FLEXIBLE DESIGN OF WILDFIRE MANAGEMENT SYSTEMS

摘要: There are few natural phenomena with the scope and complexity of forest fires [1]. These can be a major threat to prosperity well being communities and, in fact, Portugal they severe problem, accounting for more than half EU Mediterranean region [2]. In recent years, their consequences have been severe, 2.5% forestland burned, 250 M€ direct losses, 120 spent prevention suppression, every year, on average. Understanding how mitigate these finite budget is thus critical, especially given current restrictive economic environment, catastrophic (2003, 2005, 2013). Forest Fire Management (FFM) systems complex involving technology, people, organizations, subject large uncertainties, whose design faces challenges that benefit from an interdisciplinary approach. This research addresses one those – uncertainty management by focusing use flexibility, important part Engineering Systems (ES) approach [3]. Starting study waste non-value-added FFM activities, we then proceed examine role flexibility designing integrated portfolios diverse alternatives, allow perform better under uncertainty. Our results will reinforce contributions ES body knowledge systems, assist policymakers fire managers enhancing design. Decision makers must manage portfolio alternative options resource constraints. alternatives include (e.g., education, public campaigns, fuel treatments), pre-suppression firefighter recruitment training, maintenance breaks water sources), restoration [4]. The this requires evaluation wildfires spread without impact terms monetary value destroyed or damaged assets [5]. several authors updated number reviews state art field: operations methods [6], decision support tools methodologies wildfire risk assessment face [7], applications efficiency analysis theory [8], simulators [9], surface simulation models [10], integration into planning [11]. Analytical solutions using standard mathematical always preferred approach, but when application not possible, as almost case working [12], arguably most robust method applied model real-life stochastic evolve probabilistically over time [6]. Since 1982 [13], even though considering non-market resources cultural heritage) [14], computer simulation, GIS, losses fighting costs successfully combined provide efficient choice suppression combinations. Behind cost-benefit analyses, there microeconometrics such [4, 15, 16].