Multi Carrier Code Division Multiple Access (MC-CDMA), a promising technology for the 4G communication systems is considered in this paper. The foremost limitation of such system is the Multiple Access Interference (MAI) which is due to frequency-selective fading, near-far effect, frequency offset, and nonlinear power amplification due to clipping noise. The performance of MC-CDMA under such scenario is poor and optimal detection is one of the solutions with a high complexity is required. In this paper the Bit Error Rate (BER) performance is compared under clipping noise with sphere decoding and Global search algorithm based Multiuser detectors

This paper describes a model-based decision support system (DSS) for reservoir engineers who may wish to use simpler analytical methods to optimize waterfloods in large waterflood operations, as is common in many oil and gas companies. The engineers select a set of existing injector and producer wells, and a set of past time periods over which the model will be fitted. The model provides outputs which support waterflooding decisions, using only oil, water and gas production and bottom hole pressure at producing wells, and water injection at each injection well in each time period. The model relating injection and production is a set of discrete-time difference equations, from the family of capacitance-resistance models. We focus on the model fitting process, which uses the least squares approach, and leads to a linearly constrained nonlinear optimization problem with non-quadratic objective. The DSS is coded in the GAMS modeling language. A numerical example is provided, and some experience with the system is described. An Excel file with the data used is available as Supplementary Online material accompanying the article on the Springer website.

Abstract This paper describes the application of capacitance resistive modeling (CRM) to 99 wells in a waterflooded field in the Permian Basin of West Texas. CRM is a quantitative technique based on material balance that requires only injection, production rates and well coordinates to identify and quantify interwell connectivity in a waterflood. The study uses CRM results to optimize oil production by reallocating water injection and then assesses the resulting improvement by analyzing field data after water injection has been rescheduled. This first field implementation of CRM theory shows that the oil production rate has increased 45 bbl/day and the cumulative incremental oil production in the first year is 5, 372 bbls, equivalent to $402, 900 at $75/bbl. This work serves as a demonstration of how to apply and analyze CRM results. Before optimizing injection, interwell connectivities were established by fitting CRM over an appropriate fitting window selected using field events. The forecasting ability of the model was examined by fitting part of the historical data and then using the fitted model to forecast the remainder of the historical data. CRM interwell connectivities were used to optimize future production for the next five years by changing water injection rates. The optimized injection rates were applied for one year and the resulting production rates were analyzed. The oil production rates increase after injection rates were rescheduled according to the CRM optimization. This first field test of CRM technology has demonstrated that CRM has the advantages of short computation time and of using readily available field data, and disadvantages of sensitivity to reservoir events and data errors. The connectivities are consistent with field geological knowledge. CRM can be used to improve oil production with little additional cost. The case study shows that CRM is a simple yet powerful tool for engineers in planning and monitoring waterflood. The work increases confidence in the technique by demonstrating improvements made by using CRM in a practical context, and identifying shortcomings of the technique. CRM should be applied with knowledge of the field geology and history to understand the results and use them to enhance waterflood performance.

Motivated by the successful use of a pseudo-cut strategy within the setting of constrained nonlinear and nonconvex optimization in Lasdon et al. (2010), we propose a framework for general pseudo-cut strategies in global optimization that provides a broader and more comprehensive range of methods. The fundamental idea is to introduce linear cutting planes that provide temporary, possibly invalid, restrictions on the space of feasible solutions, as proposed in the setting of the tabu search metaheuristic in Glover (1989), in order to guide a solution process toward a global optimum, where the cutting planes can be discarded and replaced by others as the process continues. These strategies can be used separately or in combination, and can also be used to supplement other approaches to nonlinear global optimization. Our strategies also provide mechanisms for generating trial solutions that can be used with or without the temporary enforcement of the pseudo-cuts.

The problem of finding a global optimum of a constrained multimodal function has been the subject of intensive study in recent years. Several effective global optimization algorithms for constrained problems have been developed; among them, the multi-start procedures discussed in Ugray et al. [1] are the most effective. We present some new multi-start methods based on the framework of adaptive memory programming (AMP), which involve memory structures that are superimposed on a local optimizer. Computational comparisons involving widely used gradient-based local solvers, such as Conopt and OQNLP, are performed on a testbed of 41 problems that have been used to calibrate the performance of such methods. Our tests indicate that the new AMP procedures are competitive with the best performing existing ones.

In this study we describe a stochastic optimization model for the relocation of deployable military hospitals, the reallocation of hospital beds, and the emplacement of tactical medical evacuation assets (medical evacuation helicopters and ground ambulances) during steady-state military combat operations (stability operations). The network model is built around an intuitive objective function, one that is derived from military doctrine. The objective to be minimized is the time traveled, weighted by patient severity, from the evacuation site to the point of injury and onward to the hospital location. The optimal solution also determines the number of air and ground ambulances and the hospital beds of each type required at each selected site. Since future casualty locations, numbers, and severities are uncertain, this information is treated as a number of casualty scenarios with assigned scenario probabilities. The number, location, and severities of casualties can be randomly generated, or provided as part of a planning process. The model then seeks a single set of hospital and vehicle locations, plus the paths the evacuation assets should take in each scenario, which minimize expected travel time over all scenarios. The scenario generator is based on realistic historical data from Operation Iraqi Freedom. Since mobile hospitals provide the primary surgical treatment intervention while dedicated ground and air evacuation assets provide the transportation along evacuation paths, the study objective is important for military medical planners, especially those involved in tactical medical evacuation and treatment planning.

AbstractOil production strategies traditionally attempt to combine and balance complex geophysical, petrophysical, thermodynamic and economic factors to determine an optimal method to recover hydrocarbons from a given reservoir. Reservoir simulators have traditionally been too large and run times too long to allow for rigorous solution in conjunction with an optimization algorithm. It has also proven very difficult to marry an optimizer with the large set of nonlinear partial differential equations required for accurate reservoir simulation.A simple capacitance-resistive model that characterizes the connectivity between injection and production wells can determine an injection scheme that maximizes the value of the reservoir asset. Model parameters are identified using linear and nonlinear regression. The model is then used together with a nonlinear optimization algorithm to compute a set of future injection rates which maximize discounted net profit. Research previously conducted has shown that this simple dynamic model provides an excellent match to historic data. Based on a number of simulated and two actual fields, the optimal injection schemes based on the capacitance-resistive model yield a predicted increase in hydrocarbon recovery of up to 60% over the extrapolated historic decline.An advantage of using a simple model is its ability to describe large scale systems in a straightforward way with computation times that are short to moderate. However, applying the capacitance-resistive model to large reservoirs with many wells presents several new challenges. Reservoirs with hundreds of wells have longer production histories that often represent a variety of different reservoir conditions. New wells are created, wells are shut in for varying periods of time and production wells are converted to injection wells. Additionally, history matching large reservoirs by nonlinear regression is more likely to produce parameters that are statistically insignificant, resulting in a parameter dense model that does not accurately reflect the physical properties of the reservoir. Several modeling techniques and heuristics are presented that provide a simple, accurate reservoir model that can be used to optimize the value of the reservoir over future time periods.

We present results of extensive computational tests of (i) comparing dynamic filters (first mentioned in an earlier publication addressing a feasibility seeking algorithm) with static filters and (ii) stochastic starting point generators (‘drivers’) for a multi-start global optimization algorithm called MSNLP (Multi-Start Non-Linear Programming). We show how the widely used NLP local solvers CONOPT and SNOPT compare when used in this context. Our computational tests utilize two large and diverse sets of test problems. Best known solutions to most of the problems are obtained competitively, within 30 solver calls, and the best solutions are often located in the first ten calls. The results show that the addition of dynamic filters and new global drivers can contribute to the increased reliability of the MSNLP algorithmic framework.

Acknowledgments The research presented in this dissertation would not have started, continued or turned into this final form without the guidance,and support (financial and otherwise) of my adviser, Dr. David Morton. I have learned a lot from him, not only about a wide range of topics in operations research and stochastic programming,but on all aspects of research and academic,life. I

The authors investigated cost models that incorporate quality, access, and efficiency to provide decision support for resource forecasting in the multi-billion-dollar U.S. Army health system. As the Army relocates thousands of troops, the medical system must plan for changes in demand; this study supports that effort. Loglinear cost models that include data envelopment analysis (DEA) efficiency scores were evaluated through ordinary least squares estimation, ridge regression, and robust regression, and serve as the analytical framework. Parsimonious models that incorporate a simple volume-complexity metric, a DEA metric, a quality metric, and medical center status variable provide superior forecasting capability.

This paper describes modifications to two multistart algorithms for global optimization which enable them to find feasible solutions to a system of nonlinear constraints more efficiently. The multistart algorithms, called OptQuest-NLP (OQNLP) and Multistart-NLP (MSNLP), start a local NLP Solver from a set of starting points and return the best solution found. Candidate starting points are generated either by a scatter search heuristic or by a randomized process. Two adaptive filters choose a small subset of the candidate points as starting points. The modifications to facilitate feasibility seeking include replacing the exact penalty function used to measure the goodness of a starting point with the sum of infeasibilities, and terminating when a feasible solution is found. We describe experimental results on a large and diverse set of smooth nonlinear nonconvex problems coded in the GAMS modeling language. These are chosen so that a single application of a selected solver from the user-specified starting point terminates infeasible, yet the problems all have feasible solutions. Our results show that MSNLP's feasibility mode is able to find feasible solutions to almost all problems. It is moderately faster than MSNLP not using feasibility mode, and is somewhat better a finding feasible solutions when they exist. It is now an option within MSNLP, and can be invoked by inserting an appropriate record into the options file.

The algorithm described here, called OptQuest/NLP or OQNLP, is a heuristic designed to find global optima for pure and mixed integer nonlinear problems with many constraints and variables, where all problem functions are differentiable with respect to the continuous variables. It uses OptQuest, a commercial implementation of scatter search developed by OptTek Systems, Inc., to provide starting points for any gradient-based local solver for nonlinear programming (NLP) problems. This solver seeks a local solution from a subset of these points, holding discrete variables fixed. The procedure is motivated by our desire to combine the superior accuracy and feasibility-seeking behavior of gradient-based local NLP solvers with the global optimization abilities of OptQuest. Computational results include 155 smooth NLP and mixed integer nonlinear program (MINLP) problems due to Floudas et al. (1999), most with both linear and nonlinear constraints, coded in the GAMS modeling language. Some are quite large for global optimization, with over 100 variables and 100 constraints. Global solutions to almost all problems are found in a small number of local solver calls, often one or two.

Abstract Methods to obtain optimal portfolios have been used extensively in the financial community for several decades. The first methods were published by Markowitz in the 50's and the literature has been vastly expanded since these days. Most discussions and publications to date have been centered on the methods to perform portfolio optimization. However, the greatest value added by portfolio optimization is not in the optimization itself, but on the use of the results as an aid to decision making. Unfortunately, little research has been done on this behalf. This paper proposes a new approach, and intends to bridge this gap showing concrete portfolio management applications. Portfolio optimization is not a single event, on the contrary, it is an iterative procedure where the results gathered from each iteration are used to feed the decision making process. After some decisions are made, or company policies established, the process must be repeated to observe the results of the simulation and to provide feedback to the decision making process. This paper describes three portfolio applications that aid corporate decision makers. The first application deals with the effect that production and capital constraints have on the portfolio optimization process. These constraints assist management in the setting of realistic company goals. The second application explains how acquisitions and divestures can be evaluated in the portfolio context, and the effect that those projects have on the company objectives. The third application explains the use of portfolio optimization to manage and drive the company strategy. Using these methods, management could manage acquisitions and divestures in a proactive way, set realistic goals and objectives. As a result, management gain more control and insight over the company future.

This paper describes scenario generators and portfolio models for oil and gas exploration and production (E&P) planning. The problem is how to allocate funds to a set of oil recovery projects to achieve a desired tradeoff between risk and return. A mix of development and exploration projects is assumed, further stratified by reservoir depth and uncertainty level of reserves. The scenario generator incorporates distributions for seven uncertain reservoir parameters, obtained from engineering and geologic data in the Tertiary Oil Recovery Information System (TORIS) database (http://www.netl.doe.gov/scngo/Petroleum/Software/database.html). Capital and operating expenditure data comes from the IHS Energy Discovery Module (www.ihsenergy.com). A slightly compressible-liquid tank model is used to model the yearly oil production from each well in each project. Oil price trajectories are generated using a mean reverting model. A complex income and tax calculation evaluates the net present value (NPV) of free cash flow, and a Monte Carlo simulation generates NPV scenarios for each project. We describe and compare optimization models that minimize several risk measures, including variance, semivariance, expected loss, and loss probability, as well as models that maximize expected utility. Risk measure values, optimal project weights (assuming any level of fractional participation is allowed in all projects), and portfolio NPV distributions arising from the various risk measures are compared, and contrasted with results of widely used ranking procedures that ignore risk.

This study illustrates the feasibility of incorporating technical efficiency considerations in the funding of military hospitals and identifies the primary drivers for hospital costs. Secondary data collected for 24 U.S.-based Army hospitals and medical centers for the years 2001 to 2003 are the basis for this analysis. Technical efficiency was measured by using data envelopment analysis; subsequently, efficiency estimates were included in logarithmic-linear cost models that specified cost as a function of volume, complexity, efficiency, time, and facility type. These logarithmic-linear models were compared against stochastic frontier analysis models. A parsimonious, three-variable, logarithmic-linear model composed of volume, complexity, and efficiency variables exhibited a strong linear relationship with observed costs (R2 = 0.98). This model also proved reliable in forecasting (R2 = 0.96). Based on our analysis, as much as $120 million might be reallocated to improve the United States-based Army hospital performance evaluated in this study.