Patrick De Causmaecker

KU Leuven | ku leuven · Department of Computer Science

Patient scheduling is a difficult task involving stochastic factors, such as the unknown arrival times of patients. Similarly, the scheduling of radiotherapy for cancer treatments needs to handle patients with different urgency levels when allocating resources. High-priority patients may arrive at any time, and there must be resources available to...

Decades of research on the 0-1 knapsack problem led to very efficient algorithms that are able to quickly solve large problem instances to optimality. This prompted researchers to also investigate whether relatively small problem instances exist that are hard for existing solvers and investigate which features characterize their hardness. Previousl...

In this article we propose a heuristic algorithm to explore search space trees associated with instances of combinatorial optimization problems. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees and represents the state-of-the-art algorithm for a number of games. Several enhanc...

Patient scheduling is a difficult task involving stochastic factors such as the unknown arrival times of patients. Similarly, the scheduling of radiotherapy for cancer treatments needs to handle patients with different urgency levels when allocating resources. High priority patients may arrive at any time, and there must be resources available to a...

To formulate a systematic and scientific nurse scheduling plan based on the nurse rostering problem (NRP), individual preferences and legal and other constraints must be fully considered. The NRP aims to optimize the allocation of human resources and to effectively reduce the workload to improve the efficiency and quality of nurses’ work. As variou...

Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is not always a standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are not always diverse enough to draw sound conclusion...

The Radiotherapy Scheduling Problem (RTSP) focuses on optimizing the planning of radiotherapy treatment sessions for cancer patients. In this paper, we propose a two-phase approach for the RTSP. In the first phase, radiotherapy sessions are assigned to specific linear accelerators (linacs) and days. The second phase then decides the sequence of pat...

In the literature, much effort has been devoted to providing taxi drivers with recommendations on how to find passengers efficiently. However, the load balancing problem is not taken seriously, leading to a situation where taxis gather and compete at some points while passengers wait a long time in other places due to low taxi supply. Besides, ther...

The 0-1 knapsack problem is an important optimization problem, because it arises as a special case of a wide variety of optimization problems and has been generalized in several ways. Decades of research have resulted in very powerful algorithms that can solve large knapsack problem instances involving thousands of decision variables in a short amo...

We studied the low-frequency $\lesssim 0.5\;$h$^{-1}$ (long-period $\gtrsim 2\;$h) oscillations of active regions (ARs). The investigation is based on an analysis of a time series built from Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) photospheric magnetograms and comprises case studies of several types of AR structures. T...

Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available when algorithms are selected and r...

The Radiotherapy Scheduling Problem (RTSP) fo-cuses on optimizing the planning of radiotherapy treatment sessions for cancer patients. In this paper, we propose a two-phase approach for the RTSP. In the first phase, radiotherapy sessions are assigned to specific linear accelerators (linacs) and days. The second phase then decides the sequence of pa...

Context. We studied the low-frequency ≲0.5 h ⁻¹ (long-period ≳2 h) oscillations of active regions (ARs). The investigation is based on an analysis of a time series built from Solar Dynamics Observatory/Helioseismic and Magnetic Imager photospheric magnetograms and comprises case studies of several types of AR structures. Aims. The main goals are to...

Semi-supervised or constrained graph clustering incorporates prior information in order to improve clustering results. Pairwise constraints are often utilized to guide the clustering process. This work addresses a constrained graph clustering problem in biological networks where (1) subgraph connectivity constraints are strictly required to be sati...

The personnel rostering problem is the problem of finding an optimal way to assign employees to shifts, subject to a set of hard constraints which all valid solutions must follow, and a set of soft constraints which define the relative quality of valid solutions. The problem has received significant attention in the literature and is addressed by a...

In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatoria...

Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is usually no standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are usually not diverse enough to draw sound conclusions...

This paper proposes a local search algorithm for a specific combinatorial optimisation problem in graph theory: the Hamiltonian completion problem (HCP) on undirected graphs. In this problem, the objective is to add as few edges as possible to a given undirected graph in order to obtain a Hamiltonian graph. This problem has mainly been studied in t...

We consider a single-machine scheduling problem with release dates and inventory constraints. Each job has a deterministic processing time and has an impact (either positive or negative) on the central inventory level. We aim to find a sequence of jobs such that the makespan is minimized while all release dates and inventory constraints are met. We...

When developing optimisation algorithms, the focus often lies on obtaining an algorithm that is able to outperform other existing algorithms for some performance measure. It is not common practice to question the reasons for possible performance differences observed. These types of questions relate to evaluating the impact of the various heuristic...

In the present contribution, a chance-constrained scheduling model is presented for determining admission dates of elective surgical patients. The admission scheduling model is defined considering a dynamic, stochastic decision-making environment. The primary aim of the model concerns the minimization of operating theatre costs and patient waiting...

This paper proposes a local search algorithm for a specific combinatorial optimisation problem in graph theory: the Hamiltonian Completion Problem (HCP) on undirected graphs. In this problem, the objective is to add as few edges as possible to a given undirected graph in order to obtain a Hamiltonian graph. This problem has mainly been studied in t...

This paper reports on the Second International Nurse Rostering Competition (INRC-II). Its contributions are (1) a new problem formulation which, differently from INRC-I, is a multi-stage procedure, (2) a competition environment that, as in INRC-I, will continue to serve as a growing testbed for search approaches to the INRC-II problem, and (3) fina...

A pure Nash equilibrium is a famous concept in the field of game theory and has a wide range of applications. In the last decade, a lot of progress has been made in determining the computational complexity of finding equilibria in games. Deciding if a pure Nash equilibrium exists in n-player normal form games and several subclasses has been shown t...

Automatic algorithm configuration (AAC) is an increasingly critical factor in the design of efficient metaheuristics. AAC was previously successfully applied to multi-objective local search (MOLS) algorithms using offline tools. However, offline approaches are usually very expensive, draw general recommendations regarding algorithm design for a giv...

Modern high-performing algorithms are usually highly parameterised, and can be configured either manually or by an automatic algorithm configurator. The algorithm performance dataset obtained after the configuration step can be used to gain insights into how different algorithm parameters influence algorithm performance. This can be done by a numbe...

Automatic algorithm configuration (AAC) is an increasingly critical factor in the design of efficient metaheuristics. AAC was previously successfully applied to multi-objective local search (MOLS) algorithms using offline tools. However, offline approaches are usually very expensive, draw general recommendations regarding algorithm design for a giv...

We consider a single-machine scheduling problem with release dates and inventory constraints. Each job has a deterministic processing time and has an impact (either positive or negative) on the central inventory level. We aim to find a sequence of jobs such that the makespan is minimized while all release dates and inventory constraints are met. We...

The goal of this paper is to investigate the impact of different solution representations, as part of a metaheuristic approach, on net present value optimization in project scheduling. We specifically consider the discrete time/cost trade-off problem with net present value optimization and apply three payment models from literature. Each of these m...

In this paper, we introduce a new variant of the travelling salesman problem, namely the intermittent travelling salesman problem (ITSP), which is inspired by real‐world drilling/texturing applications. In this problem, each vertex can be visited more than once and there is a temperature constraint enforcing a time lapse between two consecutive vis...

Many combinatorial optimisation problems are NP-Hard. Yet in practice high quality solutions are often obtained by (meta)heuristics. These work well in some cases, but not in others, indicating a potential for algorithm selection. In this extended abstract is discussed how to apply algorithm selection to a combinatorial optimisation problem and whi...

The Steiner Tree Problem (STP) in graphs is an important problem with various applications in many areas such as design of integrated circuits, evolution theory, networking, etc. In this paper, we propose an algorithm to solve the STP. The algorithm includes a reducer and a solver using Variable Neighborhood Descent (VND), interacting with each oth...

A set of 23 observations of coronal jet events that occurred in coronal bright points has been analyzed. The focus was on the temporal evolution of the mean brightness before and during coronal jet events. In the absolute majority of the cases either single or recurrent coronal jets were preceded by slight precursor disturbances observed in the mea...

A set of 23 observations of coronal jet events that occurred in coronal bright points has been analyzed. The focus was on the temporal evolution of the mean brightness before and during coronal jet events. In the absolute majority of the cases either single or recurrent coronal jets were preceded by slight precursor disturbances observed in the mea...

Hyper-heuristics are an optimization methodology which ‘search the space of heuristics’ rather than directly searching the space of the underlying candidate-solution representation. Hyper-heuristic search has traditionally been divided into two layers: a lower problem-domain layer (where domain-specific heuristics are applied) and an upper hyper-he...

In this paper we present how the basic building blocks of local search approaches—problem constraints, neighbourhood moves, objective function, move evaluations—can be modelled declaratively using FO(.), an extension of first order logic. We extend the Knowledge Base System IDP with three built-in local search heuristics, namely first improvement,...

This paper presents a Grammatical Evolution framework for the automatic design of Adaptive Evolutionary Algorithms. The grammar adopted by this framework can generate a novel adaptive parameter control strategy, aiming to evolve the design of evolutionary algorithms. The Travelling Salesman Problem is used to investigate the potential of the propos...

Data science and optimisation have evolved separately over several decades.

Over the recent years, several tools for the automated configuration of parameterized algorithms have been developed. These tools, also called configurators, have themselves parameters that influence their search behavior and make them malleable to different kinds of configuration tasks. The default values of these parameters are set manually based...

The present paper discusses results of a statistical study of the characteristics of coronal hole (CH) rotation in order to find connections to the internal rotation of the Sun. The goal is to measure CH rotation rates and study their distribution over latitude and their area sizes. In addition, the CH rotation rates are compared with the solar pho...

The present paper discusses results of a statistical study of the characteristics of coronal hole (CH) rotation in order to find connections to the internal rotation of the Sun. The goal is to measure CH rotation rates and study their distribution over latitude and their area sizes. In addition, the CH rotation rates are compared with the solar pho...

Finding groups of connected individuals in large graphs with tens of thousands or more nodes has received considerable attention in academic research. In this paper, we analyze three main issues with respect to the recent influx of papers on community detection in (large) graphs, highlight the specific problems with the current research avenues, an...

In this research, we discuss the intermittent traveling salesman problem (ITSP), which extends the traditional traveling salesman problem (TSP) by imposing temperature restrictions on each node. These additional constraints limit the maximum allowable visit time per node, and result in multiple visits for each node which cannot be serviced in a sin...

Context. The dynamics of the flaring loops in active region (AR) 11429 are studied. The observed dynamics consist of several evolution stages of the flaring loop system during both the ascending and descending phases of the registered M-class flare. The dynamical properties can also be classified by different types of magnetic reconnection, related...

Context. The dynamics of the flaring loops in active region (AR) 11429 are studied. The observed dynamics consist of several evolution stages of the flaring loop system during both the ascending and descending phases of the registered M-class flare. The dynamical properties can also be classified by different types of magnetic reconnection, related...

Active regions (ARs) are the main sources of variety in solar dynamic events. Automated detection and identification tools need to be developed for solar features for a deeper understanding of the solar cycle. Of particular interest here are the dynamical properties of the ARs, regardless of their internal structure and sunspot distribution. We stu...

Active regions (ARs) are the main sources of variety in solar dynamic events. Automated detection and identification tools need to be developed for solar features for a deeper understanding of the solar cycle. Of particular interest here are the dynamical properties of the ARs, regardless of their internal structure and sunspot distribution. We stu...

The IDP knowledge base system currently uses MiniSAT(ID) as its backend Constraint Programming (CP) solver. A few similar systems have used a Mixed Integer Programming (MIP) solver as backend. However, so far little is known about when the MIP solver is preferable. This paper explores this question. It describes the use of CPLEX as a backend for ID...

We consider a multi-neighborhood local search framework with a large number of possible neighborhoods. Each neighborhood is accompanied by a weight value which represents the probability of being chosen at each iteration. These weights are fixed before the algorithm runs, and can be tuned by off-the-shelf off-line automated algorithm configuration...

We consider a multi-neighborhood local search algorithm with a large number of possible neighborhoods. Each neighborhood is accompanied by a weight value which represents the probability of being chosen at each iteration. These weights are fixed before the algorithm runs, and are considered as parameters of the algorithm. Given a set of instances,...

An antichain of subsets is a set of subsets such that no subset in the antichain is a proper subset of any other subset in the antichain. The Dedekind number counts the total number of antichains of subsets of an n-element set. This paper investigates the interval structure of the lattice of antichains. Several partitioning theorems and counting fo...

Good Laboratory Practice has been a part of non-clinical research for over 40 years yet. Optimization Research, despite having many papers discussing standards being published over the same period of time, has yet to embrace standards that underpin its research. In this paper we argue the need to adopt standards in optimization research. Building o...

Personnel rostering is a personnel scheduling problem in which shifts are assigned to employees, subject to complex organisational and contractual time-related constraints. Academic advances in this domain mainly focus on solving specific variants of this problem using intricate exact or (meta)heuristic algorithms, while little attention has been d...

A new iterative heuristic algorithm, based on Multi Swarm Optimization, is presented for Steiner Tree Problems (STP) in the 2-dimensional Euclidean plane. The basic algorithm is made practical for large instances by applying a result from graph theory, and a well-informed approximation. The algorithm's performance is compared to perfect solutions f...

This paper has been motivated by two observations. First, empirical comparison of algorithms is often carried out in an ad hoc manner. Second, performance data is abundantly generated, yet often not efficiently used. This second observation is particularly valid in the presence of evolutionary computing and other metaheuristic techniques. Inspired...

The formation and dynamics of coronal rain are currently not fully understood. Coronal rain is the fall of cool and dense blobs formed by thermal instability in the solar corona towards the solar surface with acceleration smaller than gravitational free fall. We aim to study the observational evidence of the formation of coronal rain and to trace t...

The Lock Scheduling Problem (LSP) is a combinatorial optimization problem that represents a real challenge for many harbours and waterway operators. The LSP consists of three strongly interconnected subproblems: scheduling lockages, assigning ships to chambers, and positioning the ships inside the chambers. These should be interpreted respectively...

The present paper introduces a learning-based optimization approach to the resource-constrained multi-project scheduling problem. Multiple projects, each with their own set of activities, need to be scheduled. The objectives dealt with here include minimization of the average project delay and total makespan. The availability of local and global re...

In this paper, we provide all information to participate to the Second International Nurse Rostering Competition (INRC-II). First, we describe the problem formulation, which, differently from INRC-I, is a multi-stage procedure. Second, we illustrate all the necessary infrastructure do be used together with the participant's solver, including the te...

A simple model shows how a reasonable update scheme for the probability vector by which a hyper-heuristic chooses the next heuristic leads to neglecting useful mutation heuristics. Empirical evidence supports this on the MaxSat, TravelingSalesman, PermutationFlowshop and VehicleRoutingProblem problems. A new approach to hyper-heuristics is proposed...

The reach of an arc in a network can intuitively be described as an indication of the maximum length of the shortest paths of the digraph that pass through this arc. This concept captures the natural hierarchy of any type of network, in an accurate and comprehensive manner. Traditional reach approximation algorithms compute upper bounds to these re...

Properties of intervals in the lattice of antichains of subsets of a universe of finite size are investigated. New objects and quantitates in this lattice are defined. Expressions and numerical values are deduced for the number of connected antichains and the number of fully distinguishing antichains. The latter establish a connection with Stirling...

In practice nurse rostering problems are often too complex to be expressed through available academic models. Such models are not rich enough to represent the variegated nature of real world scenarios, and therefore have no practical relevance. This article focuses on two particular modelling issues that require careful consideration in making acad...

The ship placement problem constitutes a daily challenge for planners in tide river harbours. In essence, it entails positioning a set of ships into as few lock chambers as possible while satisfying a number of general and specific placement constraints. These constraints make the ship placement problem different from traditional 2D bin packing. A...

The present paper introduces an integrated approach to solving the generalized lock scheduling problem. Three interrelated sub problems can be discerned: ship placement, chamber assignment and lockage operation scheduling. In their turn, these are closely related to the 2D bin packing problem, the assignment problem and the (parallel) machine sched...

This paper investigates the construction of an automatic algorithm selection tool for the multi-mode resource-constrained project scheduling problem (MRCPSP). The research described relies on the notion of empirical hardness models. These models map problem instance features onto the performance of an algorithm. Using such models, the performance o...

In the single-objective automated algorithm configuration problem, given an algorithm with a set of parameters that need to be configured and a distribution of problem instances, the automated algorithm configurator will try to search for a good parameter configuration based on a pre-defined performance measure. In this paper, we point out two moti...

With the term ’anti-monotonic function’, we designate spe- cific boolean functions on subsets of a finite set of positive integers which we call the universe. Through the well-known bijective relationship be- tween the set of monotonic functions and the set of anti-monotonic func- tions, the study of the anti-monotonic functions is equivalent to th...

The best-fit heuristic is a simple and powerful tool for solving the two-dimensional orthogonal strip packing problem. It is the most efficient constructive heuristic on a wide range of rectangular strip packing benchmark problems. In this paper, the results of the original best-fit heuristic are further improved by adding new item orderings and it...

The present study concentrates on the generality of selection hyper-heuristics across various problem domains with a focus on different heuristic sets in addition to distinct experimental limits. While most hyper-heuristic research employs the term generality in describing the potential for solving various problems, the performance changes across d...

This study provides a new hyper-heuristic design using a learning-based heuristic selection mechanism together with an adaptive move acceptance criterion. The selection process was supported by an online heuristic subset selection strategy. In addition, a pairwise heuristic hybridization method was designed. The motivation behind building an intell...

The present article introduces the outdoor activity tour suggestion problem (OATSP). This problem involves finding a closed path of maximal attractiveness in a transportation network graph, given a target path length and tolerance. Total path attractiveness is evaluated as the sum of the average arc attractiveness and the sum of the vertex prizes i...

Nurse rostering is an attractive research domain due to its societal relevance, while academics are intrigued by its combinatorial complexity. Descriptions of nurse rostering problems vary largely across the literature, which makes it almost impossible to track down scientific advances of models and corresponding approaches. The present chapter int...

Many techniques that boost the speed or quality of metaheuristic search have been reported within literature. The present contribution investigates the rather rare combination of reinforcement learning and metaheuristics. Reinforcement learning techniques describe how an autonomous agent can learn from experience. Previous work has shown that a net...

In this paper we present efficient translation schemes for converting nurse rostering problem instances into satisfiability problems (SAT). We define eight generic constraints types allowing the representation of a large number of nurse rostering constraints commonly found in literature. For each of the generic constraint types, we present efficien...

Motivation: Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates-so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined, and some of them have been...