An optimization model for locating fuel treatments across a landscape to reduce expected fire losses

2008 ◽  
Vol 38 (4) ◽  
pp. 868-877 ◽  
Author(s):  
Yu Wei ◽  
Douglas Rideout ◽  
Andy Kirsch
Keyword(s):  
Spatial Information ◽  
Solution Process ◽  
Fire Risk ◽  
Simulation Models ◽  
Mixed Integer ◽  
Fire Intensity ◽  
Fuel Treatments ◽  
Fuel Treatment ◽  
Mip Model ◽  
Fire Ignition

Locating fuel treatments with scarce resources is an important consideration in landscape-level fuel management. This paper developed a mixed integer programming (MIP) model for allocating fuel treatments across a landscape based on spatial information for fire ignition risk, conditional probabilities of fire spread between raster cells, fire intensity levels, and values at risk. The fire ignition risk in each raster cell is defined as the probability of fire burning a cell because of the ignition within that cell. The conditional probability that fire would spread between adjacent cells A and B is defined as the probability of a fire spreading into cell B after burning in cell A. This model locates fuel treatments by using a fire risk distribution map calculated through fire simulation models. Fire risk is assumed to accumulate across a landscape following major wind directions and the MIP model locates fuel treatments to efficiently break this pattern of fire risk accumulation. Fuel treatment resources are scarce and such scarcity is introduced through a budget constraint. A test case is designed based on a portion of the landscape (15 552 ha) within the Southern Sierra fire planning unit to demonstrate the data requirements, solution process, and model results. Fuel treatment schedules, based upon single and dual wind directions, are compared.

Optimize landscape fuel treatment locations to create control opportunities for future fires

2012 ◽  
Vol 42 (6) ◽  
pp. 1002-1014 ◽  
Author(s):  
Yu Wei
Keyword(s):  
National Parks ◽  
Spatial Information ◽  
Programming Model ◽  
Mixed Integer ◽  
Fire Intensity ◽  
Planning Problem ◽  
Fire Control ◽  
Fuel Treatments ◽  
Fuel Treatment ◽  
Ignition Probability

Fuel treatment can improve the efficiency of controlling future catastrophic fires. Selecting optimal fuel treatment locations across a landscape is a challenging strategic planning problem in wildland fire management. This research develops a new fuel treatment optimization model by extending a fire suppression model to simultaneously consider many future fires. Fire is ignited from every grid cell in a landscape and modeled for various durations in a mixed integer programming model. Fuel treatment in a cell decreases its fire intensity and makes future fire control effective in it. This model allocates fuel treatments to minimize the total landscape future fire loss. It was first tested on several artificial landscapes for model validation. Results show that it tends to allocate fuel treatments in contiguous areas following regular and intuitive spatial patterns. Spatial fuel treatment layouts vary according to the change of fire ignition probability distribution, the distribution of value to be protected from fire, and fire duration assumptions. Trade-off between protecting different parts of a landscape is a major driver in designing fuel treatment layouts. A test case in the Sequoia and Kings Canyon national parks demonstrates how this model assembles spatial information and helps study the effects of fuel treatments in a heterogeneous landscape. This model allows managers to assemble information from many possible future fires to make informative strategic-level fuel treatment decisions. A potential model extension and the limitations of this model are also discussed.

scholarly journals Designing Operationally Relevant Daily Large Fire Containment Strategies Using Risk Assessment Results

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 311 ◽  
Author(s):  
Yu Wei ◽  
Matthew Thompson ◽  
Joe Scott ◽  
Christopher O’Connor ◽  
Christopher Dunn
Keyword(s):  
Risk Assessment ◽  
Fire Risk ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Large Fire ◽  
Decision Framework ◽  
Mip Model ◽  
Fire Risk Assessment ◽  
Fire Containment ◽  
Assessment Results

In this study, we aim to advance the optimization of daily large fire containment strategies for ground-based suppression resources by leveraging fire risk assessment results commonly used by fire managers in the western USA. We begin from an existing decision framework that spatially overlays fire risk assessment results with pre-identified potential wildland fire operational delineations (PODs), and then clusters PODs into a response POD (rPOD) using a mixed integer program (MIP) model to minimize expected loss. We improve and expand upon this decision framework through enhanced fire modeling integration and refined analysis of probabilistic and time-sensitive information. Specifically, we expand the set of data inputs to include raster layers of simulated burn probability, flame length probability, fire arrival time, and expected net value change, all calculated using a common set of stochastic weather forecasts and landscape data. Furthermore, we develop a secondary optimization model that, for a given optimal rPOD, dictates the timing of fire line construction activities to ensure completion of containment line prior to fire arrival along specific rPOD edges. The set of management decisions considered includes assignment of PODs to be included in the rPOD, assignment of suppression resources to protect susceptible structures within the rPOD, and assignment of suppression resources to construct fire lines, on specific days, along the perimeter of the rPOD. We explore how fire manager risk preferences regarding firefighter safety affect optimal rPOD characteristics, and use a simple decision tree to display multiple solutions and support rapid assessment of alternatives. We base our test cases on the FSPro simulation of the 2017 Sliderock Fire that burned on the Lolo National Forest in Montana, USA. The overarching goal of this research is to generate operationally relevant decision support that can best balance the benefits and losses from wildfire and the cost from responding to wildfire.

A high performance scalable fuzzy based modified Asymmetric Heterogene Multiprocessor System on Chip (AHt-MPSOC) reconfigurable architecture

2021 ◽  
pp. 1-12
Author(s):  
Arun Prasath Raveendran ◽  
Jafar A. Alzubi ◽  
Ramesh Sekaran ◽  
Manikandan Ramachandran
Keyword(s):  
High Performance ◽  
Standard Technique ◽  
System On Chip ◽  
Mixed Integer ◽  
Multiprocessor System ◽  
Compound System ◽  
Available Bandwidth ◽  
Mip Model ◽  
Fpga Chip ◽  
On Chip

This Ensuing generation of FPGA circuit tolerates the combination of lot of hard and soft cores as well as devoted accelerators on a chip. The Heterogene Multi-Processor System-on-Chip (Ht-MPSoC) architecture accomplishes the requirement of modern applications. A compound System on Chip (SoC) system designed for single FPGA chip, and that considered for the performance/power consumption ratio. In the existing method, a FPGA based Mixed Integer Programming (MIP) model used to define the Ht-MPSoC configuration by taking into consideration the sharing hardware accelerator between the cores. However, here, the sharing method differs from one processor to another based on FPGA architecture. Hence, high number of hardware resources on a single FPGA chip with low latency and power targeted. For this reason, a fuzzy based MIP and Graph theory based Traffic Estimator (GTE) are proposed system used to define New asymmetric multiprocessor heterogene framework on microprocessor (AHt-MPSoC) architecture. The bandwidths, energy consumption, wait and transmission range are better accomplished in this suggested technique than the standard technique and it is also implemented with a multi-task framework. The new Fuzzy control-based AHt-MPSoC analysis proves significant improvement of 14.7 percent in available bandwidth and 89.8 percent of energy minimized to various traffic scenarios as compared to conventional method.

scholarly journals COMPORTAMENTO DO FOGO EM QUEIMAS CONTROLADAS DE VEGETAÇÃO DE ESTEPE NO MUNICÍPIO DE PALMEIRA, PARANÁ, BRASIL

FLORESTA ◽  
2013 ◽  
Vol 43 (4) ◽  
pp. 557
Author(s):  
Celso Darci Seger ◽  
Antonio Carlos Batista ◽  
Alexandre França Tetto ◽  
Ronaldo Viana Soares
Keyword(s):  
Fire Behavior ◽  
Fire Risk ◽  
Fire Spread ◽  
Flame Height ◽  
Fire Intensity ◽  
Average Moisture Content ◽  
Fuel Loading ◽  
Prescribed Burns ◽  
Controlled Burning ◽  
Heat Released

As queimas controladas constituem práticas de manejo utilizadas em diferentes tipos de vegetação e difundidas em vários países. No entanto, para a realização de tais práticas com segurança e eficiência é fundamental o conhecimento do comportamento do fogo. O objetivo desse trabalho foi caracterizar o comportamento do fogo em queimas controladas de vegetação Estepe Gramíneo-Lenhosa no estado do Paraná. Para isso, foi instalado um experimento no município de Palmeira, onde 20 parcelas foram queimadas, sendo metade a favor e metade contra o vento. A carga de material combustível fino estimada foi de 2,26 kg.m-2, com teor médio de umidade de 50,45%. A quantidade de material consumido pela queima foi de 1,76 kg.m-2, com uma eficiência média de queima de 76,86%. As médias obtidas, a favor e contra o vento, foram respectivamente: velocidade de propagação do fogo de 0,049 e 0,012 m.s-1, altura das chamas de 1,34 e 0,843 m, intensidade do fogo de 210,53 e 50,68 kcal.m-1.s-1 e calor liberado de 4.067,19 e 4.508,92 kcal.m-2. Os resultados permitiram concluir que as queimas controladas em vegetação de campos naturais, realizadas dentro dos critérios estabelecidos de planos de queima, são viáveis e seguras sob o ponto de vista de perigo de incêndios.Palavras chave: Queima prescrita; material combustível; intensidade do fogo; perigo de incêndios. AbstractFire behavior of prescribed burns in grassland on Palmeira county, Paraná, Brazil. The prescribed burns are practices of management used in different types of vegetation and widespread in several countries. However, to carry out such practices safely and effectively is fundamental knowledge of fire behavior. The aim of this study was to characterize the fire behavior in controlled burning of grassland vegetation in Paraná state. For this, an experiment was conducted in Palmeira County, where 20 plots were burned, half in favor and half against the wind. The estimated fine fuel loading was 2.26 kg.m-2, with average moisture content of 50.45%. The fuel consumption by burning was 1.76 kg.m-2 with an average efficiency of burning of 76.86%. The averages, for and against the wind, were: speed of fire spread of 0.049 and 0.012 m.s-1, the flame height of 1.34 m and 0.843, fire intensity of 210.53 and 50.68 kcal.m-1.s-1 and heat released from 4,067.19 and 4,508.92 kcal.m-2. The results show that the controlled burnings of grasslands vegetation, carried out within the established criteria burning plans are feasible and safe from the aspect of fire danger.Keywords: Prescribed burns; fuel loading; fire intensity; fire risk.

scholarly journals Climate change and forest management affect forest fire risk in Fennoscandia

2021 ◽  
Author(s):  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Black Carbon ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
The Arctic ◽  
Large Fires ◽  
Forest Fire Risk ◽  
Fire Ignition

Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.

Kiln Drying Operations Scheduling with Dynamic Composition of Loading Patterns

2021 ◽  
Vol 71 (2) ◽  
pp. 101-110
Author(s):  
Philippe Marier ◽  
Jonathan Gaudreault ◽  
Thomas Noguer
Keyword(s):  
Physical Stability ◽  
Difficult Problem ◽  
Mixed Integer ◽  
Planning And Scheduling ◽  
Operations Planning ◽  
Kiln Drying ◽  
Dynamic Composition ◽  
Mip Model ◽  
Loading Patterns ◽  
Lumber Drying

Abstract Planning and scheduling wood lumber drying operations is a very difficult problem. The literature proposes different methods aiming to minimize order lateness. They all make use of pre-established kiln loading patterns that are known to offer good physical stability in the kiln and allow full kiln space utilization. Instead, we propose a mixed integer programming (MIP) model, which can be used to generate loading patterns “on the fly.” This MIP model can be integrated into existing kiln drying operation planning/scheduling systems in order to improve their solutions. We show how this integration can be done by adapting a state of the art drying operations planning and scheduling methodology from the literature. We compare the solutions obtained by this system using the predefined loading patterns versus the solutions it generates if it is connected to our loading patterns generator MIP model. The study shows it is much better to dynamically create loading patterns than to use predefined ones, as most North American sawmills do.

A note on bi-objective optimization for sustainable supply chain network design in omnichannel

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weidong Lei ◽  
Dandan Ke ◽  
Pengyu Yan ◽  
Jinsuo Zhang ◽  
Jinhang Li
Keyword(s):  
Mixed Integer ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
Product Flow ◽  
Flow Balance ◽  
Mip Model ◽  
Milp Model ◽  
Facility Capacity

PurposeThis paper aims to correct the existing mixed integer programming (MIP) model proposed by Yadav et al. (2019) [“Bi-objective optimization for sustainable supply chain network design in omnichannel.”, Journal of Manufacturing Technology Management, Vol. 30 No. 6, pp. 972–986].Design/methodology/approachThis paper first presents a counterexample to show that the existing MIP model is incorrect and then proposes an improved mixed integer linear programming (MILP) model for the considered problem. Last, a numerical experiment is conducted to test our improved MILP model.FindingsThis paper demonstrates that the formulations of the facility capacity constraints and the product flow balance constraints in the existing MIP model are incorrect and incomplete. Due to this reason, infeasible solutions could be identified as feasible ones by the existing MIP model. Hence, the optimal solution obtained with the existing MIP model could be infeasible. A counter-example is used to verify our observations. Computational results verify the effectiveness of our improved MILP model.Originality/valueThis paper gives a complete and correct formulation of the facility capacity constraints and the product flow balance constraints, and conducts other improvements on the existing MIP model. The improved MILP model can be easily implemented and would help companies to have more effective distribution networks under the omnichannel environment.

Production and Inter-Facility Transportation with Shipment Size Restrictions

2013 ◽  
pp. 74-92
Author(s):  
Mohamed K. Omar
Keyword(s):  
Integer Programming ◽  
Transportation Problem ◽  
Mixed Integer ◽  
Actual Performance ◽  
Industrial Data ◽  
Chemical Company ◽  
Mip Model ◽  
Proposed Model ◽  
Production Inventory ◽  
End Products

This chapter studies production and transportation problem confronting a speciality chemical company that has two manufacturing facilities. Facility I produces intermediate products which are then transported to Facility II where the end products are to be manufactured to meet customers’ demand. The author formulated the problem as a mixed integer programming (MIP) model that integrates the production and transportation decisions between the two facilities. The developed MIP aims to minimize the production, inventory, manpower, and transportation costs. Real industrial data are used to test and validate the developed MIP model. Comparing the model’s results and the company’s actual performance indicate that, if the company implemented the proposed model, significant costs savings could be achieved.

Mitigation Strategies for Foot and Mouth Disease

2013 ◽  
pp. 176-203
Author(s):  
Sohini Roy Chowdhury ◽  
Caterina Scoglio ◽  
William H. Hsu
Keyword(s):  
Predictive Models ◽  
Spatial Information ◽  
Ethical Issues ◽  
Foot And Mouth Disease ◽  
Simulation Models ◽  
Cost Effective ◽  
Mouth Disease ◽  
Mitigation Strategies ◽  
Foot And Mouth ◽  
The Cost

Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected countries. In the absence of precise and accurate spatial information regarding disease dynamics, learning- based predictive models can be used to mimic latent spatial parameters so as to predict the spread of epidemics in time. This paper analyzes temporal predictions from four such learning-based models, namely: neural network, autoregressive, Bayesian network, and Monte-Carlo simulation models. The prediction qualities of these models have been validated using FMD incidence reports in Turkey. Additionally, the authors perform simulations of mitigation strategies based on the predictive models to curb the impact of the epidemic. This paper also analyzes the cost-effectiveness of these mitigation strategies to conclude that vaccinations and movement ban strategies are more cost-effective than premise culls before the onset of an epidemic outbreak; however, in the event of existing epidemic outbreaks, premise culling is more effective at controlling FMD.

Tri-Generation Systems Optimization: Comparison of Heuristic and Mixed Integer Linear Programming Approaches

2014 ◽  
Author(s):  
Aldo Bischi ◽  
Stefano Campanari ◽  
Alberto Castiglioni ◽  
Giampaolo Manzolini ◽  
Emanuele Martelli ◽  
...  
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Storage System ◽  
Simulation Models ◽  
Point Of View ◽  
Mixed Integer ◽  
Test Case ◽  
Optimization Strategy ◽  
Software Codes

This work compares two optimization approaches for combined cooling, heating and power (CCHP or Tri-generation) energy systems scheduling. Both approaches are developed through dedicated software codes and are based on simulation models capable of evaluating of the best operating strategy (both economically and energy-wise) to run a given trigeneration plant while dealing with time-variable loads and tariffs. The simultaneous use of different prime movers operating in parallel is taken into consideration as well as their part load performance, the influence of ambient temperature and the usage of a heat storage system. Cooling may be generated through absorption chillers or electrically driven compression cycles. One of the models is heuristic and adopts an optimization strategy based on a multi-step approach: it simulates several cases according to a pre-defined number of paths, exploring the most reasonable operational modes and comparing them systematically. The other relies on a mathematical approach, based on a Mixed Integer Linear Programming (MILP) optimization model which has been developed in order to deal with more complex systems without the need of predefining a too large variety of operation paths. Results of the two models are compared against a test case based on real plant specifications, discussing their performance by the point of view of simulation capabilities, quality and accuracy of the optimization results (in terms of differences in energy and economic performance) and computational resources.

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