João Claro

Forest fire management to avoid unintended consequences: A case study of Portugal using system dynamics

Forest fires are a serious management challenge in many regions, complicating the appropriate allocation to suppression and prevention efforts. Using a System Dynamics (SD) model, this paper explores how interactions between physical and political systems in forest fire management impact the effectiveness ofxa0different allocations. A core issue is that apparently sound management can have unintended consequences. An instinctive management response to periods of worsening fire severity is to increase fire suppression capacity, an approach with immediate appeal as it directly treats the symptom of devastating fires and appeases the public. However, the SD analysis indicates that a policy emphasizing suppression can degrade the long-run effectiveness of forest fire management. By crowding out efforts to preventative fuel removal, it exacerbates fuel loads and leads to greater fires, which further balloon suppression budgets. The business management literature refers to this problem as the firefighting trap, wherein focus on fixing problems diverts attention from preventing them, and thus leads to inferior outcomes. The paper illustrates these phenomena through a case study of Portugal, showing that axa0balanced approach to suppression and prevention efforts can mitigate the self-reinforcing consequences of this trap, and better manage long-term fire damages. These insights can help policymakers and fire managers better appreciate the interconnected systems in which their authorities reside and the dynamics that may undermine seemingly rational management decisions.

Effects of cyanobacterial extracts containing anatoxin-a and of pure anatoxin-a on early developmental stages of carp.

This study compares the effects of pure anatoxin-a and cyanobacterial extracts of an anatoxin-a producing strain on early stages of development of carp. Carp eggs were exposed from 2:30 h to 4 days post-fertilization to different ecologically relevant concentrations of anatoxin-a, provided as pure toxin or contained in the cyanobacterial extracts. Data on time to mortality, mortality rate, time to hatching, hatching rate, skeletal malformations rate, and larval standard length were registered until 8 days post-fertilization. At any tested concentration of anatoxin-a, the pure toxin was almost harmless to carp early stages of development, contrarily to cell extracts that were highly toxic. Only an adverse effect on the larval length was found at the highest concentration of pure toxin, while increasing concentrations of cell extracts caused increasing adverse effects in all the analyzed parameters. Anatoxin-a producing cyanobacteria should be regarded as putative modulators of aquatic ecosystems communities.

A made-to-measure indicator for cross-disciplinary bibliometric ranking of researchers performance

This paper presents and discusses a new bibliometric indicator of research performance, designed with the fundamental concern of enabling cross-disciplinary comparisons. The indicator, called x-index, compares a researcher’s output to a reference set of research output from top researchers, identified in the journals where the researcher has published. It reflects publication quantity and quality, uses a moderately sized data set, and works with a more refined definition of scientific fields. x-index was developed to rank researchers in a scientific excellence award in the Faculty of Engineering of the University of Porto. The data set collected for the 2009 edition of the award is used to study the indicator’s features and design choices, and provides the basis for a discussion of its advantages and limitations.

A hybrid approach for integrated healthcare cooperative purchasing and supply chain configuration

This paper presents an innovative and flexible approach for recommending the number, size and composition of purchasing groups, for a set of hospitals willing to cooperate, while minimising their shared supply chain costs. This approach makes the financial impact of the various cooperation alternatives transparent to the group and the individual participants, opening way to a negotiation process concerning the allocation of the cooperation costs and gains. The approach was developed around a hybrid Variable Neighbourhood Search (VNS)/Tabu Search metaheuristic, resulting in a flexible tool that can be applied to purchasing groups with different characteristics, namely different operative and market circumstances, and to supply chains with different topologies and atypical cost characteristics. Preliminary computational results show the potential of the approach in solving a broad range of problems.

The role of fire-suppression force in limiting the spread of extremely large forest fires in Portugal

AbstractnLarge forest fires are notorious for their environmental and socio-economic impacts and are assigned a disproportionately high percentage of the fire management budget. This study addresses extremely large fires (ELF, ≥2500xa0ha) in Portugal (2003–2013). We analysed the effect of fire-suppression force variation on ELF duration, size and growth rate, versus the effect of the concomitant fire environment (namely fuel and weather) conditions. ELF occurred in highly flammable landscapes and typically were impelled by extreme fire weather conditions. Allocation of suppression resources (normalized per unit of burned area or perimeter length) was disparate among fires, suggesting inadequate incident management. Fire-suppression effort did not affect time to containment modelled by survival analysis. Regression tree analysis indicated ELF spread to be negatively affected by higher fire-suppression resourcing, less severe fire weather, lower time to containment and higher presence of <9-year-old fuels, by decreasing order of importance; regional variability was relevant. Fire environment-to-fire suppression ratios of influence were 3:1 for fire size and 1:1 for fire growth rate, respectively, explaining 76 and 60xa0% of the existing variability. Results highlight the opportunistic nature of large-fire containment. To minimize the area burned by ELF, management and operational improvements leading to faster containment are recommended, rather than higher fire-suppression resourcing; more effective identification and exploration of containment opportunities are preferable to the accumulation of suppression resources.

A multiobjective metaheuristic for a mean-risk multistage capacity investment problem

We propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with irreversibility, lumpiness and economies of scale in capacity costs. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented and indicate that the approach is capable of producing high-quality approximations to the efficient sets with a modest computational effort. The best results are achieved with a new hybrid approach, combining Tabu Search and Variable Neighbourhood Search.

A multiobjective metaheuristic for a mean-risk multistage capacity investment problem with process flexibility

Abstract In this paper, we propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with process flexibility, irreversibility, lumpiness and economies of scale in capacity costs. In each period, discrete decisions concerning the investment in capacity expansion, and continuous decisions concerning the utilization of the available capacity to satisfy demand are considered. We solve the capacity utilization problems with linear programming, in order to find the minimum capacity for each resource with the other resources remaining unchanged, this way providing information on the feasibility of the discrete investment decisions. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented, that show the approach is capable of obtaining high-quality approximations to the efficient sets, with a modest computational effort.

A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem

In this paper we address two major challenges presented by stochastic discrete optimisation problems: the multiobjective nature of the problems, once risk aversion is incorporated, and the frequent difficulties in computing exactly, or even approximately, the objective function. The latter has often been handled with methods involving sample average approximation, where a random sample is generated so that population parameters may be estimated from sample statistics—usually the expected value is estimated from the sample average. We propose the use of multiobjective metaheuristics to deal with these difficulties, and apply a multiobjective local search metaheuristic to both exact and sample approximation versions of a mean-risk static stochastic knapsack problem. Variance and conditional value-at-risk are considered as risk measures. Results of a computational study are presented, that indicate the approach is capable of producing high-quality approximations to the efficient sets, with a modest computational effort.

Integrated method for assessing and planning uncertain technology investments

We propose a method for assessment and planning of uncertain technology investments, in the context of corporate venture capital. It addresses three main issues. It is an integrated approach that starts from the technology, and proceeds to exhaustively cover the entire process that precedes valuation; it explicitly supports the identification of synergies between parent corporation and technology venture; it provides an improved treatment of uncertainty, adopting a broader view on the identification of uncertainty and sources of managerial flexibility, and starting to address it sooner, in the opportunity development phase. It is facilitated by a suite of practical tools. We provide a detailed description of the method and demonstrate its application with an illustrative case study.

Optimization of power flow with energy storage using genetic algorithms

This paper applies genetic algorithms to optimize the operation of a transmission network with energy storage capabilities, to optimize its costs, which include both generation and storage costs, for cases when the data inherent to the system is assumed to be perfectly known. The problem is formulated through the DC optimal power flow equations, including losses across the transmission lines, therefore allowing solutions regarding the network generation costs to be obtained, with and without storage. In this way, the financial impact inherent to the usage of energy storage can be derived. Since we are dealing with a large combinatorial problem, the search throughout the solution space was done by means of the Genetic Algorithms. The solutions consist of the storage devices charging or discharging rate at which it must be operating during each sub-interval considered for the simulations. The results delivered by the GA have proven the profitability of including energy storage capabilities in the transmission network of São Miguel (Portugal) and the usefulness of such algorithm in a real world application.