We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which constraints and objectives are directly learned from data using machine learning, and the trained models are embedded in an optimization formulation. We exploit the mixed-integer optimization-representability of many machine learning methods, including … Read more
We consider mixed-integer linear quantile minimization problems that yield large-scale problems that are very hard to solve for real-world instances. We motivate the study of this problem class by two important real-world problems: a maintenance planning problem for electricity networks and a quantile-based variant of the classic portfolio optimization problem. For these problems, we develop … Read more
In this paper, we study the behavior of feasible rounding approaches for mixed-integer linear and nonlinear optimization problems (MILP and MINLP, respectively) when integrated into tree search of branch-and-bound. Our research addresses two important aspects. First, we develop insights into how an (enlarged) inner parallel set, which is the main component for feasible rounding approaches, … Read more
We introduce EETTlib, an instance library for the Energy-Efficient Train Timetabling problem. The task in this problem is to adjust a given timetable draft such that several key indicators relating to the energy consumption of the resulting railway traffic are minimized. These include peak power consumption, total energy consumption, loss in recuperation energy, fluctuation in … Read more
This paper introduces a computational method for generating metric Travelling Salesperson Problem (TSP) instances having a large integrality gap. The method is based on the solution of an NP-hard problem, called IH-OPT, that takes in input a fractional solution of the Subtour Elimination Problem (SEP) on a TSP instance and compute a TSP instance having … Read more
In this paper, we present a novel scheduling problem, the stem cell culturing problem (SCP), which is identified in an attempt to improve the productivity of a manufacturing system producing a commercialized autologous stem cell therapeutic product for treating an incurable disease. For a given therapeutic product along with the corresponding manufacturing process, which is … Read more
The pandemic caused by the corona virus SARS-CoV-2 raised many new challenges for humanity. For instance, governments imposed regulations such as lockdowns, resulting in supply chain shocks at different tiers. Additionally, delivery services reached their capacity limits because the demand for mail orders soared temporarily during the lockdowns. We argue that one option to support … Read more
We consider the problem of learning an optimal prescriptive tree (i.e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observational data. This problem arises in numerous socially important domains such as public health and personalized medicine, where interpretable and data-driven interventions are sought based on data gathered … Read more
In this technical report we present several Mixed Integer Linear Programming (MILP) models for the Berth Allocation and Quay Crane Assignment Problem (BACASP) considering crane movement and setup time (from now on: BACASP-S). First, we propose a MILP for the continuous-quay time-invariant BACASP in which both berthing time and position variables are continuous. Then, we … Read more
The s-clubs model cohesive social subgroups as vertex subsets that induce subgraphs of diameter at most s. In defender-attacker settings, for low values of s, they can represent tightly-knit communities whose operation is undesirable for the defender. For instance, in online social networks, large communities of malicious accounts can effectively propagate undesirable rumors. In this … Read more