24 results on '"Morton, David P."'
Search Results
2. Flow Balancing with Uncertain Demand for Automated Package Sorting Centers
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Novoa, Luis J., Jarrah, Ahmad I., and Morton, David P.
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Automation -- Usage ,Sorting (Computers) -- Methods ,Information technology -- Usage ,Mechanization -- Usage ,Packaging -- Technology application ,Information technology ,Technology application ,Science and technology ,Social sciences ,Transportation industry - Abstract
Abstract. Package carriers use sophisticated automated sorting facilities to efficiently process inbound packages and sort them to their down line destinations. During each of several daily processing windows, primary sorters [...]
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- 2018
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3. Stockpiling ventilators for influenza pandemics
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Huang, Hsin-Chan, Araz, Ozgur M., Morton, David P., Johnson, Gregory P., Damien, Paul, Clements, Bruce, and Meyers, Lauren Ancel
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United States. Centers for Disease Control and Prevention -- Analysis ,United States. Department of Health and Human Services -- Analysis ,Texas. Department of State Health Services -- Analysis ,Hospital patients -- Case studies -- Care and treatment -- Analysis -- Forecasts and trends ,Public health -- Case studies -- Analysis -- Forecasts and trends ,Influenza -- Case studies -- Care and treatment -- Analysis -- Forecasts and trends ,Market trend/market analysis ,Health ,American Association for Respiratory Care - Abstract
Diligent preparation and effective countermeasures are critical to mitigating future influenza pandemics. The 1918 influenza pandemic, the most severe in recent history, resulted in [approximately equal to] 50 million deaths [...]
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- 2017
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4. Assessing the quality of convex approximations for two-stage totally unimodular integer recourse models
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Romeijnders, Ward, Morton, David P., and van der Vlerk, Maarten H.
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Statistical sampling -- Methods ,Stochastic programming -- Methods ,Approximation theory -- Methods ,Mathematical optimization -- Research ,Computers ,Science and technology - Abstract
Abstract. We consider two types of convex approximations of two-stage totally unimodular integer recourse models. Although worst-case error bounds are available for these approximations, their actual performance has not yet [...]
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- 2017
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5. Modeling and optimization of a spatial detection system
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Lu, Fang, Hasenbein, John J., and Morton, David P.
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Oil well drilling -- Safety and security measures -- Management ,Stochastic programming -- Methods ,Engineering research ,Mathematical optimization -- Research ,Scheduling (Management) -- Methods ,Company business management ,Computers ,Science and technology - Abstract
Oil and gas companies are drilling and developing fields in the Arctic Ocean, which is an environment with ice floes. These companies must protect their platforms from ice floe collisions. [...]
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- 2016
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6. Opportunities in security: OR: catalyst for grand challenges
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Morton, David P. and Sen, Suvrajeet
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Management science -- Analysis ,College students -- Education ,Business, general ,Science and technology ,Social sciences ,National Academy of Engineering -- Educational aspects - Abstract
The infrastructure of the United States, and the world, is increasingly interconnected. The ongoing integration of systems--including transportation, energy, water, communications, finance and more--has been central to their growth in [...]
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- 2016
7. Prioritization via stochastic optimization
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Koc, Ali and Morton, David P.
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Stochastic programming -- Methods ,Resource allocation -- Methods ,Decision-making -- Methods ,Business, general ,Business - Abstract
We take a novel approach to decision problems involving binary activity-selection decisions competing for scarce resources. The literature approaches such problems by forming an optimal portfolio of activities. However, often practitioners instead form a rank-ordered list of activities and select those with the highest priority. We account for both viewpoints. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework, and we apply our approach to a facility location problem and a multidimensional knapsack problem. We develop two sets of cutting planes to improve computation. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1865. Keywords: priority lists; activity-selection problem; stochastic programming; cutting planes History : Received December 20, 2011; accepted February 17, 2013, by Dimitris Bertsimas, optimization. Published online in Articles in Advance May 15, 2014., 1. Introduction Resource-constrained activity-selection problems (RCASPs) involve binary activity-selection decisions, which compete for scarce resources. The bulk of the current literature approaches RCASPs by forming an optimal portfolio of activities [...]
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- 2015
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8. Optimizing distribution of pandemic influenza antiviral drugs
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Singh, Bismark, Huang, Hsin-Chan, Morton, David P., Johnson, Gregory P., Gutfraind, Alexander, Galvani, Alison P., Clements, Bruce, and Meyers, Lauren A.
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Texas. Department of State Health Services -- Analysis ,Wal-Mart Stores Inc. ,Walgreen Co. ,Discount stores -- Analysis ,Antiviral agents -- Analysis ,Drugstores -- Analysis ,Influenza -- Drug therapy -- Analysis ,Pharmacy -- Analysis ,Health - Abstract
Influenza pandemics occur when novel strains of the influenza virus emerge in human populations and spread worldwide (1). There were 3 influenza pandemics in the 20th century (1918, 1957, and [...]
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- 2015
- Full Text
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9. Convex approximations of a probabilistic bicriteria model with disruptions
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Rengarajan, Tara, Dimitrov, Nedialko, and Morton, David P.
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Stochastic programming -- Methods ,Approximation theory -- Methods ,Mathematical optimization ,Risk assessment -- Methods ,Computers ,Science and technology - Abstract
We consider a multiperiod system operation problem with two conflicting objectives, minimizing cost and risk. Risk sterns from uncertain disruptions to the system during operation. Whereas a general model would [...]
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- 2013
10. Prioritizing project selection
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Koc, Ali, Morton, David P., Popova, Elmira, Hess, Stephen M., Kee, Ernie, and Richards, Drew
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Investment analysis -- Forecasts and trends ,Capital budgets -- Forecasts and trends ,Market trend/market analysis ,Business ,Petroleum, energy and mining industries - Abstract
We consider capital investments under uncertainty. A typical approach to this problem, when the problem parameters are assumed known, is via a multi-knapsack model. This model takes as input annual budgets as well as the cost streams and profit--i.e., net present value (NPV)--of each project. Its output is a portfolio of projects with the highest total NPV, observing yearly budget constraints. We argue that such a portfolio fails to hedge against uncertainties in the budgets, the cost streams, and the profits. As an alternative, we propose a model that forms an optimal priority list of projects, incorporating multiple scenarios for these input parameters. We apply our approach to two sets of example projects from the South Texas Project Nuclear Operating Company. DOI: 10.1080/00137910903338545, INTRODUCTION When practitioners plan for capital budgeting they often form a priority list of candidate projects, by scoring the projects individually, using economic measures like net present value, benefit-investment ratio, [...]
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- 2009
11. Models for nuclear smuggling interdiction
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Morton, David P., Pan, Feng, and Saeger, Kevin J.
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Smuggling -- Control ,Stochastic programming -- Usage ,Integer programming -- Usage ,Business ,Engineering and manufacturing industries - Abstract
We describe two stochastic network interdiction models for thwarting nuclear smuggling. In the first model, the smuggler travels through a transportation network on a path that maximizes the probability of evading detection, and the interdictor installs radiation sensors to minimize that evasion probability. The problem is stochastic because the smuggler's origin-destination pair is known only through a probability distribution at the time when the sensors are installed. In this model, the smuggler knows the locations of all sensors and the interdictor and the smuggler 'agree' on key network parameters, namely the probabilities the smuggler will be detected while traversing the arcs of the transportation network. Our second model differs in that the interdictor and smuggler can have differing perceptions of these network parameters. This model captures the case in which the smuggler is aware of only a subset of the sensor locations. For both models, we develop the important special case in which the sensors can only be installed at border crossings of a single country so that the resulting model is defined on a bipartite network. In this special case, a class of valid inequalities reduces the computation time for the identical-perceptions model. Keywords: Network interdiction, integer programming, stochastic programming, 1. Introduction We develop stochastic network interdiction models designed to locate radiation sensors, which detect gamma and neutron emissions from nuclear material, at critical border crossings in the Former Soviet [...]
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- 2007
12. Linear programming approach to optimize strategic investment in the construction workforce
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Srour, Issam M., Haas, Carl T., and Morton, David P.
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Construction workers -- Training ,Construction workers -- Recruiting ,Construction industry -- Human resource management ,Industry hiring ,Company personnel management ,Construction and materials industries ,Engineering and manufacturing industries ,Science and technology - Abstract
The construction industry in the United States and other parts of the world has been facing several challenges, including a shortage of skilled workers. A review of the relevant body of knowledge indicates that one of the key reasons for this problem is the absence of human resource management strategies for construction workers at project, corporate, regional, or industry levels. This paper addresses the issues of workforce training and allocation on construction projects. It presents a framework to optimize the investment in, and to make the best use of, the available workforce with the intent to reduce project costs and improve schedule performance. A linear program model, entitled the Optimal Workforce investment Model, is built to provide an optimization-based framework for matching supply and demand of construction labor most efficiently through training, recruitment, and allocation. Given a project schedule or demand profile and the available pool of workers, the suggested model provides human resource managers a combined strategy for training the available workers and hiring additional workers. The input data to the proposed model consists of a certain available labor pool, cost figures for training workers in different skills, the cost of hiring workers, hourly labor wages, and estimates of affinities between the different considered skills. The objective of the model is to minimize labor costs while satisfying project labor demands. Results from application of the model to typical situations are presented, and recommendations for future developments are made. CE Database subject headings: Construction industry: Optimization: Training; Employees; Computer programming.
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- 2006
13. Second-order lower bounds on the expectation of a convex function
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Dokov, Steftcho P. and Morton, David P.
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Management science -- Analysis ,Stochastic programming -- Analysis ,Approximation theory -- Analysis ,Business ,Computers and office automation industries ,Mathematics ,Analysis - Abstract
We develop a class of lower bounds on the expectation of a convex function. The bounds utilize the first two moments of the underlying random variable, whose support is contained [...]
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- 2005
14. A Bayesian stochastic programming approach to an employee scheduling problem
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Morton, David P. and Popova, Elmira
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Business ,Engineering and manufacturing industries - Abstract
Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in nonstationary systems using limited historical data. Unlike deterministic optimization, stochastic programs explicitly incorporate distributions for random parameters in the model formulation, and thus have the advantage that the resulting solutions more fully hedge against future contingencies. In this paper, we exploit the strengths of Bayesian prediction and stochastic programming in a rolling-horizon approach that can be applied to solve real-world problems. We illustrate the methodology on an employee production scheduling problem with uncertain up-times of manufacturing equipment and uncertain production rates. Computational results indicate the value of our approach., 1. Introduction We consider a production planning problem in which a nominal weekday employee schedule is published at the beginning of each month in the face of uncertain production rates [...]
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- 2004
15. Stochastic vehicle routing with random travel times
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Kenyon, Astrid S. and Morton, David P.
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Science and technology ,Social sciences ,Transportation industry - Abstract
We consider stochastic vehicle routing problems on a network with random travel and service times. A fleet of one or more vehicles is available to be routed through the network [...]
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- 2003
16. Assignment and allocation optimization of partially multiskilled workforce
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Gomar, Jorge E., Haas, Carl T., and Morton, David P.
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Construction workers -- Management ,Skilled labor -- Management ,Resource allocation -- Methods ,Construction and materials industries ,Engineering and manufacturing industries ,Science and technology - Abstract
Multiskilling is a workforce strategy that has been shown to reduce indirect labor costs, improve productivity, and reduce turnover. A multiskilled workforce is one in which the workers possess a range of skills that allow them to participate in more than one work process. In practice, they may work across craft boundaries. The success of multiskilling greatly relies on the foreman's ability to assign workers to appropriate tasks and to compose crews effectively. The foreman assigns tasks to workers according to their knowledge, capabilities, and experience on former projects. This research investigated the mechanics of allocating a multiskilled workforce and developed a linear programming model to help optimize the multiskilled workforce assignment and allocation process in a construction project, or between the projects of one company. It is concluded that the model will be most useful in conditions where full employment does not exist; however, it is also useful for short term allocation decisions. By running the model for various simulated scenarios, additional observations were made. For example, it is concluded that, for a capital project, the benefits of multiskilling are marginal beyond approximately a 20% concentration of multiskilled workers in a project workforce. Benefits to workers themselves become marginal after acquiring competency in two or three crafts. These observations have been confirmed by field experience. Extension of this model to allocation of multifunctional resources, such as construction equipment, should also be possible. DOI: 10.1061/(ASCE)0733-9364(2002)128:2(103) CE Database keywords: Construction industry; Personnel management; Optimization.
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- 2002
17. A sequential sampling procedure for stochastic programming
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Bayraksan, Guzin and Morton, David P.
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Management science -- Analysis ,Stochastic programming -- Research -- Methods ,Simulation methods -- Research ,Business ,Mathematics - Abstract
We develop a sequential sampling procedure for a class of stochastic programs. We assume that a sequence of feasible solutions with an optimal limit point is given as input to [...]
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- 2011
18. Optimizing benchmark-based portfolios with hedge funds
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Popova, Ivilina, Morton, David P., Popova, Elmira, and Yau, Jot
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Hedge funds -- Analysis ,Investment analysis -- Evaluation ,Banking, finance and accounting industries ,Business ,Business, international - Abstract
The significant growth of the hedge funds industry in the past decade has been supplemented by increased allocations to alternative investments by high-net-worth individuals as well as endowments and foundations. [...]
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- 2007
19. Efficient fund of hedge funds construction under downside risk measures
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Morton, David P., Popova, Elmira, and Popova, Ivilina
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Monte Carlo method ,Hedge funds ,Investment analysis ,Banking, finance and accounting industries ,Business - Abstract
To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbankfin.2005.04.016 Byline: David P. Morton (a), Elmira Popova (a), Ivilina Popova (b) Keywords: Portfolio choice; Expected regret; Hedge funds; Fund of funds; Portfolio optimization; Monte Carlo simulation Abstract: We consider portfolio allocation in which the underlying investment instruments are hedge funds. We consider a family of utility functions involving the probability of outperforming a benchmark and expected regret relative to another benchmark. Non-normal return vectors with prescribed marginal distributions and correlation structure are modeled and simulated using the normal-to-anything method. A Monte Carlo procedure is used to obtain, and establish the quality of, a solution to the associated portfolio optimization model. Computational results are presented on a problem in which we construct a fund of 13 CSFB/Tremont hedge-fund indices. Author Affiliation: (a) Graduate Program in Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, TX 78712, USA (b) Albers School of Business and Economics, Seattle University, Seattle, WA 98122, USA
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- 2006
20. Efficient fund of hedge funds construction under downside risk measures
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Morton, David P., Popova, Elmira, and Popova, Ivilina
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Hedge funds -- Management ,Risk management -- Methods ,Stocks -- Prices and rates ,Stocks -- Evaluation ,Risk management ,Company business management ,Banking, finance and accounting industries ,Business - Abstract
Management of hedge funds in downside stock prices scenario and risk management concepts for this are presented.
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- 2006
21. Optimizing military airlift
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Baker, Steven F., Morton, David P., Rosenthal, Richard E., and Williams, Laura Melody
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Airlift, Military -- Analysis ,Management science -- Analysis ,Business ,Mathematics ,Analysis - Abstract
We describe a large-scale linear programming model for optimizing strategic (intercontinental) airlift capability. The model routes cargo and passengers through a specified transportation network with a given fleet of aircraft [...]
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- 2002
22. Restricted-recourse bounds for stochastic linear programming
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Morton, David P. and Wood, R. Kevin
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Stochastic programming -- Research ,Linear programming -- Research ,Business ,Mathematics - Abstract
The authors present new techniques for solving the problem of bounding the expected value of a stochastic linear program. Topics include the dual restricted-recourse bound and specifications for the model.
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- 1999
23. Stopping rules for a class of sampling-based stochastic programming algorithms
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Morton, David P.
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Stochastic programming -- Analysis ,Algorithms -- Analysis ,Optimal stopping (Mathematical statistics) -- Analysis ,Statistical sampling -- Analysis ,Business ,Mathematics - Abstract
Most sampling-based stochastic based algorithms that perform repeated samples during the solution procedure for stochastic programs do not have stopping rules that can manage a priori the quality of the proposed solution. In this light, a study was conducted with the aim of formulating rules that can address this flaw for a specific type of sampling-based algorithms. The stopping rules developed have two components. One is a criteria for the termination of the algorithm and the other is a rule for the selection of the sample sizes. The stopping rules were found to ensure asymptotic validity of the targeted confidence interval statements as the interval width decreases and the sample sizes increase.
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- 1998
24. Stochastic network interdiction
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Cormican, Kelly J., Morton, David P., and Wood, R. Kevin
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Stochastic processes -- Analysis ,Computer networks -- Models ,Business ,Mathematics - Abstract
A study was conducted to characterize a stochastic network interdiction that attempts to destroy network components. The stochastic network interdiction problem was established with uncertain, binary interdiction successes to reduce achievable flow using a set of arcs. The minimization of the interdiction problem was represented with first-stage variables and random parameters. A number of variants were also establish to support nonnegative values, binary and uncertain arc capacities, and multiple uncertain interdictions on an arc. Results showed that the interdiction model reduces concave function based on the convex hull of the integer first-stage feasible region.
- Published
- 1998
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