Sara Ceschia

Design, engineering, and experimental analysis of a simulated annealing approach to the post-enrolment course timetabling problem

The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this work we design a metaheuristic approach based on simulated annealing to solve the PE-CTT. We consider all the different variants of the problem that have been proposed in the literature and we perform a comprehensive experimental analysis on all the available public instances. The outcome is that our solver, properly engineered and tuned, performs very well on all cases, providing the new best known results on many instances and state-of-the-art values for the others.

Local search and lower bounds for the patient admission scheduling problem

We propose a multi-neighborhood local search procedure to solve a healthcare problem, known as the Patient Admission Scheduling problem. We design and experiment with different combinations of neighborhoods, showing that they have diverse effectiveness for different sets of weights of the cost components that constitute the objective function. We also compute many lower bounds based on the relaxation of some constraints. The outcome is that our results compare favorably with the previous work on the problem, improving all available instances, and in some cases are also quite close to the lower bounds. Finally, we propose the application of the technique to the dynamic case, in which admission and discharge dates are not predictable in advance.

Local search for a multi-drop multi-container loading problem

We consider a complex variant of the Container Loading Problem arising from a real-world industrial application. It includes several features such as multiple containers, box rotation, and bearable weight, which are of importance in many practical situations. In addition, it also considers the situation in which boxes have to be delivered to different destinations (multi-drop).Our solution technique is based on local search metaheuristics. Local search works on the space of sequences of boxes to be loaded, while the actual load is obtained by invoking, at each iteration, a specialized procedure called loader. The loader inserts the boxes in the container using a deterministic heuristic which produces a load that is feasible according to the constraints.We test our solver on real-world instances provided by our industrial partner, showing a clear improvement on the previous heuristic solution. In addition, we compare our solver on benchmarks from the literature on the basic container loading problems. The outcome is that the results are in some cases in-line with the best ones in the literature and for other cases they also improve upon the best known ones. All instances and solutions are made available on the web for future comparisons.

Modeling and solving the dynamic patient admission scheduling problem under uncertainty

OBJECTIVE Our goal is to propose and solve a new formulation of the recently-formalized patient admission scheduling problem, extending it by including several real-world features, such as the presence of emergency patients, uncertainty in the length of stay, and the possibility of delayed admissions. METHOD We devised a metaheuristic approach that solves both the static (predictive) and the dynamic (daily) versions of this new problem, which is based on simulated annealing and a complex neighborhood structure. RESULTS The quality of our metaheuristic approach is compared with an exact method based on integer linear programming. The main outcome is that our method is able to solve large cases (up to 4000 patients) in a reasonable time, whereas the exact method can solve only small/medium-size instances (up to 250 patients). For such datasets, the two methods obtain results at the same level of quality. In addition, the gap between our (dynamic) solver and the static one, which has all information available in advance, is only 4-5%. Finally, we propose (and publish on the web) a large set of new instances, and we discuss the impact of their features in the solution process. CONCLUSION The metaheuristic approach proved to be a valid search method to solve dynamic problems in the healthcare domain.

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison.

Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs

In this work we formalize a new complex variant of the classical vehicle routing problem arising from a real-world application. Our formulation includes a heterogeneous fleet, a multi-day planning horizon, a complex carrier-dependent cost for vehicles, and the possibility of leaving orders unscheduled.For tackling this problem we propose a metaheuristic approach based on Tabu Search and on a combination of neighborhood relations. We perform an experimental analysis to tune and compare different combinations, highlighting the most important features of the algorithm.The outcome is that a significant improvement is obtained by a complex combination of neighborhood relations.In addition, we compare our solver with previous work on public benchmarks of a similar version of the problem, namely the Vehicle Routing Problem with Private fleet and Common carrier. The conclusion is that our results are competitive with the best ones in literature.

Local search techniques for a routing-packing problem

We propose a complex real-world problem in logistics that integrates routing and packing aspects. It can be seen as an extension of the Three-Dimensional Loading Capacitated Vehicle Routing Problem (3L-CVRP) introduced by Gendreau, Iori, Laporte, and Martello (2006). The 3L-CVRP consists in finding a set of routes that satisfies the demand of all customers, minimizes the total routing cost, and guarantees a packing of items that is feasible according to loading constraints. Our problem formulation includes additional constraints in relation to the stability of the cargo, to the fragility of items, and to the loading and unloading policy. In addition, it considers the possibility of split deliveries, so that each customer can be visited more than once. We propose a local search approach that considers the overall problem in a single stage. It is based on a composite strategy that interleaves simulated annealing with large-neighborhood search. We test our solver on 13 real-world instances provided by our industrial partner, which are very diverse in size and features. In addition, we compare our solver on benchmarks from the literature of the 3L-CVRP showing that our solver performs well compared to other approaches proposed in the literature.

Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays

We revisit and extend the patient admission scheduling problem, in order to make it suitable for practical applications. The main novelty is that we consider constraints on the utilisation of operating rooms for patients requiring a surgery. In addition, we propose a more elaborate model that includes a flexible planning horizon, a complex notion of patient delay, and new components of the objective function. We design a solution approach based on local search, which explores the search space using a composite neighbourhood. In addition, we develop an instance generator that uses real-world data and statistical distributions so as to synthesise realistic and challenging case studies, which are made available on the web along with our solutions and the validator. Finally, we perform an extensive experimental evaluation of our solution method including statistically principled parameter tuning and an analysis of some features of the model and their corresponding impact on the objective function.

Multi-neighborhood Local Search for the Patient Admission Problem

We propose a multi-neighborhood local search procedure to solve a healthcare problem, known as the Patient Admission problem. We design and experiment different combinations of neighborhoods, showing that they have diverse effectiveness for different sets of weights of the cost components that constitute the objective function. We also compute some lower bounds on benchmark instances based on the relaxation of some constraints and the solution of a minimum-cost maximum-cardinality matching problem on a bipartite graph. The outcome is that our results compare favorably with the previous work on the problem, improving on all the available instances, and are also quite close to the computed lower bounds.

The Second International Nurse Rostering Competition

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) final results of the competition. We discuss also the competition environment, which is an infrastructure including problem and instance definitions, testbeds, validation/simulation tools and rules. The hardness of the competition instances has been evaluated through the behaviour of our own solvers, and confirmed by the solvers of the participants. Finally, we discuss general issues about both nurse rostering problems and optimisation competitions in general.