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A matheuristic approach for the Pollution-Routing Problem

This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by Bektas and Laporte (2011) [Transportation Research Part B: Methodological 45 (8), 1232–1250]. The objective is to minimize operational and environmental costs while respecting capacity constraints and service time windows. Costs are based on driver wages and fuel consumption, which depends on many factors, such as travel distance and vehicle load. The vehicle speeds are considered as decision variables. They complement routing decisions, impacting the total cost, the travel time between locations, and thus the set of feasible routes. We propose a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm. This hybridization enables to integrate more tightly route and speed decisions. Moreover, two other “green” VRP variants, the Fuel Consumption VRP (FCVRP) and the Energy Minimizing VRP (EMVRP), are addressed, as well as the VRP with time windows (VRPTW) with distance minimization. The proposed method compares very favorably with previous algorithms from the literature, and new improved solutions are reported for all considered problems.

An efficient heuristic for the ring star problem

In this paper, we consider a combinatorial optimization problem that arises in the design of telecommunications network. It is known as the Ring Star Problem. In this problem the aim is to locate a simple cycle through a subset of vertices of a graph with the objective of minimizing the sum of two costs: a routing cost proportional to the length of the cycle, and an assignment cost from the vertices not in the cycle to their closest vertex on the cycle. We propose a new hybrid metaheuristic approach to solve the Ring Star Problem. In the hybrid metaheuristic, we use a General Variable Neighborhood Search (GVNS) to improve the quality of the solution obtained with a Greedy Randomized Adaptive Search Procedure (GRASP). A set of extensive computational experiments on instances from the classical TSP library and randomly generated are reported, comparing the GRASP/GVNS heuristic with other heuristic found in the literature. These results indicate that the proposed hybrid metaheuristic is highly efficient and superior to the other available method proposed for the Ring Star Problem.

TDR: A Distributed-Memory Parallel Routing Algorithm for FPGAs

This paper proposes a distributed-memory parallel routing algorithm for FPGAs based on the partitioning of the routing graph under special FPGA architectural constraints. Coarse-grain parallelism is adopted by assigning different processors to the routing of nets within different partitions of the routing graph. The experimental results have demonstrated that TDR can achieve linear and superlinear speedups in relation to the state-of-art VPR router even when it is running on an Ethernet cluster of workstations. However, it usually requires more tracks than VPR for routing the same circuit.

An ILS based heuristic for the vehicle routing problem with simultaneous pickup and delivery and time limit

This invention relates to the preparation of a flexible, open cell polyurethane foam which can be rigidized by incorporating an epoxy resin into the mixture prior to foaming and, after forming a flexible foam, adding an epoxy curing agent thereto to cause the epoxy resin to cure thereby rigidizing the previously flexible foam. The flexible foam can be reshaped prior to rigidizing if desired.

GRASP-UTS: an algorithm for unsupervised trajectory segmentation

An important problem in the knowledge discovery of trajectories is segmentation in subparts (subtrajectories). Existing algorithms for trajectory segmentation generally use explicit criteria to create segments. In this article, we propose segmenting trajectories using a novel, unsupervised approach, in which no explicit criteria are predetermined. To achieve this, we apply the Minimum Description Length (MDL) principle, which can measure homogeneity in the trajectory data by computing the similarities between landmarks (i.e. representative points of the trajectory) and the points in their neighborhood. Based on the homogeneity measurements, we propose an algorithm named Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation (GRASP-UTS), which is a meta-heuristic that builds segments by modifying the number and positions of landmarks. We perform experiments with GRASP-UTS in two real-world datasets, using segment purity and coverage metrics to evaluate its efficiency. Experimental results demonstrate that GRASP-UTS correctly segmented sample trajectories without predetermined criteria, by computing similarities between landmarks and other trajectory points.

DC-GRASP: directing the search on continuous-GRASP

Several papers in the scientific literature use metaheuristics to solve continuous global optimization. To perform this task, some metaheuristics originally proposed for solving combinatorial optimization problems, such as Greedy Randomized Adaptive Search Procedure (GRASP), Tabu Search and Simulated Annealing, among others, have been adapted to solve continuous global optimization problems. Proposed by Hirsch et al., the Continuous-GRASP (C-GRASP) is one example of this group of metaheuristics. The C-GRASP is an adaptation of GRASP proposed to solve continuous global optimization problems under box constraints. It is simple to implement, derivative-free and widely applicable method. However, according to Hedar, due to its random construction, C-GRASP may fail to detect promising search directions especially in the vicinity of minima, which may result in a slow convergence. To minimize this problem, in this paper we propose a set of methods to direct the search on C-GRASP, called Directed Continuous-GRASP (DC-GRASP). The proposal is to combine the ability of C-GRASP to diversify the search over the space with some efficient local search strategies to accelerate its convergence. We compare the DC-GRASP with the C-GRASP and other metaheuristics from literature on a set of standard test problems whose global minima are known. Computational results show the effectiveness and efficiency of the proposed methods, as well as their ability to accelerate the convergence of the C-GRASP.

A parallel hybrid metaheuristic for bicluster editing

The bicluster editing problem (BEP) consists of editing (adding or removing) the edges of a bipartite graph G in order to transform it into a vertex-disjoint union of complete bipartite subgraphs, in such a way that the sum of the weights of the edited edges is minimum. In this paper, we propose five parallel strategies for the implementation of a hybrid metaheuristic for the BEP, consisting of a GRASP with VNS as local search. Computational experiments show near-linear speedups on Linux cluster with 64 processors and better solutions than those of the sequential approach.

Hybrid Metaheuristic for Bicluster Editing Problem

The NP-hard Bicluster Editing Problem consists of adding and/or removing at most k edges to make a bipartite graph G=(V,E) a vertex-disjoint union of complete bipartite subgraphs. It has applications in the analysis of gene expression data. We propose the metaheuristics GRASP and VNS, and their hybridization, for the Bicluster Editing Problem, as well as a heuristic construction based on intersection neighborhood set.

A hybrid metaheuristic for the minimum labeling spanning tree problem

Abstract An edge-labeled graph (ELG) is a graph in which each edge has a label associated. Given G, an ELG, the minimum labeling spanning tree problem (MLSTP) is an NP-hard problem that consists in finding a spanning tree in G by using a minimum number of labels. The MLSTP has applications in areas such as computer networks, multimodal transportation networks, and data compression. This paper introduces new concepts, presents a revised version of the maximum vertex covering algorithm and provides a tighter bound to its time complexity. Further, a new MIP-based metaheuristic is proposed for solving the MLSTP, the multi-start local branching (MSLB). It combines the efficiency of the proposed constructive heuristics with the capacity of exploration of a new local search method based on MIP. The computational experiments performed show that the MSLB is superior to the current state-of-the-art metaheuristics in respect to quality of the solutions and processing times.

Efficient heuristics for the minimum labeling global cut problem

Abstract Let G = ( V , E , L ) be an edge-labeled graph. Let V be the set of vertices of G, E the set of edges, L the set of labels (colors) such that each edge e ∈ E has an associated label L ( e ) . The goal of the minimum labeling global cut problem (MLGCP) is to find a subset L ′ ⊆ L of labels such that G ′ = ( V , E ′ , L \ L ′ ) is not connected and | L ′ | is minimized. In this work, we generate random instances for the MLGCP to perform empirical tests. Also propose a set of heuristics using concepts of Genetic Algorithm and metaheuristic VNS, including some of their procedures, like two local search moves, and an auxiliary data structure to speed up the local search. Computational experiments show that the metaheuristic VNS outperforms other methods with respect to solution quality.