Increasing Driver Flexibility through Personalized Menus and Incentives in Ridesharing and Crowdsourced Delivery Platforms

Allowing drivers to choose which requests to fulfill provides drivers with much-needed autonomy in ridesharing and crowdsourced delivery platforms. While stochastic, a driver’s acceptance of requests in their menu is influenced by the platform’s offered compensation. Therefore, in this work, we create and solve an optimization model to determine personalized menus and incentives to offer … Read more

A Reformulation Technique to Solve Polynomial Optimization Problems with Separable Objective Functions of Bounded Integer Variables

Real-world problems are often nonconvex and involve integer variables, representing vexing challenges to be tackled using state-of-the-art solvers. We introduce a mathematical identity-based reformulation of a class of polynomial integer nonlinear optimization (PINLO) problems using a technique that linearizes polynomial functions of separable and bounded integer variables of any degree. We also introduce an alternative … Read more

Decision Intelligence for Nationwide Ventilator Allocation

Many states in the U.S. have faced shortages of medical resources because of the surge in the number of patients suffering from COVID-19. As many projections indicate, the situation will be far worse in coming months. The upcoming challenge is not only due to the exponential growth in cases but also because of inherent uncertainty … Read more

Multi-criteria Course Mode Selection and Classroom Assignment Under Sudden Space Scarcity

Problem Definition: While physical (or ‘social’) distancing is an important public health intervention during airborne pandemics, physical distancing dramatically reduces the effective capacity of classrooms. During the COVID-19 pandemic, this presented a unique problem to campus planners who hoped to deliver a meaningful amount of in-person instruction in a way that respected physical distancing. This … Read more

Confidence Region for Distributed Stochastic Optimization Problem via Stochastic Gradient Tracking Method

Since stochastic approximation (SA) based algorithms are easy to implement and need less memory, they are very popular in distributed stochastic optimization problems. Many works have focused on the consistency of the objective values and the iterates returned by the SA based algorithms. It is of fundamental interest to know how to quantify the uncertainty … Read more