Stefan Creemers

• Professor
• Professor (Full) at IESEG School of Management

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubi...

Quantum annealing emerges as a viable solution for solving complex problems such as the resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer Linear Programming formulations in an attempt to solve the RCPSP, and convert the most qubit-efficient formulation into a Quadratic Unconstrained Binary Optimization (QUBO) mode...

We investigate the limitations of existing quantum algorithms to solve discrete optimization problems. First, we discuss the quantum counting algorithm of Brassard et al. (1998), and show that it has performance that is equivalent to that of a brute-force approach when approximating the number of valid solutions. In addition, we show that a straigh...

In part I of this paper, we introduce the basics of quantum computing, and use Grover's algorithm as a subroutine in a binary search procedure that can solve any discrete optimization problem. In part II, we improve the performance of this binary search procedure. For this purpose, we propose two new procedures, and use them to solve 66000 instance...

We propose a new method to evaluate any stationary joint replenishment policy under compound Poisson demand. The method makes use of an embedded Markov chain that only considers the state of the system after an order is placed. The resulting state space reduction allows exact analysis of instances that until now could only be evaluated using approx...

Quantum computing has sparked a tremendous interest from governments, academics, and the private sector alike. In spite of this, quantum computing has received only limited attention in the Operations Research (OR) community. This is somewhat surprising given the potential that has been ascribed to quantum computers to solve complex OR problems. To...

Appointment scheduling rules are used to determine when a customer is to receive service during a service session. In general, appointment scheduling rules do not consider the sequencing of individual customers, but provide simple guidelines on how to assign appointment times to a set of (arriving) customers. Many appointment scheduling rules exist...

Preemption (or the splitting of activities) is a common practice in many project environments, and has been a standard feature of commercial project management software packages for years. Despite its prevalence in daily practice, preemption has received little attention in the project scheduling literature. A possible explanation for this lack of...

The goal of sequential testing is to discover the state of a system by testing its components one by one. We consider n-out-of-n systems, which function only if all n components work. The testing continues until the system state (up or down) is identified. The tests have known execution costs and failure probabilities, and are subject to precedence...

We tackle precedence-constrained sequencing on a single machine in order to minimize total weighted tar-diness. Classic dynamic programming (DP) methods for this problem are limited in performance due to excessive memory requirements, particularly when the precedence network is not sufficiently dense. Over the last decades, a number of precedence t...

We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a-priori decisions...

We study the Net Present Value (NPV) of a project with multiple stages that are executed in sequence. A cash flow (positive or negative) may be incurred at the start of each stage, and a payoff is obtained at the end of the project. The duration of a stage is a random variable with a general distribution function. For such projects, we obtain exact...

The goal of sequential testing is to discover the state of a system by testing its components one by one. We consider n-out-of-n systems, which function only if all n components work. The testing continues until the system state (up or down) is identified. The tests have known execution costs and failure probabilities and may be subject to preceden...

We study the integration of order acceptance and capacity planning in multi-project environments with dynamically arriving projects. We model this planning problem as a continuous-time Markov decision process to determine long-term optimal decisions. We examine whether macro-process planning should be performed before or after order acceptance. We...

We study projects with activities that have stochastic durations that are modeled using phase-type distributions. Intermediate cash flows are incurred during the execution of the project. Upon completion of all project activities a payoff is obtained. Because activity durations are stochastic, activity starting times cannot be defined at the start...

Schrage and Baker (1978) proposed a generic dynamic programming (DP) algorithm to tackle precedence-constrained sequencing on a single machine. The performance of their DP method, however, is limited due to excessive memory requirements, particularly when the precedence network is not very dense. Emmons (1969) and Rinnooy Kan et al. (1975) describe...

Collaborative shipping programs, whereby companies bundle their transport loads, are a growing trend in logistics. By bundling shipments with other partners, available space in truck hauls for one company can be used to transport shipments for other companies. The benefits are reduced logistics costs and a lower carbon footprint. Although the advan...

We tackle precedence-constrained sequencing on a single machine in order to minimize total weighted tardiness. Classic dynamic programming (DP) methods for this problem are limited in performance due to excessive memory requirements, particularly when the precedence network is not sufficiently dense. Over the last decades, a number of precedence th...

In this article we study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a superset of most of the existing classes that are available in the literature. A policy in this new class makes a n...

In this article we study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a superset of most of the existing classes that are available in the literature. A policy in this new class makes a n...

In this article, we approach the Examination-Timetabling Problem (ETP) from a student-centric point of view. We allow for multiple versions of an exam to be scheduled to increase the spreading of exams for students. We propose two Column Generation (CG) algorithms. In the first approach, a column is defined as an exam schedule for every unique stud...

We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a-priori decisions...

We present a globally optimal solution procedure to tackle the preemptive stochastic resource-constrained project scheduling problem (PSRCPSP). A solution to the PSRCPSP is a policy that allows to construct a precedence- and resource-feasible schedule that minimizes the expected makespan of a project. The PSRCPSP is an extension of the stochastic r...

We study the Net Present Value (NPV) of a project with multiple stages that are executed in sequence. A cash flow (positive or negative) may be incurred at the start of each stage, and a payoff is obtained at the end of the project. The duration of a stage is a random variable with a general distribution function. For such projects, we obtain exact...

The resource-constrained project scheduling problem (RCPSP) has been widely studied. A fundamental assumption of the basic type of RCPSP is that activity durations are deterministic (i.e., they are known in advance). In reality, however, this is almost never the case. In this article we illustrate why it is important to incorporate activity duratio...

We investigate project scheduling with stochastic activity durations to maximize the expected net present value. Individual activities also carry a risk of failure, which can cause the overall project to fail. In the project planning literature, such technological uncertainty is typically ignored and project plans are developed only for scenarios i...

The Resource-Constrained Project Scheduling Problem (RCPSP) has been widely studied. A fundamental assumption of the RCPSP is that the activity durations are known before their execution. In reality, however, this is almost never the case. In this article, we illustrate why it is important to incorporate activity duration variability and develop an...

The goal of project risk management is to mitigate the impact of risks on project objectives such as budget and time. A popular approach to determine where to focus mitigation efforts, is the use of so-called "ranking indices". Ranking indices produce a ranking of activities (or even better, risks) based on their impact on project objectives. In tu...

Project risk management aims to provide insight into the risk profile of a project as to facilitate decision makers to mitigate the impact of risks on project objectives such as budget and time. A popular approach to determine where to focus mitigation efforts, is the use of so-called ranking indices (e.g. the criticality index, the significance in...

We present a Markov model to analyze the queueing behavior of the nonstationary G(t)/G(t)/s(t)+G(t) queue. We assume an exhaustive service discipline (where servers complete their current service before leaving) and use acyclic phase-type distributions to approximate the general interarrival, service, and abandonment time distributions. The time-va...

We present a Markov model to approximate the queueing behavior at the G(t)/G(t)/s(t)+G(t) queue with exhaustive discipline and abandonments. The performance measures of interest are: (1) the average number of customers in queue, (2) the variance of the number of customers in queue, (3) the average number of abandonments and (4) the virtual waiting...

We investigate project scheduling with stochastic activity durations to maximize the expected net present value. Individual activities also carry a risk of failure, which can cause the overall project to fail. In the project planning literature, such technological uncertainty is typically ignored and project plans are developed only for scenarios i...

Purpose The production dice game is a powerful learning exercise focusing on the impact of variability and dependency on throughput and work‐in‐process inventory of flow lines. This paper seeks to extend the basic dice game along the following lines. First, it will allow operations to take place concurrently as opposed to sequentially, which works...

Appointment scheduling rules are used to determine when a customer is to receive service. Many appointment scheduling rules exist and are being used in practice (e.g., in healthcare and legal services). Which appointment scheduling rule is best, however, is still an open question. In order to answer this question, we develop an analytical model tha...

We present a model for assigning server time slots to different classes of patients. The objective is to minimize the total expected weighted waiting time of a patient (where different patient classes may be assigned different weights). A bulk service queueing model is used to obtain the expected waiting time of a patient of a particular class, giv...

The goal of project risk management is to provide insight into the risk profile of a project as to facilitate decision makers to mitigate the impact of risks on project objectives such as budget and time. A popular approach to determine where to focus mitigation efforts, is the use of so-called ranking indices. Ranking indices allow the ranking of...

We look into project scheduling with expected-NPV objective and stochastic activity durations. Individual activities carry a risk of failure, and an activity's failure can cause the overall project to fail. More than one alternative may exist for reaching intermediate project deliverables, and these alternatives can be implemented either in paralle...

The health care sector is a fast-growing segment of GNP in almost every economy. No wonder that we witnessed a tremendous increase in research to improve both medical practice and management practice. Patient flow management is an example of such a management practice and represents the ability of the health care system to serve patients quickly, r...

Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing network of a general class of healthcare systems. In order to analyze such networks, we use the parametric decomposition approach. Using this approach the network is decomposed...

Many service systems are appointment-driven. In such systems, customers make an appointment and join an external queue (also referred to as the “waiting list”). At the appointed date, the customer arrives at the service facility, joins an internal queue and receives service during a service session. After service, the customer leaves the system. Im...

We examine project scheduling with net present value objective and exponential activity durations, using a continuous-time Markov decision chain. On the basis of a judicious partitioning of the state space, we achieve a significant performance improvement as compared to the existing algorithms.

We study project scheduling when individual activities carry a risk of failure, and where an activity's failure may lead to the project's overall failure. In the project planning and scheduling literature, this technological uncertainty has typically been ignored and project plans are developed only for scenarios in which the project succeeds. To m...

The production dice game is a powerful learning exercise focusing on the impact of variability and dependency on throughput and work-in-process inventory of flow lines. In this paper we will extend the basic dice game along the following lines. First, we allow that the operations take place concurrently as opposed to the more traditional way of pla...

Many service systems are appointment-driven. In such systems, customers make an appointment and join an external queue (also referred to as the “waiting list”). At the appointed date, the customer arrives at the service facility and receives service. Important measures of interest include the size of the waiting list as well as the time spent in th...

The literature on project scheduling with uncertain activity durations is still in its burn-in phase. We examine project scheduling with net-present-value objective and exponential activity durations by means of a backward stochastic dynamic programming recursion. We examine the particular setting in which the individual activities carry a risk of...

We examine project scheduling with net-present-value objective and exponential activity durations, using a continuous-time Markov decision chain. Based on a judicious partitioning of the state space, we achieve a significant performance improvement compared to the existing algorithms.

Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing model of a general class of healthcare systems. We develop new expressions to assess the impact of service outages and use the resulting model to approximate patient flow times...

The performance of health care systems in terms of patient flow times and utilization of critical resources can be assessed through queueing and simulation models. We model the orthopaedic department of the Middelheim hospital (Antwerpen, Belgium) focusing on the impact of outages (preemptive and nonpreemptive outages) on the effective utilization...

Many service systems require customers to make an appointment prior to receiving service. After making an appointment, customers joi n a queue and wait for the appointment date to take place. We refer to such systems as appointment -driven queueing systems. Appointment- driven queueing systems may be found in many services and manufacturing industr...