# fORum Seminar

An internal seminar series organised for and by PhD students in OOR. Find information on upcoming and past events here.

## Events

### 2022

**Date:** 22^{nd} of July 2022, 13:00 - 15:00 (BST)

**Location:** Zoom

Speaker | Talk |
---|---|

Jan Krause^{(3)} |
The Bipartite Boolean Quadric Polytope with Multiple-Choice Constraints The well-known bipartite boolean quadric polytope (BQP) is defined as the convex hull of all quadric incidence vectors over a bipartite graph. Here, we consider the case where there is a partition on one of the two bipartite node sets such that at most one node per subset of the partition can be chosen. In this talk, results of the polyhedral study and the relation to the pooling problem are presented. Furthermore, a similar polytope is introduced that will be analyzed in future work. |

Haoyue (Claire) Zhang |
Facility Location Problem under Uncertainty with Service Level Constraints In facility location, most models assume customer demands to be deterministic. However, in practice, there is often a large degree of uncertainty about future demands, especially given the strategic nature of location problems where decisions have to be made for the next twenty or thirty years. Stochastic programming models are widely applied to solve FLPs under uncertainty. However, since all the demand has to be satisfied in every scenario, the models sometimes give conservative results. This presentation is about including service levels as chance constraints in the stochastic programming model, allowing for a certain probability and a certain percent of the demand to be unsatisfied. In the model, the α-service level is applied both locally and globally while the β-service level constraints take the expected value, as well as the maximum value of the excess into account. To decide which combination of service levels “works the best”, we carry out experiments and tests with combinations of different settings on randomly generated data sets. |

Adrian Göß^{(3)} |
Gas Network Control by Simulation-based Reinforcement Learning Optimal control problems for gas flow in pipeline networks are usually tackled in mathematics within three steps: first, modelling the problem as a MINLP, second, approximating it to receive a MIP, third, optimizing the approximated problem. Imagining a discretized time horizon, we leave the determination of control variables in every time step as a decision to a machine learning approach. In contrast to standard methods, we leverage the optimization as a simulation framework for the resulting easier problem. This technique combines the fields of artificial intelligence as well as mathematical optimization and is more accurate in the modelling of nonlinearities, as well as regarding the functionality of gas network controls. In cooperation with an industry partner, we apply a reinforcement learning technique called (categorical) deep Q-network (CDQN) to control gas subnetworks. The learning of the agent and the improvement of its control is achieved via Q-learning, a special case of approximate dynamic programming by Bellman that incorporates future, as well as present states to accomplish the overall best result. This thesis contains a description of the original model, as well as an explanation of the used CDQN approach, and closes with computational results on a real-world gas subnetwork. |

Andrés Miniguano Trujillo^{(1)(2}^{)} |
A nonlocal PDE-constrained optimisation model for containment of infectious diseases Nonpharmaceutical interventions have proven crucial in the containment and prevention of Covid-19 outbreaks. In particular public health policy makers have to assess the effects of strategies such as social distancing and isolation to avoid exceeding social and economical costs. In this work, we study an optimal control approach for parameter selection applied to a dynamical density functional theory model. This is applied in particular to a spatially-dependent SIRD model where social distancing and isolation of infected persons are explicitly taken into account. Special attention is paid when the strength of these measures is considered as a function of time and their effect on the overall infected compartment. A first order optimality system is presented, and numerical simulations are presented using a proximal method. This work could potentially provide some mathematical insights into the management of disease outbreaks. |

**Affiliation:**

(1) PhD Student - University of Edinburgh

(2) PhD Student - Heriot-Watt University

(3) PhD Student - University of Erlangen-Nuremberg

**Date:** 11^{th} of May 2022, 14:00 - 15:00 (BST)

**Location:** Maths Seminar Room (5323 JCMB) and Zoom

Speaker | Talk |
---|---|

Egon Persak |
An inexpensive machine learning solution to fix the forecasting models that the pandemic broke The pandemic’s effect on consumer behaviour caused many predictive models to completely fail. As this predictive work is essential in downstream decision making, especially in such a time of crisis, a reliable and efficient method to mend the predictive capacity of forecasting models is a crucial business need. This presentation is about a method developed at ExPretio Technologies to tackle this problem for rail passenger demand forecasting. The key insight is that existing models can be recycled as inputs for machine learning, specifically to reassess the predictions from the original models in the context of newly incoming data. A major advantage of this method is that it alleviates the need for costly calibration required to develop predictive models from scratch. This method not only fixed the models broken by the pandemic but also gives an improved performance compared to previously used individual models on pre-pandemic data. |

**Affiliation:**

(1) PhD Student - University of Edinburgh

**Date:** 10^{th} of February 2022, 12:30-14:00 (GMT)

**Location:** Maths Seminar Room (5323 JCMB) and Zoom

Speaker | Talk |
---|---|

Nicholas Good |
Challenges and careers at KrakenFlex |

Monse Guedes Ayala |
How can we speed up solving a large-scale distributed energy portfolio management problem? |

**Affiliation:**

(1) Senior Data Scientist - Krakenflex Ltd

(2) PhD Student - University of Edinburgh

### 2021

**Date:** 10^{th} of December 2021, 13:00-16:00 (GMT)

**Location:** GatherTown

Speaker | Talk |
---|---|

Lukas Hümbs^{(1)} |
A generalized optimality certificate for convex MINLPs |

Yuzhou Qiu^{(2}^{)} |
Convex Relaxation and Global Solutions of QCQPs |

Lukas Glomb^{(1)} |
A novel decomposition approach for holistic airline optimization |

Paula Fermin Cueto^{(2}^{)} |
What if you gave your MIP to Marie Kondo? Solving problems faster with preprocessing |

Jorge Weston^{(1)} |
Robust AC Optimal Power Flow |

Spyros Pougkakiotis^{(2}^{)} |
Regularized interior point methods for convex programming |

**Affiliation:**

(1) PhD Student - University of Erlangen-Nuremberg

(2) PhD Student - University of Edinburgh

**Date:** 19^{th} of November 2021, 16:00-17:00 (GMT)

**Location:** 6206 JCMB and Zoom

Speaker | Talk |
---|---|

Jakub Kruzik |
PERMON toolbox for quadratic programming |

**Affiliation:**

(1) Guest of HPC-Europa program, VSB-Technical University of Ostrava

**Date:** 5^{th} of November 2021, 13:00-16:00 (GMT)

**Location:** Zoom and GatherTown

Speaker | Talk |
---|---|

Charlotte Cost^{(1)} |
Using Integer Programming for Statistical Data Privacy |

Yiran Zhu^{(2}^{)} |
Approximate Nonlinear programming by Monomial Orders |

Yasmine Beck^{(1)} |
Bounded Rationality in Bilevel Optimization |

Josh Fogg^{(2}^{)} |
High Performance Portfolio Optimization |

Luka Schlegel^{(1)} |
Fluid Modelling and Shape Optimization for Coastal Protection |

Nagisa Sugishita^{(2}^{)} |
Pre-trained Heuristics for Unit Commitment |

**Affiliation:**

(1) PhD Student - University of Trier

(2) PhD Student - University of Edinburgh

**Date:** 21^{st} of October 2021, 16:00-17:00 (GMT)

**Location:** 6206 JCMB and Zoom

Speaker | Talk |
---|---|

Albert Solà Vilalta |
ADMM-based Unit and Time Decomposition for Price Arbitrage by Cooperative Price-Maker Electricity Storage Units Decarbonisationvia the integration of renewablesposes significant challenges for electric power systems, but also creates new market opportunities. Electric energy storage can take advantage of these opportunities while providing flexibility to power systems that can help address these challenges. We propose a solution method for the optimal control of multiple price-maker electric energy storage units that cooperate to maximise their total profit from price arbitrage. The proposed method can tackle the nonlinearityintroduced by the price-maker assumption. The main novelty of the proposed method is the combination of a decomposition by unit and a decomposition in time. The decomposition by unit is based on the Alternating Direction Method of Multipliers and breaks the problem into several one-unit subproblems. Every subproblemis solved using an efficient algorithm for one-unit problems from the literature that exploits an on the fly decomposition in time, and this results in a time decomposition for the whole solution method. Our numerical experiments show very promising performance in terms of accuracy and computational time. In particular, they suggest that computational time scales linearly with the number of storage units. |

**Affiliation:**

(1) PhD Student - University of Edinburgh

**Date:** 7^{th} of October 2021, 16:00-17:00 (GMT)

**Location:** Teams

Speaker | Talk |
---|---|

Shunee Johnn Andrés Miniguano Trujillo Yiran Zhu |
13th AIMMS-MOPTA Optimization Modeling Competition: Winners and their approach |

**Affiliation:**

(1) PhD Student - University of Edinburgh

(2) PhD Student - Heriot-Watt University

### 2020

**Date:** 25^{th} of November 2020, 12:00-13:00 (GMT)

**Location:** GatherTown

Speaker | Talk |
---|---|

Andrés Miniguano Trujillo |
In this talk, we will briefly examine some problems that I have encountered in through my journey in OR. Among the cases we will cover are the modelling and algorithmic approach to the combined routing of pollsters and vehicles, the allocation of rose-stems for international commercialisation, the assignment of patients to therapists for psychological intervention, and how set partitioning can be used to solve a particular puzzle. We will review the techniques employed for each problem and their challenges for practical applications. |

**Affiliation:**

(1) PhD Student - University of Edinburgh

(2) PhD Student - Heriot-Watt University