Atle Riise

Local search for the surgery admission planning problem

We present a model for the surgery admission planning problem, and a meta-heuristic algorithm for solving it. The problem involves assigning operating rooms and dates to a set of elective surgeries, as well as scheduling the surgeries of each day and room. Simultaneously, a schedule is created for each surgeon to avoid double bookings. The presented algorithm uses simple Relocate and Two-Exchange neighbourhoods, governed by an iterated local search framework. The problem’s search space associated with these move operators is analysed for three typical fitness surfaces, representing different compromises between patient waiting time, surgeon overtime, and waiting time for children in the morning on the day of surgery. The analysis shows that for the same problem instances, the different objectives give fitness surfaces with quite different characteristics. We present computational results for a set of benchmarks that are based on the admission planning problem in a chosen Norwegian hospital.

Dynamic And Stochastic Vehicle Routing In Practice

The VRP is a key to efficient transportation logistics. It is a computationally very hard problem. Whereas classical OR models are static and deterministic, these assumptions are rarely warranted in an industrial setting. Lately, there has been an increased focus on dynamic and stochastic vehicle routing in the research community. However, very few generic routing tools based on stochastic or dynamic models are available. We illustrate the need for dynamics and stochastic models in industrial routing, describe the Dynamic and Stochastic VRP, and how we have extended a generic VRP solver to cope with dynamics and uncertainty

Modelling and solving generalised operational surgery scheduling problems

Abstract The term ‘surgery scheduling’ is used to describe a variety of strategic, tactical and operational scheduling problems, many of which are critical to the quality of treatment and to the efficient use of hospital resources. We consider operational surgery scheduling problems. The exact problem formulation varies substantially between hospitals or, even, hospital departments. In addition, the level of detail varies between different planning situations, ranging from long term patient admission planning to the very detailed scheduling of a particular day׳s surgeries. This diversity makes it difficult to design general scheduling methods and software solutions that can be applied without extensive customisation for each application. We approach this challenge by proposing a new generalised model for surgery scheduling problems. We show how this model extends the multi-project, multi-mode resource constrained project scheduling problem with generalised time constraints, including some extensions that to our knowledge have not been previously studied. Furthermore, we present a search method for solving the proposed model. The algorithm uses on-line learning to balance computational loads between a construction and an improvement method, both working on high level solution representations. An adapted schedule generation scheme is used to map these to concrete schedules. We perform computational experiments using realistic problem instances from three surgery scheduling planning situations at a medium sized Norwegian hospital; day scheduling, week scheduling and admission planning. The results show that the algorithm performs well across these quite different problems without any off-line customisation or parameter tuning.

A hybrid approach for solving real-world nurse rostering problems

Nurse rostering is the process of creating a plan for nurse working hours over a given time horizon. This problem, most variants of which are NP-hard, has been studied extensively for many years. Still, practical nurse rostering is mostly done manually, often by highly qualified health care personnel. This underlines the need to address the challenges of realistic, applied nurse rostering, and the implementation of advanced rostering methods in commercial software. In this paper, we present an industrial case study of a nurse rostering software currently used in several hospitals and other health care institutions in Norway and Sweden. The presented problem model has a rich set of hard and soft constraints, as required by Norwegian hospitals. Our solution approach is a hybrid: An Iterated Local Search framework that uses Constraint Programming for initial solution construction and diversification, and a Variable Neighborhood Descent for iterative improvement. The search method shows good results in terms of solution quality and computation time on a set of real world instances. We make these test instances available on-line.

Recursive logic-based Benders’ decomposition for multi-mode outpatient scheduling

Efficient outpatient scheduling is becoming increasingly important for the overall cost effectiveness and treatment efficiency of a hospital. We consider a class of multi-mode appointment scheduling problems, with variable resource availability and resource setup times. These problems are frequently found in hospital outpatient clinics, and they are typically hard to solve.

On parallel local search for permutations

We investigate some ways in which massively parallel computing devices can be exploited in local search algorithms. We show that the substantial speedups that can be gained from parallel neighbourhood evaluation enables an efficient best improvement local search, and that this in turn enables further speedups through selection and parallel application of a set of independent, improving moves. Our experiments demonstrate a total speedup of up to several hundred times compared to a classical, sequential best improvement search. We also demonstrate how an exchange of good partial solutions between the incumbent and best found solutions improves the efficiency of the Iterated Local Search algorithm.

The Practice and Theory of Automated Timetabling (2012)

This special volume is focused upon a selection of the papers that were presented at the 9th International Conference on the Practice and Theory of Automated Timetabling (PATAT) in Son, Norway, between 28th and 31st August, 2012. The PATAT conferences are held biennially. This is the third occasion that we have employed the Annals of Operations Research as the forum for a revised and selected collection of papers. The first and second PATAT special volumes of this journal (Volumes 194 and 218) represented the 7th and 8th conferences, held in Montreal in 2008 and in Belfast in 2010, respectively. The PATAT series has been running since 1995. It has been held in Scotland, Canada (twice), Germany, Belgium, USA, Czech Republic, Northern Ireland, Norway, and England. The series acts as a bridge between disciplines and as a bridge between science and practice. Its focus is on all aspects of timetabling research and practice across personnel rostering, educational timetabling, sports scheduling and transportation timetabling. The conference in Son had over 90 delegates from all over the world. We had 4 plenary presentations, 49 standard talks, and 11 practitioner presentations. All the presenters were invited to submit revised papers to this special volume. Additionally, a public announcement was circulated