Sabir Ribas

A hybrid heuristic algorithm for the open-pit-mining operational planning problem

This paper deals with the Open-Pit-Mining Operational Planning problem with dynamic truck allocation. The objective is to optimize mineral extraction in the mines by minimizing the number of mining trucks used to meet production goals and quality requirements. According to the literature, this problem is NP-hard, so a heuristic strategy is justified. We present a hybrid algorithm that combines characteristics of two metaheuristics: Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search. The proposed algorithm was tested using a set of real-data problems and the results were validated by running the CPLEX optimizer with the same data. This solver used a mixed integer programming model also developed in this work. The computational experiments show that the proposed algorithm is very competitive, finding near optimal solutions (with a gap of less than 1%) in most instances, demanding short computing times.

Using Reference Groups to Assess Academic Productivity in Computer Science

In this paper we discuss the problem of how to assess academic productivity based on publication outputs. We are interested in knowing how well a research group in an area of knowledge is doing relatively to a pre-selected set of reference groups, where each group is composed by academics or researchers. To assess academic productivity we adopt a new metric we propose, which we call P-score. We use P-score, citation counts and H-Index to obtain rankings of researchers in Brazil. Experimental results using data from the area of Computer Science show that P-score outperforms citation counts and H-Index when assessed against the official ranking produced by the Brazilian National Research Council (CNPq). This is of our interest for two reasons. First, it suggests that citation-based metrics, despite wide adoption, can be improved upon. Second, contrary to citation-based metrics, the P-score metric does not require access to the content of publications to be computed.

Random Walks on the Reputation Graph

The identification of reputable entities is an important task in business, education, and many other fields. On the other hand, as an arguably subjective, multi-faceted concept, quantifying reputation is challenging. In this paper, instead of relying on a single, precise definition of reputation, we propose to exploit the transference of reputation among entities in order to identify the most reputable ones. To this end, we propose a novel random walk model to infer the reputation of a target set of entities with respect to suitable sources of reputation. We instantiate our model in an academic search setting, by modeling research groups as reputation sources and publication venues as reputation targets. By relying on publishing behavior as a reputation signal, we demonstrate the effectiveness of our model in contrast to standard citation-based approaches for identifying reputable venues as well as researchers in the broad area of computer science. In addition, we demonstrate the robustness of our model to perturbations in the selection of reputation sources. Finally, we show that effective reputation sources can be chosen via the proposed model itself in a semi-automatic fashion.