Asset Protection

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The Experts below are selected from a list of 2061 Experts worldwide ranked by ideXlab platform

Jd Ike Devji - One of the best experts on this subject based on the ideXlab platform.

Sarah Kammer - One of the best experts on this subject based on the ideXlab platform.

John W Hearne - One of the best experts on this subject based on the ideXlab platform.

  • a spatial decomposition based math heuristic approach to the Asset Protection problem
    Operations Research Perspectives, 2020
    Co-Authors: Dian Nuraiman, Melih Ozlen, John W Hearne
    Abstract:

    Abstract This paper addresses the highly critical task of planning Asset Protection activities during uncontrollable wildfires known in the literature as the Asset Protection Problem (APP). In the APP each Asset requires a protective service to be performed by a set of emergency response vehicles within a specific time period defined by the spread of fire. We propose a new spatial decomposition based math-heuristic approach for the solution of large-scale APP’s. The heuristic exploits the property that time windows are geographically correlated as fire spreads across a landscape. Thus an appropriate division of the landscape allows the problem to be decomposed into smaller more tractable sub-problems. The main challenge then is to minimise the difference between the final locations of vehicles from one division to the optimal starting locations of the next division. The performance of the proposed approach is tested on a set of benchmark instances from the literature and compared to the most recent Adaptive Large Neighborhood Search (ALNS) algorithm developed for the APP. The results show that our proposed solution approach outperforms the ALNS algorithm on all instances with comparable computation time. We also see a trend with the margin of out-performance becoming more significant as the problems become larger.

  • a two stage stochastic approach for the Asset Protection problem during escaped wildfires with uncertain timing of a wind change
    arXiv: Optimization and Control, 2018
    Co-Authors: Iman Roozbeh, John W Hearne, Babak Abbasi, Melih Ozlen
    Abstract:

    Wildfires are natural disasters capable of damaging economies and communities. When wildfires become uncontrollable, Incident Manager Teams (IMT's) dispatch response vehicles to key Assets to undertake protective tasks and so mitigate the risk to these Assets. In developing a deployment plan under severe time pressure, IMT's need to consider the special requirements of each Asset, the resources (vehicles and their teams), as well as uncertainties associated with the wildfire. A common situation that arises in southern Australian wildfires is a wind change. There is a reliable forecast of a wind change, but some uncertainty around the timing of that change. To assist IMT's to deal with this situation we develop a two-stage stochastic model to integrate such an uncertainty with the complexities of Asset Protection operations. This is the first time a mathematical model is proposed which considers uncertainty in the timing of a scenario change. The model is implemented for a case study that uses the context of the 2009 Black Saturday bushfires in Victoria. A new set of benchmark instances is generated using realistic wildfire attributes to test the computational tractability of our model and the results compared to a dynamic rerouting approach. The computations reveal that, compared with dynamic rerouting, the new model can generate better deployment plans. The model can achieve solutions in operational time for realistic-sized problems, although for larger problems the sub-optimal rerouting algorithm would still need to be deployed.

  • an adaptive large neighbourhood search for Asset Protection during escaped wildfires
    Computers & Operations Research, 2018
    Co-Authors: Iman Roozbeh, Melih Ozlen, John W Hearne
    Abstract:

    Abstract The Asset Protection problem is encountered where an uncontrollable fire is sweeping across a landscape comprising important infrastructure Assets. Protective activities by teams of firefighters can reduce the risk of losing a particular Asset. These activities must be performed during a time-window for each Asset determined by the progression of the fire. The nature of some Assets is such that they require the simultaneous presence of more than one fire vehicle and its capabilities must meet the requirements of each Asset visited. The objective is then to maximise the value of the Assets protected subject to constraints on the number and type of fire trucks available. The solution times to this problem using commercial solvers preclude their use for operational purposes. In this work we develop an Adaptive Large Neighbourhood Search algorithm (ALNS) based on problem-specific attributes. Several removal and insertion heuristics, including some new algorithms, are applied. A new benchmark set is generated by considering the problem attributes. In tests with small instances the ALNS is shown to achieve optimal, or near optimal, results in a fraction of the time required by CPLEX. In a second set of experiments comprising larger instances the ALNS was able to produce solutions in times suitable for operational purposes. These solutions mean that significantly more Assets can be protected than would be the case otherwise.

  • a mixed integer programming approach for Asset Protection during escaped wildfires
    Canadian Journal of Forest Research, 2015
    Co-Authors: Martijn Van Der Merwe, Melih Ozlen, James P Minas, John W Hearne
    Abstract:

    Incident management teams (IMTs) are responsible for managing the response to wildfires. One of the objectives of IMTs is the Protection of Assets and infrastructure. In this paper, we develop a mathematical model to assist IMTs in assigning resources to Asset Protection activities during wildfires. We present a mixed integer programming model for resource allocation with the aim of protecting the maximum possible total value of Assets. The model allows for mixed vehicle types with interchangeable capabilities and with travel times determined by vehicle-specific speed and road network information. We define location-specific Protection requirements in terms of vehicle capabilities. The formulated model extends classic variants of the team orienteering problem with time windows. The model capabilities are demonstrated using a hypothetical fire scenario impacting South Hobart, Tasmania, Australia. Computational testing shows that realistically sized problems can be solved within a reasonable time.

Ulrich G Reinhardt - One of the best experts on this subject based on the ideXlab platform.

  • Asset Protection in juvenile salmon how adding biological realism changes a dynamic foraging model
    Behavioral Ecology, 2002
    Co-Authors: Ulrich G Reinhardt
    Abstract:

    The "Asset-Protection principle" created by Clark is based on a dynamic programming model and states that individuals should (1) become more averse to predation risk as they accumulate fitness Assets but (2) generally be more willing to accept predation risk later in the foraging season. To test whether these predictions hold under biologically meaningful foraging parameters, I constructed a dynamic model of the optimal trade-off between foraging and predator avoidance in juvenile salmon. The model incorporates temperature and body-size dependent bio-energetic constraints typical for juvenile fish, which grow by orders of magnitude over a season. In its simplest form using seasonally constant growth potential and a linear over-winter survival function, my results equal those of Clark's model. Adding a fitness function and environmental data from field studies accentuates the Asset-Protection effect and fundamentally changes the seasonal pattern of optimal effort. Simulation of typical poor feeding conditions in mid-summer yields the prediction of increased foraging in the spring in anticipation of worsening conditions. Increasing overall predation risk results in smaller fish at the end of the season with a trade-off between summer and winter survival. The model generates testable predictions for juvenile salmon and provides insights for other organisms (particularly poikilotherms) that are subject to size-dependent or seasonally changing foraging dynamics. Copyright 2002.

  • season and size dependent risk taking in juvenile coho salmon experimental evaluation of Asset Protection
    Animal Behaviour, 1999
    Co-Authors: Ulrich G Reinhardt, Michael C Healey
    Abstract:

    Abstract Using juvenile coho salmon, Oncorhynchus kisutch , we tested predictions arising from dynamic optimization models of foraging under predation risk. Coho juveniles from two size groups raised in the laboratory were individually fed varying food rations. Their willingness to risk predation was measured as the time to resume foraging after presentation of a predator model. Small fish (mean weight 1.5 g) resumed feeding earlier than larger fish (3.5 g) as predicted by dynamic models under summer photoperiod but not under autumn photoperiod. Contrary to predictions, larger fish did not increase risk taking and small fish decreased risk taking between summer and autumn treatments. Food ration significantly influenced time to resume feeding only in small coho. A simple mechanistic model we proposed to explain feeding motivation under risk as a function of body size and prior growth rate was not sufficient to explain observed variation in risk taking. This study suggests that coho salmon use photoperiod and their own body size as cues for long-term, state-dependent adjustments of feeding behaviour. The lower risk taking of larger fish is probably an example of Asset Protection, whereby larger animals accept less predation risk to protect their greater accumulated fitness value. The decrease of risk taking in small fish in the autumn was possibly caused by a switch of life history trajectory towards delayed smolting.

Mark P Kaminskiy - One of the best experts on this subject based on the ideXlab platform.

  • risk analysis for critical Asset Protection
    Risk Analysis, 2007
    Co-Authors: William L Mcgill, Bilal M Ayyub, Mark P Kaminskiy
    Abstract:

    This article proposes a quantitative risk assessment and management framework that supports strategic Asset-level resource allocation decision making for critical infrastructure and key resource Protection. The proposed framework consists of five phases: scenario identification, consequence and criticality assessment, security vulnerability assessment, threat likelihood assessment, and benefit-cost analysis. Key innovations in this methodology include its initial focus on fundamental Asset characteristics to generate an exhaustive set of plausible threat scenarios based on a target susceptibility matrix (which we refer to as Asset-driven analysis) and an approach to threat likelihood assessment that captures adversary tendencies to shift their preferences in response to security investments based on the expected utilities of alternative attack profiles assessed from the adversary perspective. A notional example is provided to demonstrate an application of the proposed framework. Extensions of this model to support strategic portfolio-level analysis and tactical risk analysis are suggested.

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