One transfer per patient suffices: Structural insights about patient-to-room assignment

While many heuristics have been proposed for the problem of patient-to-room assignment (PRA) with a large variety of different practical constraints, a thorough investigation of the problem’s structure itself has been neglected so far. Therefore, in this paper, we present insights about the basic, underlying combinatorial problem of PRA. At first we consider the problem … Read more

A Distributionally Robust Optimization Approach for Stochastic Elective Surgery Scheduling with Limited Intensive Care Unit Capacity

In this paper, we study the decision process of assigning elective surgery patients to available surgical blocks in multiple operating rooms (OR) under random surgery durations, random postoperative length-of-stay in the intensive care unit (ICU), and limited capacity of ICU. The probability distributions of random parameters are assumed to be ambiguous, and only the mean … Read more

The Multi-Stop Station Location Problem

We introduce the (directed) multi-stop station location problem. The goal is to install stations such that ordered (multi-)sets of stops can be traversed with respect to range restrictions that are reset whenever a station is visited. Applications arise in telecommunications and transportation, e.g., charging station placement problems. The problem generalizes several network optimization problems such … Read more

A New Coherent Multivariate Average-Value-at-Risk

A new multivariate performance measure Average-Value-at-Risk, mAVaR αevaluating the sum of N risky assets composing the portfolio of an investor with respect to riskN-dimensional risk level vectorαis proposed. We show that the proposed operator satisfies the four axioms of a coherent risk measure, while reducing to the one variableAverage-Value-at-RiskAVaR, in caseN= 1. In that respect, … Read more

The Crop Growth Planning Problem in Vertical Farming

In this paper, we study the problem of planning the growth of crops on shelves in vertical farming cabinets under controlled growth conditions. By adjusting temperature, humidity, light, and other environmental conditions in different parts of the cabinets, a planner must ensure that crop growth is able to satisfy some deterministic demand. We prove this … Read more

Mining for diamonds – matrix generation algorithms for binary quadratically constrained quadratic problems

In this paper, we consider binary quadratically constrained quadratic problems and propose a new approach to generate stronger bounds than the ones obtained using the Semidefinite Programming relaxation. The new relaxation is based on the Boolean Quadric Polytope and is solved via a Dantzig-Wolfe Reformulation in matrix space. For block-decomposable problems, we extend the relaxation … Read more

Enhancements to the DIDO© Optimal Control Toolbox

In 2020, DIDO© turned 20! The software package emerged in 2001 as a basic, user-friendly MATLAB teaching tool to illustrate the various nuances of Pontryagin’s Principle but quickly rose to prominence in 2007 after NASA announced it had executed a globally optimal maneuver using DIDO. Since then, the toolbox has grown in applications well beyond … Read more

Epi-convergence of Sample Averages of a Random Lower Semi-continuous Functional Generated by a Markov Chain and Application to Stochastic Optimization

The purpose of this article is to establish epigraphical convergence of the sample averages of a random lower semi-continuous functional associated with a Harris recurrent Markov chain with stationary distribution $\pi$. Sample averages associated with an ergodic Markov chain with stationary probability distribution will epigraphically converge from $\pi$-almost all starting points. The property of Harris … Read more

Combinatorial Acyclicity Models for Potential-based Flows

Potential-based flows constitute a basic model to represent physical behavior in networks. Under natural assumptions, the flow in such networks must be acyclic. The goal of this paper is to exploit this property for the solution of corresponding optimization problems. To this end, we introduce several combinatorial models for acyclic flows, based on binary variables … Read more

K-Adaptability in stochastic optimization

We consider stochastic problems in which both the objective function and the feasible set are affected by uncertainty. We address these problems using a K-adaptability approach, in which K solutions for the underlying problem are computed before the uncertainty dissolves and afterwards the best of them can be chosen for the realised scenario. This paradigm … Read more