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Decision Variables (decision + variable)
Selected AbstractsOptimal Contracts when Enforcement is a Decision Variable: A ReplyECONOMETRICA, Issue 1 2003Stefan Krasa No abstract is available for this article. [source] Fast computation evolutionary programming algorithm for the economic dispatch problemEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 1 2006P. Somasundaram Abstract This paper essentially aims to propose a new EP based algorithm for solving the ED problem. The ED problem is solved using EP with system lambda as decision variable and power mismatch as fitness function. The algorithm is made fast through judicious modifications in initialization of the parent population, offspring generation and selection of the normal distribution curve. The proposed modifications reduce the search region progressively and generate only effective offsprings. The proposed algorithm is tested on a number of sample systems with quadratic cost function and also on a 10-unit system with piecewise quadratic cost function. The computational results reveal that the proposed algorithm has an excellent convergence characteristic and is superior to other EP based methods in many respects. Copyright © 2005 John Wiley & Sons, Ltd. [source] An Integer Linear Programming Problem with Multi-Criteria and Multi-Constraint Levels: a Branch-and-Partition AlgorithmINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 5 2001Jun Li In this paper, we propose a branch-and-partition algorithm to solve the integer linear programming problem with multi-criteria and multi-constraint levels (MC-ILP). The procedure begins with the relaxation problem that is formed by ignoring the integer restrictions. In this branch-and-partition procedure, an MC linear programming problem is adopted by adding a restriction according to a basic decision variable that is not integer. Then the MC-simplex method is applied to locate the set of all potential solutions over possible changes of the objective coefficient parameter and the constraint parameter for a regular MC linear programming problem. We use parameter partition to divide the (,, ,) space for integer solutions of MC problem. The branch-and-partition procedure terminates when every potential basis for the relaxation problem is a potential basis for the MC-ILP problem. A numerical example is used to demonstrate the proposed algorithm in solving the MC-ILP problems. The comparison study and discussion on the applicability of the proposed method are also provided. [source] Multi-objective optimization of venturi scrubbers using a three-dimensional model for collection efficiency,JOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 2-3 2003Gopalan Ravi Abstract Multi-objective optimization of a venturi scrubber was carried out using a three-dimensional model for collection efficiency and non-dominated sorting genetic algorithm (NSGA). Two objective functions, namely (a) maximization of the overall collection efficiency, and (b) minimization of the pressure drop were used in this study. Three decision variables including two operating parameters, viz liquid,gas ratio and gas velocity in the throat, and the nozzle configuration, which takes into account the three-dimensional nature of the problem, were used in the optimization. Optimal design curves (non-dominated Pareto sets) and the values of the decision variables corresponding to optimum conditions on the Pareto set for a pilot-scale scrubber were obtained. The liquid to gas (L/G) ratio, which is a key decision variable that determines the uniformity of liquid distribution, and a staggered nozzle configuration can produce uniform liquid distribution in the scrubber. Multiple penetration using nozzles of two different sizes in a triangular staggered arrangement can reduce liquid loading by as much as 50%, consequently reducing the pressure drop in the scrubber. © 2003 Society of Chemical Industry [source] COMPLEX METHOD FOR NONLINEAR CONSTRAINED MULTI-CRITERIA (MULTI-OBJECTIVE FUNCTION) OPTIMIZATION of THERMAL PROCESSINGJOURNAL OF FOOD PROCESS ENGINEERING, Issue 4 2003FERRUH ERDO ABSTRACT The goal in a multi-objective function optimization problem is to optimize the several objective functions simultaneously. the complex method is a powerful algorithm to find the optimum of a general nonlinear function within a constrained region. the objective of this study was to apply the complex method to two different shapes (a sphere and a finite cylinder) subjected to the same thermal processing boundary conditions to find a variable process temperature profile (decision variable) to maximize the volume-average retention of thiamine. A process temperature range of 5 to 150C was used as an explicit constraint. Implicit constraints were center temperature and accumulated center lethality of the sphere and the finite cylinder. the objective functions for both shapes were combined into a single one using a weighting method. Then, the previously developed complex algorithm was applied using Lexicographic Ordering to order the objective functions with respect to their significance. the results were reported as optimum variable process temperature profiles using the given geometries and objective functions. the thiamine retentions were also compared with a constant process temperature process, and 3.0% increase was obtained in the combined objective function. the results showed that the complex method can be successfully used to predict the optimum variable process temperature profiles in multi-criteria thermal processing problems. [source] A note on optimal pricing for finite capacity queueing systems with multiple customer classesNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 5 2008Serhan Ziya Abstract This article investigates optimal static prices for a finite capacity queueing system serving customers from different classes. We first show that the original multi-class formulation in which the price for each class is a decision variable can be reformulated as a single dimensional problem with the total load as the decision variable. Using this alternative formulation, we prove an upper bound for the optimal arrival rates for a fairly large class of queueing systems and provide sufficient conditions that ensure the existence of a unique optimal arrival rate vector. We show that these conditions hold for M/M/1/m and M/G/s/s systems and prove structural results on the relationships between the optimal arrival rates and system capacity. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008 [source] Optimal switchover times between two activities utilizing the same resourceNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 2 2002M. Karakul Abstract The "gold-mining" decision problem is concerned with the efficient utilization of a delicate mining equipment working in a number of different mines. Richard Bellman was the first to consider this type of a problem. The solution found by Bellman for the finite-horizon, continuous-time version of the problem with two mines is not overly realistic since he assumed that fractional parts of the same mining equipment could be used in different mines and this fraction could change instantaneously. In this paper, we provide some extensions to this model in order to produce more operational and realistic solutions. Our first model is concerned with developing an operational policy where the equipment may be switched from one mine to the other at most once during a finite horizon. In the next extension we incorporate a cost component in the objective function and assume that the horizon length is not fixed but it is the second decision variable. Structural properties of the optimal solutions are obtained using nonlinear programming. Each model and its solution is illustrated with a numerical example. The models developed here may have potential applications in other areas including production of items requiring the same machine or choosing a sequence of activities requiring the same resource. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 186,203, 2002; DOI 10.1002/nav.10008 [source] Optimal control of a revenue management system with dynamic pricing facing linear demandOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 6 2006Fee-Seng Chou Abstract This paper considers a dynamic pricing problem over a finite horizon where demand for a product is a time-varying linear function of price. It is assumed that at the start of the horizon there is a fixed amount of the product available. The decision problem is to determine the optimal price at each time period in order to maximize the total revenue generated from the sale of the product. In order to obtain structural results we formulate the decision problem as an optimal control problem and solve it using Pontryagin's principle. For those problems which are not easily solvable when formulated as an optimal control problem, we present a simple convergent algorithm based on Pontryagin's principle that involves solving a sequence of very small quadratic programming (QP) problems. We also consider the case where the initial inventory of the product is a decision variable. We then analyse the two-product version of the problem where the linear demand functions are defined in the sense of Bertrand and we again solve the problem using Pontryagin's principle. A special case of the optimal control problem is solved by transforming it into a linear complementarity problem. For the two-product problem we again present a simple algorithm that involves solving a sequence of small QP problems and also consider the case where the initial inventory levels are decision variables. Copyright © 2006 John Wiley & Sons, Ltd. [source] A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making,DECISION SCIENCES, Issue 1 2005D. J. Van Der Zee ABSTRACT Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time-indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object-oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads. [source] Energy, exergy and exergoeconomic analysis of a steam power plant: A case studyINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 5 2009Mohammad Ameri Abstract The objective of this paper is to perform the energy, exergy and exergoeconomic analysis for the Hamedan steam power plant. In the first part of the paper, the exergy destruction and exergy loss of each component of this power plant is estimated. Moreover, the effects of the load variations and ambient temperature are calculated in order to obtain a good insight into this analysis. The exergy efficiencies of the boiler, turbine, pump, heaters and the condenser are estimated at different ambient temperatures. The results show that energy losses have mainly occurred in the condenser where 306.9,MW is lost to the environment while only 67.63,MW has been lost from the boiler. Nevertheless, the irreversibility rate of the boiler is higher than the irreversibility rates of the other components. It is due to the fact that the combustion reaction and its high temperature are the most significant sources of exergy destruction in the boiler system, which can be reduced by preheating the combustion air and reducing the air,fuel ratio. When the ambient temperature is increased from 5 to 24°C, the irreversibility rate of the boiler, turbine, feed water heaters, pumps and the total irreversibility rate of the plant are increased. In addition, as the load varies from 125 to 250,MW (i.e. full load) the exergy efficiency of the boiler and turbine, condenser and heaters are increased due to the fact that the power plant is designed for the full load. In the second part of the paper, the exergoeconomic analysis is done for each component of the power plant in order to calculate the cost of exergy destruction. The results show that the boiler has the highest cost of exergy destruction. In addition, an optimization procedure is developed for that power plant. The results show that by considering the decision variables, the cost of exergy destruction and purchase can be decreased by almost 17.11%. Copyright © 2008 John Wiley & Sons, Ltd. [source] Exergoeconomic optimization of a 1000,MW light water reactor power generation systemINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 4 2009Hoseyn Sayyaadi Abstract A typical 1000,MW pressurized water reactor nuclear power plant is considered for optimization. The thermodynamic modeling is performed based on the energy and exergy analysis, while an economic model is developed according to the total revenue requirement method. The objective function based on the exergoeconomic analysis is obtained. The exergoeconomic optimization process with 10 decision variables is performed using a hybrid stochastic/deterministic search algorithm namely as genetic algorithm. The results that are obtained using optimization process are compared with the base case system and the discussion is presented. Copyright © 2009 John Wiley & Sons, Ltd. [source] A fuzzy goal programming procedure for solving quadratic bilevel programming problemsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2003Bijay Baran Pal This article presents a fuzzy goal programming (FGP) procedure for solving quadratic bilevel programming problems (QBLPP). In the proposed approach, the membership functions for the defined fuzzy objective goals of the decision makers (DM) at both the levels are developed first. Then, a quadratic programming model is formulated by using the notion of distance function minimizing the degree of regret to satisfaction of both DMs. At the first phase of the solution process, the quadratic programming model is transformed into an equivalent nonlinear goal programming (NLGP) model to maximize the membership value of each of the fuzzy objective goals on the extent possible on the basis of their priorities in the decision context. Then, at the second phase, the concept of linear approximation technique in goal programming is introduced for measuring the degree of satisfaction of the DMs at both the levels by arriving at a compromised decision regarding the optimality of two different sets of decision variables controlled separately by each of them. A numerical example is provided to illustrate the proposed approach. © 2003 Wiley Periodicals, Inc. [source] Modeling and optimization of cylindrical antennas using the mode-expansion method and genetic algorithmsINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 6 2005Dawei Shen Abstract For monopole antennas with cylindrically symmetric structures, a mode-expansion method is highly time efficient, which is a realistic approach for integrating function-optimization tools, such as genetic algorithms (GAs), in order to extract the best bandwidth property. In this article, a mode-expansion method is used to simulate the impedance characteristics of the cylindrical antennas. As examples, two new types of monopole antennas are presented, one of which possesses a two-step top-hat structure while the other has an annulus around the stem. After the modeling scheme is examined for convergence and data validity, the associated optimization problem, with dimensions as decision variables, structural limitations as linear constraints, and desired bandwidth performance as an objective function, is solved using GAs. The effects of the geometric parameters on the impedance characteristics are investigated in order to demonstrate the optimality of the calculated solutions. Two optimized practical antennas are designed based on our numerical studies. One has a broad bandwidth of 3 GHz while the other shows a dual-band property, which can satisfy the bandwidth requirements for both Bluetooth (2.45-GHz band) and WLAN (5-GHz band) systems. © 2005 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005. [source] A team algorithm for robust stability analysis and control design of uncertain time-varying linear systems using piecewise quadratic Lyapunov functionsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 4 2001H. L. S. Almeida Abstract A team algorithm based on piecewise quadratic simultaneous Lyapunov functions for robust stability analysis and control design of uncertain time-varying linear systems is introduced. The objective is to use robust stability criteria that are less conservative than the usual quadratic stability criterion. The use of piecewise quadratic Lyapunov functions leads to a non-convex optimization problem, which is decomposed into a convex subproblem in a selected subset of decision variables, and a lower-dimensional non-convex subproblem in the remaining decision variables. A team algorithm that combines genetic algorithms (GA) for the non-convex subproblem and interior-point methods for the solution of linear matrix inequalities (LMI), which form the convex subproblem, is proposed. Numerical examples are given, showing the advantages of the proposed method. Copyright © 2001 John Wiley & Sons, Ltd. [source] Multi-objective optimization of venturi scrubbers using a three-dimensional model for collection efficiency,JOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 2-3 2003Gopalan Ravi Abstract Multi-objective optimization of a venturi scrubber was carried out using a three-dimensional model for collection efficiency and non-dominated sorting genetic algorithm (NSGA). Two objective functions, namely (a) maximization of the overall collection efficiency, and (b) minimization of the pressure drop were used in this study. Three decision variables including two operating parameters, viz liquid,gas ratio and gas velocity in the throat, and the nozzle configuration, which takes into account the three-dimensional nature of the problem, were used in the optimization. Optimal design curves (non-dominated Pareto sets) and the values of the decision variables corresponding to optimum conditions on the Pareto set for a pilot-scale scrubber were obtained. The liquid to gas (L/G) ratio, which is a key decision variable that determines the uniformity of liquid distribution, and a staggered nozzle configuration can produce uniform liquid distribution in the scrubber. Multiple penetration using nozzles of two different sizes in a triangular staggered arrangement can reduce liquid loading by as much as 50%, consequently reducing the pressure drop in the scrubber. © 2003 Society of Chemical Industry [source] Dynamic optimization of the methylmethacrylate cell-cast process for plastic sheet productionAICHE JOURNAL, Issue 6 2009Martín Rivera-Toledo Abstract Traditionally, the methylmethacrylate (MMA) polymerization reaction process for plastic sheet production has been carried out using warming baths. However, it has been observed that the manufactured polymer tends to feature poor homogeneity characteristics measured in terms of properties like molecular weight distribution. Nonhomogeneous polymer properties should be avoided because they give rise to a product with undesired wide quality characteristics. To improve homogeneity properties force-circulated warm air reactors have been proposed, such reactors are normally operated under isothermal air temperature conditions. However, we demonstrate that dynamic optimal warming temperature profiles lead to a polymer sheet with better homogeneity characteristics, especially when compared against simple isothermal operating policies. In this work, the dynamic optimization of a heating and polymerization reaction process for plastic sheet production in a force-circulated warm air reactor is addressed. The optimization formulation is based on the dynamic representation of the two-directional heating and reaction process taking place within the system, and includes kinetic equations for the bulk free radical polymerization reactions of MMA. The mathematical model is cast as a time dependent partial differential equation (PDE) system, the optimal heating profile calculation turns out to be a dynamic optimization problem embedded in a distributed parameter system. A simultaneous optimization approach is selected to solve the dynamic optimization problem. Trough full discretization of all decision variables, a nonlinear programming (NLP) model is obtained and solved by using the IPOPT optimization solver. The results are presented about the dynamic optimization for two plastic sheets of different thickness and compared them against simple operating policies. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Preferential crystallization: Multi-objective optimization frameworkAICHE JOURNAL, Issue 2 2009Shrikant A. Bhat Abstract A four objective optimization framework for preferential crystallization of D-L threonine solution is presented. The objectives are maximization of average crystal size and productivity, and minimization of batch time and the coefficient of variation at the desired purity while respecting design and operating constraints. The cooling rate, enantiomeric excess of the preferred enantiomer, and the mass of seeds are used as the decision variables. The optimization problem is solved by using adaptation of the nondominated sorting genetic algorithm. The results obtained clearly distinguish different regimes of interest during preferential crystallization. The multi-objective analysis presented in this study is generic and gives a simplified picture in terms of three zones of operations obtained because of relative importance of nucleation and growth. Such analysis is of great importance in providing better insight for design and decision making, and improving the performance of the preferential crystallization that is considered as a promising future alternative to chromatographic separation of enantiomers. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] On the theory of optimal sensor placementAICHE JOURNAL, Issue 5 2002Donald J. Chmielewski On the Theory of Optimal Sensor Placement An optimal sensor placement is defined as a sensor configuration that achieves the minimum capital cost while observing prespecified performance criteria. Previous formulations of this problem have resulted in the definition of a mixed-integer nonlinear program (MINLP) with dimensions dependent on the value of the integer decision variables. The main contribution of this work is an equivalent reformulation of the design problem such that the dimension of the NLP is independent of all decision variables. Additionally, the traditional sensor-placement problem, based on static process conditions, is extended to linear dynamic processes. The final contribution is the exact conversion of the general NLP into a convex program through the use of linear matrix inequalities. The aggregation of these results show that the sensor-placement problem can be solved globally and eficiently using standard interior-point and branch-and-bound search algorithms. [source] Strategic managerial incentives under adverse selectionMANAGERIAL AND DECISION ECONOMICS, Issue 8 2005Michel Cavagnac We extend the strategic contract model where the owner designs incentive schemes for her manager before the latter takes output decisions. Firstly, we introduce private knowledge regarding costs within each owner,manager pair. Under adverse selection, we show that delegation involves a trade-off between strategic commitment and the cost of an extra informational rent linked to decentralization. Which policies will arise in equilibrium? We introduce in the game an initial stage where owners can simultaneously choose between control and delegation. We show that if decision variables are strategic substitutes, choosing output control through a quantity-lump sum transfer contract is a dominating strategy. If decision variables are strategic complements, this policy is a dominated strategy. Further, two types of dominant-strategies equilibrium may arise: in the first type, both principals use delegation; in the second one, both principals implement delegation for a low-cost manager and output control for a high-cost one. Copyright © 2005 John Wiley & Sons, Ltd. [source] When (not) to indulge in ,puffery': the role of consumer expectations and brand goodwill in determining advertised and actual product qualityMANAGERIAL AND DECISION ECONOMICS, Issue 6 2000Praveen K. Kopalle We analyze why some firms advertise product quality at a level different from the actual quality of a product. By considering the interacting effects of product quality and advertising, we develop a dynamic model of consumer expectations about product quality and the development of brand goodwill to determine the optimal values for the decision variables. The model parameters are determined based on prior literature and we use numerical techniques to arrive at the solution. We then derive conditions under which a firm will find it optimal to overstate or understate product quality. The results suggest that quality may be overstated in markets characterized by high price sensitivity, low quality sensitivity, low brand loyalty, and high source credibility, suggesting the need for vigilance on the part of consumers, upper level managers and regulatory authorities in such market conditions. This is important because current regulatory resources are insufficient to reduce deceptive advertising practices (Davis JJ. 1994. Ethics in advertising decision-making: implications for reducing the incidence of deceptive advertising. Journal of Consumer Affairs28: 380,402). Further, the law of deceptive advertising prohibits some advertising claims on the ground that they are likely to harm consumers or competitors (Preston IL, Richards JI. 1993. A role for consumer belief in FTC and Lanham Act deceptive advertising cases. American Business Law Journal31: 1,29). Also, Nagler (1993. Rather bait than switch: deceptive advertising with bounded consumer rationality. Journal of Public Economics51: 359,378) shows that deceptive advertising causes a net social welfare loss and a public policy effectively preventing deception will improve social welfare. Copyright © 2000 John Wiley & Sons, Ltd. [source] Models of sensor operations for border surveillance,NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 1 2008Roberto Szechtman Abstract This article is motivated by the diverse array of border threats, ranging from terrorists to arms dealers and human traffickers. We consider a moving sensor that patrols a certain section of a border with the objective to detect infiltrators who attempt to penetrate that section. Infiltrators arrive according to a Poisson process along the border with a specified distribution of arrival location, and disappear a random amount of time after their arrival. The measures of effectiveness are the target (infiltrator) detection rate and the time elapsed from target arrival to target detection. We study two types of sensor trajectories that have constant endpoints, are periodic, and maintain constant speed: (1) a sensor that jumps instantaneously from the endpoint back to the starting-point, and (2) a sensor that moves continuously back and forth. The controlled parameters (decision variables) are the starting and end points of the patrolled sector and the velocity of the sensor. General properties of these trajectories are investigated. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008 [source] Optimal control of a revenue management system with dynamic pricing facing linear demandOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 6 2006Fee-Seng Chou Abstract This paper considers a dynamic pricing problem over a finite horizon where demand for a product is a time-varying linear function of price. It is assumed that at the start of the horizon there is a fixed amount of the product available. The decision problem is to determine the optimal price at each time period in order to maximize the total revenue generated from the sale of the product. In order to obtain structural results we formulate the decision problem as an optimal control problem and solve it using Pontryagin's principle. For those problems which are not easily solvable when formulated as an optimal control problem, we present a simple convergent algorithm based on Pontryagin's principle that involves solving a sequence of very small quadratic programming (QP) problems. We also consider the case where the initial inventory of the product is a decision variable. We then analyse the two-product version of the problem where the linear demand functions are defined in the sense of Bertrand and we again solve the problem using Pontryagin's principle. A special case of the optimal control problem is solved by transforming it into a linear complementarity problem. For the two-product problem we again present a simple algorithm that involves solving a sequence of small QP problems and also consider the case where the initial inventory levels are decision variables. Copyright © 2006 John Wiley & Sons, Ltd. [source] Optimal sequence of landfills in solid waste managementOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 5-6 2001Francisco J. André Abstract Given that landfills are depletable and replaceable resources, the right approach, when dealing with landfill management, is that of designing an optimal sequence of landfills rather than designing every single landfill separately. In this paper, we use Optimal Control models, with mixed elements of both continuous-and discrete-time problems, to determine an optimal sequence of landfills, as regarding their capacity and lifetime. The resulting optimization problems involve splitting a time horizon of planning into several subintervals, the length of which has to be decided. In each of the subintervals some costs, the amount of which depends on the value of the decision variables, have to be borne. The obtained results may be applied to other economic problems such as private and public investments, consumption decisions on durable goods, etc. Copyright © 2001 John Wiley & Sons, Ltd. [source] Designing an accelerated degradation experiment by optimizing the estimation of the percentileQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2003Hong-Fwu Yu Abstract Degradation tests are widely used to assess the reliability of highly reliable products which are not likely to fail under traditional life tests or accelerated life tests. However, for some highly reliable products, the degradation may be very slow and hence it is impossible to have a precise assessment within a reasonable amount of testing time. In such cases, an alternative is to use higher stresses to extrapolate the product's reliability at the design stress. This is called an accelerated degradation test (ADT). In conducting an ADT, several decision variables, such s the inspection frequency, sample size and termination time, at each stress level are influential on the experimental efficiency. An inappropriate choice of these decision variables not only wastes experimental resources but also reduces the precision of the estimation of the product's reliability at the use condition. The main purpose of this paper is to deal with the problem of designing an ADT. By using the criterion of minimizing the mean-squared error of the estimated 100th percentile of the product's lifetime distribution at the use condition subject to the constraint that the total experimental cost does not exceed a predetermined budget, a nonlinear integer programming problem is built to derive the optimal combination of the sample size, inspection frequency and the termination time at each stress level. A numerical example is provided to illustrate the proposed method. Copyright © 2003 John Wiley & Sons, Ltd. [source] The Interdependent and Intertemporal Nature of Financial Decisions: An Application to Cash Flow SensitivitiesTHE JOURNAL OF FINANCE, Issue 2 2010VLADIMIR A. GATCHEV ABSTRACT We develop a dynamic multiequation model where firms make financing and investment decisions jointly subject to the constraint that sources must equal uses of cash. We argue that static models of financial decisions produce inconsistent coefficient estimates, and that models that do not acknowledge the interdependence among decision variables produce inefficient estimates and provide an incomplete and potentially misleading view of financial behavior. We use our model to examine whether firms are constrained from accessing capital markets. Unlike static single-equation studies that find firms underinvest given cash flow shortfalls, we conclude that firms maintain investment by borrowing. [source] Optimization of Fed-Batch Saccharomyces cerevisiae Fermentation Using Dynamic Flux Balance ModelsBIOTECHNOLOGY PROGRESS, Issue 5 2006Jared L. Hjersted We developed a dynamic flux balance model for fed-batch Saccharomyces cerevisiae fermentation that couples a detailed steady-state description of primary carbon metabolism with dynamic mass balances on key extracellular species. Model-based dynamic optimization is performed to determine fed-batch operating policies that maximize ethanol productivity and/or ethanol yield on glucose. The initial volume and glucose concentrations, the feed flow rate and dissolved oxygen concentration profiles, and the final batch time are treated as decision variables in the dynamic optimization problem. Optimal solutions are generated to analyze the tradeoff between maximal productivity and yield objectives. We find that for both cases the prediction of a microaerobic region is significant. The optimization results are sensitive to network model parameters for the growth associated maintenance and P/O ratio. The results of our computational study motivate continued development of dynamic flux balance models and further exploration of their application to productivity optimization in biochemical reactors. [source] | ||||||||||||||||||||||||||||