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07 Oct
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Department
Department of Industrial Engineering
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Distributionally Robust Optimization Approaches for a Stochastic Mobile Facility Routing and Scheduling Problem

This is a past event.

Abstract

A mobile facility (MF) is a “facility-like vehicle” capable of moving from one place to another, providing real-time service to customers in the vicinity of its location when it is stationary. In this talk, we discuss a mobile facility (MF) fleet sizing, routing, and scheduling problem in which probability distributions of the time-dependent demand for MF services are unknown. Specifically, we present and analyze two distributionally robust MF routing and scheduling (DMFRS) models to address uncertainty and distributional ambiguity. These models seek to minimize the fixed cost of establishing the MF fleet and maximum expected transportation and unmet demand costs over all possible demand distributions residing within an ambiguity set. In the first model, we use a moment-based ambiguity set. In the second model, we use an ambiguity set that incorporates all distributions within a 1-Wasserstein distance from a reference distribution. To solve DMFRS models, we propose a decomposition-based algorithm and derive lower bound and two–families of symmetry breaking inequalities to strengthen the master problem and speed up convergence. In addition, we extend our models using a mean conditional value-at-risk criterion to model the decision maker’s risk-averse attitude. Finally, we present extensive computational experiments comparing the operational and computational performance of the proposed distributionally robust models and a stochastic programming model and drive insights into DMFRS.

Biography

Dr. Karmel S. Shehadeh is an Assistant Professor of Industrial Systems and Engineering (ISE) at Lehigh University. She currently serves as one of the directors of the IISE Operations Research Division.  Before joining Lehigh,  she was a Presidential and Dean Postdoctoral Fellow at Heinz College of Information Systems and Public Policy at Carnegie Mellon University. She holds a doctoral degree in Industrial and Operations Engineering from the University of Michigan, a master's degree in Systems Science and Industrial Engineering from Binghamton University, and a bachelor's in Biomedical Engineering from Jordan University of Science and Technology. 

Shehadeh’s broad methodological research expertise and interests include scheduling theory and algorithms, mixed-integer programming, and stochastic optimization. Her primary application areas and expertise are in healthcare operations and analytics. Her research group is currently working on solving emerging and challenging real-world optimization problems within and outside healthcare operations. These include healthcare scheduling and capacity planning, home care, hospital readmission, facility location, and disaster response operations.

Hosted by Dr. Amin Rahimian

Thursday, October 7 at 3:30 p.m. to 4:30 p.m.

Virtual Event

Distributionally Robust Optimization Approaches for a Stochastic Mobile Facility Routing and Scheduling Problem

Abstract

A mobile facility (MF) is a “facility-like vehicle” capable of moving from one place to another, providing real-time service to customers in the vicinity of its location when it is stationary. In this talk, we discuss a mobile facility (MF) fleet sizing, routing, and scheduling problem in which probability distributions of the time-dependent demand for MF services are unknown. Specifically, we present and analyze two distributionally robust MF routing and scheduling (DMFRS) models to address uncertainty and distributional ambiguity. These models seek to minimize the fixed cost of establishing the MF fleet and maximum expected transportation and unmet demand costs over all possible demand distributions residing within an ambiguity set. In the first model, we use a moment-based ambiguity set. In the second model, we use an ambiguity set that incorporates all distributions within a 1-Wasserstein distance from a reference distribution. To solve DMFRS models, we propose a decomposition-based algorithm and derive lower bound and two–families of symmetry breaking inequalities to strengthen the master problem and speed up convergence. In addition, we extend our models using a mean conditional value-at-risk criterion to model the decision maker’s risk-averse attitude. Finally, we present extensive computational experiments comparing the operational and computational performance of the proposed distributionally robust models and a stochastic programming model and drive insights into DMFRS.

Biography

Dr. Karmel S. Shehadeh is an Assistant Professor of Industrial Systems and Engineering (ISE) at Lehigh University. She currently serves as one of the directors of the IISE Operations Research Division.  Before joining Lehigh,  she was a Presidential and Dean Postdoctoral Fellow at Heinz College of Information Systems and Public Policy at Carnegie Mellon University. She holds a doctoral degree in Industrial and Operations Engineering from the University of Michigan, a master's degree in Systems Science and Industrial Engineering from Binghamton University, and a bachelor's in Biomedical Engineering from Jordan University of Science and Technology. 

Shehadeh’s broad methodological research expertise and interests include scheduling theory and algorithms, mixed-integer programming, and stochastic optimization. Her primary application areas and expertise are in healthcare operations and analytics. Her research group is currently working on solving emerging and challenging real-world optimization problems within and outside healthcare operations. These include healthcare scheduling and capacity planning, home care, hospital readmission, facility location, and disaster response operations.

Hosted by Dr. Amin Rahimian

Thursday, October 7 at 3:30 p.m. to 4:30 p.m.

Virtual Event

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