• Network Interdiction

Network interdiction problems involve sequential games typically played by two players on a network: a follower who wishes to maximize his or her own interest (or profit), and a leader who wishes to minimize the maximum profit attainable by the follower.  These problems are often observed in applications facing threats of a malignant adversary, and have received much attention in that realm.

  • New Service Development (NSD) – Autonomous EV Charging

The user involvement has been considered a crucial factor for successful NSD and using a simulation platform is listed as one effective, but costly practice. However, when the service is rendered under the M2M environment, the user involvement becomes less critical as the interaction between the user and the service system is minimal. A simulation platform can be beneficially utilized during the NSD process to gather a large amount of data for a service under the M2M environment. The autonomous EV charging service for self-driving vehicles is essentially rendered in the M2M environment. The simulation-based analytical platform can be useful to share the assessed performance with businesses and stakeholders during the NSD process.

  • Stability in Production Planning

The flexible requirements profile (FRP) is a production planning scheme that is designed to reduce variability in production plans over a certain planning-horizon.  Under FRP, current and future production quantities are subject to a funnel-shaped set of bounds, which typically stipulates a narrower range between lower and upper bounds in the near future while this range increases as we move into the further future.

  • Nondifferentiable Optimization

Nondifferentiable optimization (NDO) involves nondifferentiability in the objective and constraint functions. I have particularly worked on the nondifferentiable optimization problems arising in the context of solving Lagrangian relaxations of large-scale mixed integer programs. Conventional gradient-based optimization methods cannot be employed due to nondifferentiability, and hence, NDO methods are specially designed to guarantee convergence.

  • Optimal Stopping

Optimal stopping problem seeks the best timing to take an action that yields the maximum expected rewards when a series of decision-making points are set ahead. A well-known example of this type of problems is a secretary problem, where an open secretary position is filled during a finite sequence of interviews. The interviewer must accept or reject the interviewee right after the interview only based on relative rankings. Although there are numerous variants of the problem that have been introduced in literature, the most common solution approach is to employ dynamic programming, which utilizes recursive decision-making.