Thiago F. Noronha

Routing and wavelength assignment by partition colouring

The problem of routing and wavelength assignment in all-optical networks may be solved by a combined approach involving the computation of alternative routes for the lightpaths, followed by the solution of a partition colouring problem in a conflict graph. A new tabu search heuristic is also proposed for the partition colouring problem, which may be viewed as an extension of the graph colouring problem. Computational experiments are reported, showing that the tabu search heuristic outperforms the best heuristic for partition colouring by approximately 20% in the average and illustrating that the new approach for the problem of routing and wavelength assignment is more robust than a well established heuristic for this problem.

A biased random-key genetic algorithm for routing and wavelength assignment

The problem of routing and wavelength assignment in wavelength division multiplexing optical networks consists in routing a set of lightpaths and assigning a wavelength to each of them, such that lightpaths whose routes share a common fiber are assigned different wavelengths. This problem was shown to be NP-hard when the objective is to minimize the total number of wavelengths used. We propose a genetic algorithm with random keys for routing and wavelength assignment with the goal of minimizing the number of different wavelengths used in the assignment. This algorithm extends the best heuristic in the literature by embedding it into an evolutionary framework. Computational results show that the new heuristic improves the state-of-the-art algorithms in the literature.

Iterated local search heuristics for the Vehicle Routing Problem with Cross-Docking

This work addresses the Vehicle Routing Problem with Cross-Docking (VRPCD). The problem consists in defining a minimum cost set of routes for a fleet of vehicles that meets the demands of products for a set of suppliers and customers. The vehicles leave a single Cross-Dock (CD) towards the suppliers, pick up products and return to the CD, where products can be exchanged before being delivered to their customers. The vehicle routes must respect the vehicle capacity constraints, as well as the time window constraints. We adapted a constructive heuristic and six local search procedures from the literature of VRP, and made them efficient in the presence of the synchronization constraints of VRPCD. Besides, we propose three Iterated Local Search (Lourenco et al., 2010) heuristics for VRPCD. The first heuristic is a standard implementation of ILS, while the second extends the classic ILS framework by keeping a set of elite solutions, instead of a single current solution. The latter set is used in a restart procedure. As far as we can tell, this is the first ILS heuristic in the literature that keeps a population of current elite solutions. The third heuristic is an extension of the second that relies on an intensification procedure based on an Integer Programming formulation for the Set Partitioning problem. The latter allows a neighborhood with an exponential number of neighbors to be efficiently evaluated. We report computational results and comparisons with the best heuristics in the literature. Besides, we also present a new set with the largest instances in the literature of VRPCD, in order to demonstrate that the improvements we propose for the ILS metaheuristic are efficient even for large size instances. Results show that the best of our heuristics is competitive with the best heuristics in the literature of VRPCD. Besides, it improved the best solution known for half of the benchmark instances in the literature.

A hybrid heuristic for a multi-objective real-life car sequencing problem with painting and assembly line constraints

We address a multi-objective version of the car sequencing problem, which consists in sequencing a given set of cars to be produced in a single day, minimizing the number of violations of assembly constraints and the number of paint color changes in the production line. We propose a set of heuristics for approximately solving this problem, based on the paradigms of the VNS and ILS metaheuristics, to which further intensification and diversification strategies have been added. Computational results on real-life test instances are reported. The work presented in this paper obtained the second prize in the ROADEF challenge 2005 sponsored by Renault.

A branch-and-cut algorithm for scheduling the highly-constrained Chilean soccer tournament

The qualifying phase of the Chilean soccer championship follows the structure of a compact single round robin tournament. Good schedules are of major importance for the success of the tournament, making them more balanced, profitable, and attractive. The schedules were prepared by ad hoc procedures until 2004, when a rough integer programming strategy was proposed. In this work, we improve the original integer programming formulation. We derive valid inequalities for improving the linear relaxation bound and we propose a new branch-and- cut strategy for the problem. Computational results on a real-life instance illustrate the effectiveness of the approach and the improvement in solution quality.

A branch-and-cut algorithm for the equitable coloring problem using a formulation by representatives

An equitable k-coloring of a graph is defined by a partition of its vertices into k disjoint stable subsets, such that the difference between the cardinalities of any two subsets is at most one. The equitable coloring problem consists of finding the minimum value of k such that a given graph can be equitably k-colored. We present two new integer programming formulations based on representatives for the equitable coloring problem. We propose a primal constructive heuristic, branching strategies, and the first branch-and-cut algorithm in the literature of the equitable coloring problem. The computational experiments were carried out on randomly generated graphs, DIMACS graphs, and other graphs from the literature.

Efficient implementations of heuristics for routing and wavelength assignment

The problem of Routing and Wavelength Assignment in Wavelength Division Multiplexing (WDM) optical networks consists in routing a set of lightpaths and assigning a wavelength to each of them, such that lightpaths whose routes share a common fiber are assigned to different wavelengths. When the objective is to minimize the total number of wavelengths used, this problem is NP-hard. The current state-of-the-art heuristics were proposed in 2007 by Skorin-Kapov. The solutions provided by these heuristics were near-optimal. However, the associated running times reported were high. In this paper, we propose efficient implementations of these heuristics and reevaluate them on a broader set of testbed instances.

A biased random-key genetic algorithm for single-round divisible load scheduling

A divisible load is an amount W of computational work that can be arbitrarily divided into chunks and distributed among a set P of worker processors to be processed in parallel. Divisible load applications occur in many fields of science and engineering. They can be parallelized in a master-worker fashion, but they pose several scheduling challenges. The divisible load scheduling problem consists in (a) selecting a subset of active workers, (b) defining the order in which the chunks will be transmitted to each of them, and (c) deciding the amount of load that will be transmitted to each worker , with , so as to minimize the makespan, i.e., the total elapsed time since the master began to send data to the first worker, until the last worker stops its computations. In this work, we propose a biased random-key genetic algorithm for solving the divisible load scheduling problem. Computational results show that the proposed heuristic outperforms the best heuristic in the literature.

Constraint Programming for the Diameter Constrained Minimum Spanning Tree Problem

Abstract We propose a new formulation for the Diameter Constrained Minimum Spanning Tree Problem using constraint programming. Computational results have shown that this formulation combined with an appropriate search procedure solves larger instances and is faster than the other approaches in the literature.

Biased random-key genetic algorithm for fiber installation in Optical Network Optimization

The problem of Fiber Installation in Optical Network Optimization consists in routing a set of lightpaths (all-optical connections), such that the cost of the optical components necessary to operate the network is minimized. We propose a genetic algorithm with random keys that extends the best heuristic in the literature by embedding it into an evolutionary framework. Computational results showed that the new heuristic improves the best heuristic in the literature.