supply chain management graduate programs, how do you see the curriculum evolving to stay current with industry trends?
There are a number of trends that we can observe in the area of supply chain management. One, there are myriad risks that exist in supply chains. With the globalization of supply chains, these risks are only increasing; two, lean operations, sustainable supply chains, and reverse logistics are gaining more importance than ever before; and three, businesses have been utilizing new digital technologies including cloud platforms, big data, analytics, and the Internet of Things (IoT). Companies are leveraging these approaches in their manufacturing processes, logistics processes, and for understanding their customers and competitors.
At BU MET, we revise our courses constantly to stay current with these recent trends in the area of supply chain management.