Browsing by Author "Vera, Jorge R."

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    A functional-structural model for radiata pine (Pinus radiata) focusing on tree architecture and wood quality
    (OXFORD UNIV PRESS, 2011) Paulina Fernandez, M.; Norero, Aldo; Vera, Jorge R.; Perez, Eduardo
    Backgrounds and Aims Functional-structural models are interesting tools to relate environmental and management conditions with forest growth. Their three-dimensional images can reveal important characteristics of wood used for industrial products. Like virtual laboratories, they can be used to evaluate relationships among species, sites and management, and to support silvicultural design and decision processes. Our aim was to develop a functional-structural model for radiata pine (Pinus radiata) given its economic importance in many countries.
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    A tabu search approach for solving a difficult forest harvesting machine location problem
    (ELSEVIER, 2007) Legues, Andres Diaz; Ferland, Jacques A.; Ribeiro, Celso C.; Vera, Jorge R.; Weintraub, Andres
    This paper deals with two main problems in forest harvesting. The first is that of selecting the locations for the machinery to haul logs from the points where they are felled to the roadside. The second consists in designing the access road network connecting the existing road network with the points where machinery is installed. Their combination induces a very important and difficult problem to solve in forest harvesting. It can be formulated as a combination of two difficult optimization problems: a plant location problem and a fixed charge network flow problem. In this paper, we propose a solution approach based on tabu search. The proposed heuristic includes several enhancements of the basic tabu search framework. The main difficulty lies in evaluating neighboring solutions, which involves decisions related to location of machinery and to road network arcs. Hence, the neighborhood is more complex than in typical applications of metaheuristics. Minimum spanning tree algorithms and Steiner tree heuristics are used to deal with this problem. Numerical results indicate that the heuristic approach is very attractive and leads to better solutions than those provided by state-of-the-art integer programming codes in limited computation times, with solution times significantly smaller. The numerical results do not vary too much when typical parameters such as the tabu tenure are modified, except for the dimension of neighborhood. (c) 2005 Elsevier B.V. All rights reserved.
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    Comparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation
    (Blackwell Publishing Ltd, 2021) Avanzini, Elbio Leonel; Mac Cawley A.F.; Vera, Jorge R.; Maturana Valderrama, Sergio
    © 2021 The Authors. International Transactions in Operational Research © 2021 International Federation of Operational Research SocietiesOperations planning is an important step in any activity as it aligns resources to achieve economic production value. In agriculture operations where uncertainty is present, planners must deal with biological and environmental factors, among others, which add variability and complexity to the production planning process. In this work, we consider operations planning to harvest grapes for wine production where uncertainty in weather conditions will affect the quality of grapes and, consequently, the economic value of the product. In this setting, planners make decisions on labor allocation and harvesting schedules, considering uncertainty of future rain. Weather uncertainty is modeled following a Markov Chain approach, in which rain affects the quality of grapes and labor productivity. We compare an expected value with a multi-stage stochastic optimization approach using standard metrics such as Value of Stochastic Solution and Expected Value of Perfect Information. We analyze the impact of grape quality over time, if they are not harvested on the optimal ripeness day, and also consider differences in ability between workers, which accounts for the impact of rain in their productivity. Results are presented for a small grape harvest instance and we compare the performance of both models under different scenarios of uncertainty, manpower ability, and product qualities. Results indicate that the multi-stage approach produces better results than the expected value approach, especially under high uncertainty and high grape quality scenarios. Worker ability is also a mechanism for dealing with uncertainty, and both models take advantage of this variable.
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    Equivalence of Convex Problem Geometry and Computational Complexity in the Separation Oracle Model
    (INFORMS, 2009) Freund, Robert M.; Vera, Jorge R.
    Consider the supposedly simple problem of computing a point in a convex set that is conveyed by a separation oracle with no further information (e. g., no domain ball containing or intersecting the set, etc.). The authors' interest in this problem stems from fundamental issues involving the interplay of (i) the computational complexity of computing a point in the set, (ii) the geometry of the set, and (iii) the stability or conditioning of the set under perturbation. Under suitable definitions of these terms, the authors show herein that problem instances with favorable geometry have favorable computational complexity, validating conventional wisdom. The authors also show a converse of this implication by showing that there exist problem instances that require more computational effort to solve in certain families characterized by unfavorable geometry. This in turn leads, under certain assumptions, to a form of equivalence among computational complexity, geometry, and the conditioning of the set. The authors' measures of the geometry, relative to a given reference point, are based on the radius of a certain domain ball whose intersection with the set contains a certain inscribed ball.
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    Improving the efficiency of the Branch and Bound algorithm for integer programming based on "flatness" information
    (ELSEVIER SCIENCE BV, 2006) Derpich, Ivan; Vera, Jorge R.
    This paper describes a strategy for defining priorities for the branching variables in a Branch and Bound algorithm. The strategy is based on shape information about the polyhedron over which we are optimizing. This information is related to measures of the integer width, as provided by the so called "Flatness Theorem". Our selection rule uses that knowledge to define branching priorities on the variables. Computational results with simulated small to medium size integer problems are presented, as well with multi-knapsack problems. These show savings of about 40% in CPU time, as well as in nodes generated in the search tree, and compare favorably with respect to other standard branching rules. (c) 2005 Elsevier B.V. All rights reserved.