activity
20172022
most citedAn Efficient Approach to Distributionally Robust Network Capacity Planning

3 citations · 7 across the 7 of their papers we have counts for

collaborators

12 papers

math.OC2025

Algorithms for min-buying in networks

Aaditya Bhardwaj, Ben Black, Trivikram Dokka +1

The paper is motivated by pricing decisions faced by forecourt fuel retailers across their outlets on a road network. Through our modelling approach we are able adapt the network s…

math.OC20221 cited

Robust Markov decision processes under parametric transition distributions

Ben Black, Trivikram Dokka, Christopher Kirkbride

This paper considers robust Markov decision processes under parametric transition distributions. We assume that the true transition distribution is uniquely specified by some param…

math.OC20212 cited

Data-driven Heuristics for DC optimal transmission switching problem

Juncheng Li, Trivikram Dokka, Guglielmo Lulli +1

The goal of Optimal Transmission Switching (OTS) problem for power systems is to identify a topology of the power grid that minimizes the cost of the system operation while satisfy…

eess.SP2020

Understanding controlled EV charging impacts using scenario-based forecasting models

Rahul Roy, Trivikram Dokka, David A. Ellis +2

Electrification of transport is a key strategy in reducing carbon emissions. Many countries have adopted policies of complete but gradual transformation to electric vehicles (EVs).…

math.OC2020

Automatic Generation of Algorithms for Black-Box Robust Optimisation Problems

Martin Hughes, Marc Goerigk, Trivikram Dokka

We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly t…

math.OC20203 cited

An Efficient Approach to Distributionally Robust Network Capacity Planning

Trivikram Dokka, Francis Garuba, Marc Goerigk +1

In this paper, we consider a network capacity expansion problem in the context of telecommunication networks, where there is uncertainty associated with the expected traffic demand…