The Experts below are selected from a list of 78291 Experts worldwide ranked by ideXlab platform
Michael Bevers - One of the best experts on this subject based on the ideXlab platform.
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a stochastic mixed Integer Program to model spatial wildfire behavior and suppression placement decisions with uncertain weather
Canadian Journal of Forest Research, 2016Co-Authors: Erin J Belval, Yu Wei, Michael BeversAbstract:Wildfire behavior is a complex and stochastic phenomenon that can present unique tactical management challenges. This paper investigates a multistage stochastic mixed Integer Program with full recourse to model spatially explicit fire behavior and to select suppression locations for a wildland fire. Simplified suppression decisions take the form of “suppression nodes”, which are placed on a raster landscape for multiple decision stages. Weather scenarios are used to represent a distribution of probable changes in fire behavior in response to random weather changes, modeled using probabilistic weather trees. Multistage suppression decisions and fire behavior respond to these weather events and to each other. Nonanticipativity constraints ensure that suppression decisions account for uncertainty in weather forecasts. Test cases for this model provide examples of fire behavior interacting with suppression to achieve a minimum expected area impacted by fire and suppression.
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a mixed Integer Program to model spatial wildfire behavior and suppression placement decisions
Canadian Journal of Forest Research, 2015Co-Authors: Erin J Belval, Yu Wei, Michael BeversAbstract:Wildfire suppression combines multiple objectives and dynamic fire behavior to form a complex problem for decision makers. This paper presents a mixed Integer Program designed to explore integrating spatial fire behavior and suppression placement decisions into a mathematical Programming framework. Fire behavior and suppression placement decisions are modeled using nodes associated with cell centers from raster landscapes. The nodes at which suppression is located are determined by control variables. Response variables include fire spread paths, arrival times, and fireline intensities for each node. Both fire arrival times and fireline intensities are necessary to address ecological objectives and fire control. Test cases for this model provide examples of fire behavior interacting with suppression placement to achieve multiple objectives.
Kathleen L. Irwin - One of the best experts on this subject based on the ideXlab platform.
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Applying a Mixed-Integer Program to Model Re-Screening Women who Test Positive for C. trachomatis Infection
Health Care Management Science, 2004Co-Authors: Bartholomew K. Abban, Thomas L. Gift, Guantao Chen, Kathleen L. IrwinAbstract:We proposed a mixed-Integer Program to model the management of C. trachomatis infections in women visiting publicly funded family planning clinics. We intended to maximize the number of infected women cured of C. trachomatis infections. The model incorporated screening, re-screening, and treatment options for three age groups with respective age-specific C. trachomatis infection and re-infection rates, two possible test assays, and two possible treatments. Our results showed the total budget had a great impact on the optimal strategy incorporating screening coverage, test selection, and treatment. At any budget level, the strategy that used a relatively small per-patient budget increase to re-screen all women who tested positive 6 months earlier always resulted in curing more infected women and more cost-saving than the strategy that was optimal under the condition of not including a re-screening option.
Andrew J Schaefer - One of the best experts on this subject based on the ideXlab platform.
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a polyhedral characterization of the inverse feasible region of a mixed Integer Program
Operations Research Letters, 2015Co-Authors: Jourdain B Lamperski, Andrew J SchaeferAbstract:Given a feasible solution x 0 to a mixed-Integer Program (MIP), the inverse MIP problem is to find an objective d such that x 0 is optimal for the MIP with objective function d , and among all such objectives, the distance from a given target objective is minimized. By using a novel expression for the MIP value function, we formulate the inverse MIP problem as a linear Program (LP), albeit of exponentially large size.
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on a level set characterization of the value function of an Integer Program and its application to stochastic Programming
Operations Research, 2013Co-Authors: Andrew C Trapp, Oleg A Prokopyev, Andrew J SchaeferAbstract:We propose a level-set approach to characterize the value function of a pure linear Integer Program with inequality constraints. We study theoretical properties of our characterization and show how they can be exploited to optimize a class of stochastic Integer Programs through a value function reformulation. Specifically, we develop algorithmic approaches that solve two-stage multidimensional knapsack problems with random budgets, yielding encouraging computational results.
Arif A. Anwar - One of the best experts on this subject based on the ideXlab platform.
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Sequential irrigation scheduling avoiding night irrigation
Journal of Irrigation and Drainage Engineering-asce, 2017Co-Authors: Arif A. Anwar, Tonny T. De VriesAbstract:AbstractThe sequential irrigation scheduling problem is one where a set of irrigation jobs have to be scheduled sequentially. In this paper, an Integer Program is presented to solve this problem. T...
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Genetic algorithms for the sequential irrigation scheduling problem
Irrigation Science, 2012Co-Authors: Arif A. AnwarAbstract:A sequential irrigation scheduling problem is the problem of preparing a schedule to sequentially service a set of water users. This problem has an analogy with the classical single machine earliness/tardiness scheduling problem in operations research. In previously published work, Integer Program and heuristics were used to solve sequential irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding (NP-hard). This is widely reported in operations research. Hence, Integer Program can only be used to solve relatively small problems usually in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms (GA), simulated annealing, or tabu search methods need to be used. These need to be formulated carefully and tested thoroughly. The current research is to explore the potential of GA to solve the sequential irrigation scheduling problems. Four GA models are presented that model four different sequential irrigation scenarios. The GA models are tested extensively for a range of problem sizes, and the solution quality is compared against solutions from Integer Programs and heuristics. The GA is applied to the practical engineering problem of scheduling water scheduling to 94 water users.
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Irrigation Scheduling. II: Heuristics Approach
Journal of Irrigation and Drainage Engineering-asce, 2004Co-Authors: Arif A. Anwar, Tonny T. De VriesAbstract:A sequential irrigation schedule that honors user demands of duration and minimizes earliness and tardiness is interpreted as a single-machine schedule with earliness and tardiness costs and a common deadline (or fixed interval). A heuristic solution is presented for this irrigation scheduling problem. Four models are presented to reflect the different methods in which an irrigation system at the tertiary unit level may be operated, the first model permits jobs to be noncontiguous, i.e., idle time between jobs is permitted, whereas the others permit contiguous jobs only. The heuristic is tested extensively and the solution quality is compared with either an optimum solution from an Integer Program or the best available solution obtained from an Integer Program within allocated computation time. The heuristic is computationally efficient for all models presented, however for schedules with noncontiguous jobs, or where idle time is inserted before and after a contiguous set of jobs, solution quality deteriorates. The work brings the science of single scheduling from operations research into irrigation scheduling and suggests areas for further development.
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Irrigation Scheduling. I: Integer Programming Approach
Journal of Irrigation and Drainage Engineering-asce, 2004Co-Authors: Tonny T. De Vries, Arif A. AnwarAbstract:This paper shows how a sequential irrigation schedule for a tertiary unit can be interpreted as a single machine scheduling problem with earliness, tardiness, and a common deadline. An Integer Program solution is presented for this irrigation scheduling problem. Two different models are presented to reflect different management options at the tertiary level. The first model allows jobs to be scheduled noncontiguously. In the second model only contiguous jobs are allowed. The second model has three submodels reflecting the various ways in which contiguous jobs can be scheduled over a fixed interval. Earlier work in determining unit costs of earliness/tardiness is reviewed and an alternative improved method is suggested. The models presented in this paper are applied to a tertiary unit with 16 users, both as a single interval and multi-interval irrigation scheduling problem. An alternative Integer Program is also presented which although computationally more efficient can only be used for single period scheduling problems. The models developed in this paper can be used to solve small scheduling problems and also to calibrate the heuristics as presented in the companion paper.
Turgut Aykin - One of the best experts on this subject based on the ideXlab platform.
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on a quadratic Integer Program for the location of interacting hub facilities
European Journal of Operational Research, 1990Co-Authors: Turgut AykinAbstract:O'Kelly (1987) considers the following hub location problem: Given n interacting nodes, flows between pairs of nodes, denoted by W u (W, i = 0 by assumption), the transportation cost C u of a unit of flows between nodes i and j (with Cii = 0), and p the number of hub facilities to be located ( p < n). Let X~k = 1 if node i is serviced by a hub at k and 0 otherwise. Further, let X,, = 1 if node i is a hub and 0 otherwise. The problem is to find the locations of the hub facilities and the node assignments that minimize the total transportation cost. This problem is formulated as given below.
