Túlio Toffolo

Integer programming techniques for the nurse rostering problem

This work presents integer programming techniques to tackle the problem of the International Nurse Rostering Competition. Starting from a compact and monolithic formulation in which the current generation of solvers performs poorly, improved cut generation strategies and primal heuristics are proposed and evaluated. A large number of computational experiments with these techniques produced the following results: the optimality of the vast majority of instances was proved, the best known solutions were improved by up to 15xa0% and strong dual bounds were obtained. In the spirit of reproducible science, all code was implemented using the Computational Infrastructure for Operations Research.

GOAL solver: a hybrid local search based solver for high school timetabling

This work presents a local search approach to the High School Timetabling Problem. The addressed timetabling model is the one stated in the Third International Timetabling Competition (ITC 2011), which considered many instances from educational institutions around the world and attracted seventeen competitors. Our team, named GOAL (Group of Optimization and Algorithms), developed a solver built upon the Kingston High School Timetabling Engine. Several neighborhood structures were developed and used in a hybrid metaheuristic based on Simulated Annealing and Iterated Local Search. The developed algorithm was the winner of the competition and produced the best known solutions for almost all instances.

A SA-VNS approach for the High School Timetabling Problem

The High School Timetabling Problem consists in assigning timeslots and resources to events, satisfying constraints which heavily depend on the specific institution. This work deals with the problem of the ongoing III International Timetabling Competition (ITC), which includes a diverse set of instances from many educational institutions around the world. We proposed an approach based on Simulated Annealing and Variable Neighborhood Search metaheuristics. One important structural feature of our approach is the use of the Kingston’s High School Timetabling Engine (KHE) to generate initial solutions combined with the multi-neighborhood search. Such approach led us to the finals of the ongoing competition.

An integer programming approach to the multimode resource-constrained multiproject scheduling problem

The project scheduling problem (PSP) is the subject of several studies in computer science, mathematics, and operations research because of the hardness of solving it and its practical importance. This work tackles an extended version of the problem known as the multimode resource-constrained multiproject scheduling problem. A solution to this problem consists of a schedule of jobs from various projects, so that the job allocations do not exceed the stipulated limits of renewable and nonrenewable resources. To accomplish this, a set of execution modes for the jobs must be chosen, as the jobs’ duration and amount of needed resources vary depending on the mode selected. Finally, the schedule must also consider precedence constraints between jobs. This work proposes heuristic methods based on integer programming to solve the PSP considered in the Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA) 2013 Challenge. The developed solver was ranked third in the competition, being able to find feasible and competitive solutions for all instances and improving best known solutions for some problems.

Branch-and-bound with decomposition-based lower bounds for the Traveling Umpire Problem

The Traveling Umpire Problem (TUP) is an optimization problem in which umpires have to be assigned to games in a double round robin tournament. The objective is to obtain a solution with minimum total travel distance over all umpires, while respecting hard constraints on assignments and sequences. Up till now, no general nor dedicated algorithm was able to solve all instances with 12 and 14 teams. We present a novel branch-and-bound approach to the TUP, in which a decomposition scheme coupled with an efficient propagation technique produces the lower bounds. The algorithm is able to generate optimal solutions for all the 12- and 14-team instances as well as for 11 of the 16-team instances. In addition to the new optimal solutions, some new best solutions are presented and other instances have been proven infeasible.

Analysis of stochastic local search methods for the unrelated parallel machine scheduling problem

This work addresses the unrelated parallel machine scheduling problem with sequence-dependent setup times, in which a set of jobs must be scheduled for execution by one of the several available machines. Each job has a machine-dependent processing time. Furthermore, given multiple jobs, there are additional setup times, which vary based on the sequence and machine employed. The objective is to minimize the schedules completion time (makespan). The problem is NP-hard and of significant practical relevance. The present paper investigates the performance of four different stochastic local search (SLS) methods designed for solving the particular scheduling problem: simulated annealing, iterated local search, late acceptance hill-climbing, and step counting hill-climbing. The analysis focuses on design questions, tuning effort, and optimization performance. Simple neighborhood structures are considered. All proposed SLS methods performed significantly better than two state-of-the-art hybrid heuristics, especially for larger instances. Updated best-known solutions were generated for 901 of the 1000 large benchmark instances considered, demonstrating that particular SLS methods are simple yet powerful alternatives to current approaches for addressing the problem. Implementations of the contributed algorithms have been made available to the research community.