Welcome to OR&S! Where students, academics and professionals come together

In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities

Fairness aspects in personnel scheduling

In industries like health care, public transport or call centers a shift-based system ensures permanent availability of employees for covering needed services. The resource allocation problem – assigning employees to shifts – is known as personnel scheduling in literature and often aims at minimizing staffing costs. Working in shifts, though, impacts employees’ private lives which adds to the problem of increasing staff shortage in recent years. Therefore, more and more effort is spent on incorporating fairness into scheduling approaches in order to increase employees’ satisfaction. This paper presents a literature review of approaches for personnel scheduling considering fairness aspects. Since fairness is not a quantitative objective, but can be evaluated from different point of views, a large number of fairness measurements exists in the literature. Furthermore, perspective (group vs individual fairness) or time horizon (short-term vs long-term fairness) are often considered very differently. To conclude, we show that a uniform definition and approach for considering fairness in personnel scheduling is challenging and point out gaps for future research

An Integrated Framework for Staffing and Shift Scheduling in Hospitals

Over the years, one of the main concerns confronting hospital management is optimising the staffing and scheduling decisions. Consequences of inappropriate staffing can adversely impact on hospital performance, patient experience and staff satisfaction alike. A comprehensive review of literature (more than 1300 journal articles) is presented in a new taxonomy of three dimensions; problem contextualisation, solution approach, evaluation perspective and uncertainty. Utilising Operations Research methods, solutions can provide a positive contribution in underpinning staffing and scheduling decisions. However, there are still opportunities to integrate decision levels; incorporate practitioners view in solution architectures; consider staff behaviour impact, and offer comprehensive applied frameworks. Practitioners’ perspectives have been collated using an extensive exploratory study in Irish hospitals. A preliminary questionnaire has indicated the need of effective staffing and scheduling decisions before semi-structured interviews have taken place with twenty-five managers (fourteen Directors and eleven head nurses) across eleven major acute Irish hospitals (about 50% of healthcare service deliverers). Thematic analysis has produced five key themes; demand for care, staffing and scheduling issues, organisational aspects, management concern, and technology-enabled. In addition to other factors that can contribute to the problem such as coordination, environment complexity, understaffing, variability and lack of decision support. A multi-method approach including data analytics, modelling and simulation, machine learning, and optimisation has been employed in order to deliver adequate staffing and shift scheduling framework. A comprehensive portfolio of critical factors regarding patients, staff and hospitals are included in the decision. The framework was piloted in the Emergency Department of one of the leading and busiest university hospitals in Dublin (Tallaght Hospital). Solutions resulted from the framework (i.e. new shifts, staff workload balance, increased demands) have showed significant improvement in all key performance measures (e.g. patient waiting time, staff utilisation). Management team of the hospital endorsed the solution framework and are currently discussing enablers to implement the recommendation

Advanced Methods and Models for Employee Timetabling Problems

This thesis is focused on the design of efficient models and algorithms for employee timetabling problems (ETPs). From our point of view, there are two significant gaps in the current state of the art. The first one, also important in practice, concerns the ETP with strongly varying workforce demand. Unlike the classical Nurse Rostering Problem (NRP) this problem considers dozens of shift types that can cover the demand more precisely than early, late and night shift type used in NRP. In this work we call this problem the Employee Timetabling Problem with a High Diversity of shifts (ETPHD). It comes as no surprise that the exact methods like Integer Linear Programming are not able to find its solution in reasonable time. Therefore, a transformation of ETPHD based on mapping of shift types to shift kinds was proposed. The transformation allows one to design a multistage approach (MSA). The aim of the first two stages is to find an initial ETPHD solution, where a rough position of assigned shifts is determined. This proved to be substantial for the last stage of MSA, where the solution is consequently improved in terms of its quality. In order to verify the MSA performance, a cross evaluation methodology was proposed. It is based on the comparison of the performance provided by more approaches on more combinatorial problems. Therefore, real life ETPHD instances from an airport ground company and also standard benchmark NRP instances were considered. The experiments confirmed the better or equal performance of our approach in the most of the cases. The second gap in the literature is an absence of parallel algorithms for ETPs. We focused on the Nurse Rerostering Problem (NRRP) that appears when a disruption in the roster occurs, e.g., when one of the employees becomes sick. For this purpose, the parallel algorithm solving NRRP was proposed in order to shorten needed computational time. This algorithm was designed for a Graphics Processing Unit (GPU) offering a massive parallelization. To the best of our knowledge, this is the first usage of GPU for ETPs. The performance of the GPU parallel algorithm was tested on the real life NRRP benchmark instances and evaluated from two points of view. Firstly, the quality of the results was compared to the known results from the state of the art. Secondly, the speedup achieved by the parallel algorithm related to the sequential one was verified. In average, the parallel algorithm is able to provide the results of the same quality 15 times faster than the sequential one.Katedra řídicí technik

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Copyright @ Springer Science + Business Media. All rights reserved.The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

Rescheduling rehabilitation sessions with answer set programming

The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g. patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular as a consequence of the COVID-19 pandemic that significantly increased rehabilitation’s needs. The problem has been recently successfully solved via a two-phase solution based on answer set programming (ASP). In this paper, we focus on the problem of rescheduling the rehabilitation sessions, which comes into play when the original schedule cannot be implemented, for reasons that involve the unavailability of operators and/or the absence of patients. We provide rescheduling solutions based on ASP for both phases, considering different scenarios. Results of experiments performed on real benchmarks, provided by ICS Maugeri, show that also the rescheduling problem can be solved in a satisfactory way. Finally, we present a web application that supports the usage of our solution