The economic impact of a transportation bottleneck: an integrated input-output and linear programming approach

A transport-constrained input-output and linear programming (IO/LP) model is proposed for the purpose of studying the impact of a transportation bottleneck in an economy. This removes the implicit assumption of no capacity constraints that characterizes the traditional demand-driven input-output model. Data for Taiwan are used for empirical experiments, and results from these experiments indicate the kinds of policy implications that might be drawn.     

Input-output linearizing control of constrained nonlinear processes

An input-output linearization strategy for constrained nonlinear processes is proposed. The system may have constraints on both the manipulated input and the controlled output. The nonlinear control system is comprised of: (i) an input-output linearizing controller that compensates for processes nonlinearities; (ii) a constraint mapping algorithm that transforms the original input constraints into constraints on the manipulated input of the feedback linearized system; (iii) a linear model predictive controller that regulates the resulting constrained linear system; and (iv) a disturbance model that ensures offset-free setpoint tracking. As a result of these features, the approach combines the computational simplicity of input output linearization and the constraint handling capability of model predictive control. Simulation results for a continuous stirred tank reactor demonstrate the superior performance of the proposed strategy as compared to conventional input-output linearizing control and model predictive control techniques.     

Entrepreneurship policy evaluation and decision analysis for SMEs

Since the second half of 2007, the global economy has felt the effects of the credit crunch, and the sub-prime mortgage crisis in the US has resulted in a loss of consumer confidence. Small and medium enterprises (SMEs) have been the foundation for economic development and have provided important contributions to job creation. Economic policy makers worldwide are trying to implement actions that will enhance the competitive vitality of SMEs and foster entrepreneurial drive through effective entrepreneurship policy. Surprisingly, prior research has given little attention to the evaluation of entrepreneurship policy. The paper proposes an integrated model that combines the Decision Making Trial and Evaluation Laboratory (DEMATEL), analytic network process (ANP), and zero-one goal programming (ZOGP) methods. This model considers the interdependent relationship among complex policy evaluation criteria and alternatives, and addresses the constraints of itemized annual budgets. Then, the paper applies the model to an empirical case study of entrepreneurship policy for SMEs in Taiwan. The evaluation results show that three alternatives (incubator center, financial assistance, and knowledge-sharing platforms) are selected under budget constraints and the model provides an effective solution to help policy makers evaluate and select feasible entrepreneurship policy mix.     

Predictive control based on neural network models with I/O feedback linearization

This paper presents an approach for the constrained non-linear predictive control problem based on the input-output feedback linearization (IOFL) of a general non-linear system modelled by a discrete-time affine neural network model. Using the resulting linear system in the formulation of the original non-linear predictive control problem enables to restate the optimization problem as the minimization of a quadratic function, which solution can be found using reliable and fast quadratic programming (QP) routines. However, the presence of a non-linear feedback linearizing controller maps the original linear input constraints onto non-linear and state dependent constraints on the controller output, which invalidates a direct application of QP routines. In order to cope with this problem and still be able to use QP routines, an approximate method is proposed which simultaneously guarantees a feasible solution without constraints violation over the complete prediction horizon within a finite number of steps, while allowing only for a small performance degradation.     

A natural language discourse model to explain linear programming models and solutions

This paper presents a natural language (i.e., English) discourse model to explain a linear programming model and, possibly, its computed solution (which need not be optimal). Drawing from earlier syntactic translations, a semantic model is presented, based on economic input-output relations commonly found in large-scale linear programs. These are then shown how to form rule-driven explanations, which comprises a fundamental component of an intelligent mathematical programming system.     

Generalized bang-bang control for feedforward constrained regulation

In the behavioral framework for continuous-time linear scalar systems, simple sufficient conditions for the solution of the minimum-time rest-to-rest feedforward constrained control problem are provided. The investigation of the time-optimal input-output pair reveals that the input or the output saturates on the assigned constraints at all times except for a set of zero measure. The resulting optimal input is composed of sequences of bang-bang functions and linear combinations of the modes associated to the zero dynamics. This signal behavior constitutes a generalized bang-bang control that can be fruitfully exploited for feedforward constrained regulation. Using discretization, an arbitrarily good approximation of the optimal generalized bang-bang control is found by solving a sequence of linear programming problems. Numerical examples are included.     

Efficiency in public schools: a case study in non-parametric measurement

This study illustrates the problem of efficiency measurement in stochastic input-output systems when market price data are unavailable. Efficiency comparisons by non-parametric methods are attempted for selected public elementary schools in California for 1976-77 and 1977-78. A suitable linear programming model is developed for the efficiency frontier in the input-output space, which is found to be significantly different from an average relation. This is shown to have important policy implications for improving input performance for better efficiency.     

Matching swimmers to events in a championship swimming meet

This paper presents a zero-one mathematical programming model for determining which swimmers to assign to different events in a championship swimming meet. Since the model includes nonlinear constraints, it is demonstrated how some ad hoc linear constraints may be created as surrogates for the nonlinear constraints so that a zero-one linear programming code may be used to solve the problem.     

Structural identification and software package for linear multivariable systems

This paper is concerned with the structural identification of linear multivariable systems and an interactive identification package. The structural identification is done by taking the time-invariant subsystem from the realizations of the input-output relations identified using data of disjoint time intervals, and the statistical hypothesis test is employed to determine the order, where the input-output relation is identified based on the generalized least squares method using the possibly larger model for the plant. The identification package is for the identification of the input-output relation of a linear multivariable system, for the structural identification based on the realization and for data management.     

A non-linear controller design method for processes with saturating actuators

An optimization approach is proposed to derive non-linear model-based control laws for non-linear processes with actuator saturation non-linearities. The derived control laws induce a linear closed-loop process output response in the absence of input constraints (are input-output linearizing), are able to minimize the mismatch between the constrained and the linear unconstrained process output responses, and inherently include optimal directionality and windup compensators. Connections between the derived control laws and (a) already available, input-output linearizing, non-linear, control methods, (b) modified internal model control, and (c) model state feedback control, are established. The application and performance of the derived control laws are shown by examples.     

A time-domain approach to model validation

In this paper we offer a novel approach to control-oriented model validation problems. The problem is to decide whether a postulated nominal model with bounded uncertainty is consistent with measured input-output data. Our approach directly uses time-domain input-output data to validate uncertainty models. The algorithms we develop are computationally tractable and reduce to (generally nondifferentiable) convex feasibility problems or to linear programming problems. In special cases, we give analytical solutions to these problems     

Fuzzy data envelopment analysis: A discrete approach

Data envelopment analysis (DEA) as introduced by Charnes, Cooper, and Rhodes (1978) is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on α-cut. One drawback of the α-cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the α-cut approach. We introduce the concept of “local α-level” to develop a multi-objective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.     

A lambda-calculus model of programming languages—I. Simple constructs

A simple correspondence is presented between the elementary constructs of programming languages and the lambda-calculus. The correspondence is obtained by using intuitive, functional interpretations of programming language constructs, completely avoiding the notions of machine memory and address. In particular, the concepts of program variable and assignment are accounted for in terms of the concepts of mathematical variable and substitution, respectively. Lambda-expression representations are given of assignments, conditional and compound statements, input-output, and blocks. Algol 60 is used as the illustrative language.     

A class of linear interval programming problems and its application to portfolio selection

This paper discusses a class of linear programming problems with interval coefficients in both the objective functions and constraints. The noninferior solutions to such problems are defined based on two order relations between intervals, and can be found by solving a parametric linear programming problem. Considering the uncertain returns of assets in capital markets as intervals, we propose a model for portfolio selection based on the semiabsolute deviation measure of risk, which can be transformed to a linear interval programming model studied in the paper. The method is illustrated by solving a simplified portfolio selection problem.     

An interactive fuzzy satisficing method based on variance minimization under expectation constraints for multiobjective stochastic linear programming problems

In this paper, we focus on multiobjective linear programming problems involving random variable coefficients in objective functions and constraints. Using the concept of chance constrained conditions, such multiobjective stochastic linear programming problems are transformed into deterministic ones based on the variance minimization model under expectation constraints. After introducing fuzzy goals to reflect the ambiguity of the decision maker??s judgements for objective functions, we propose an interactive fuzzy satisficing method to derive a satisficing solution for them as a fusion of the stochastic programming and the fuzzy one. The application of the proposed method to an illustrative numerical example shows its usefulness.     

Simultaneous machine-part grouping approach in manufacturing cells

We present a comprehensive model including all of the operational constraints for solving a cell formation problem in cellular manufacturing systems. It is formulated as a generalized quadratic binary programming model with the objective of minimizing total moves evaluated as a weighted sum of inter- and intracell moves. Two most significant operational constraints included in the model are the sequence of operations associated with each part and the capability of assigning machines of the same type to different cells if two or more machines are considered due to workload requirements. The original model is Transformed into a linear binary programming model, and an example problem is solved using a commercial programming package. The final assignment of parts and machines to cells result in a lower total move than that evaluated from a previous study for the same problem.     

INTERACTIVE FUZZY PROGRAMMING BASED ON FRACTILE CRITERION OPTIMIZATION MODEL FOR TWO-LEVEL STOCHASTIC LINEAR PROGRAMMING PROBLEMS

In this article, we focus on two-level linear programming problems involving random variable coefficients in objective functions and constraints. Following the concept of chance constrained programming, the two-level stochastic linear programming problems are transformed into deterministic ones based on the fractile criterion optimization model. After introducing fuzzy goals for objective functions, interactive fuzzy programming to derive a satisfactory solution for decision makers is presented as a fusion of a stochastic approach and a fuzzy one. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.     

Macroeconomic Effects of Sectoral Shocks in Germany, The U.K. and, The U.S. A VAR-GARCH-M Approach

A VAR-GARCH-M model for aggregate employment and employment shares isdeveloped to explore the macroeconomic effects of sectoral shocks. Using U.S.,U.K. and German data, three main issues are investigated: the relevance ofshocks volatility; the amount of aggregate employment growth variationaccounted for by re-allocation shocks and the amount of aggregate innovation volatility explained by sectoral components. Bayesian methods are used for estimation model selection and innovation accounting – Bayes factors for model selection and MCMC for estimation. The results favor the VAR-GARCH-M model. A significant GARCH-M component indicates the presence of volatility clustering and the feedback of volatilities on aggregate employment and sectoral shares growth rates. The innovation analysis supports sectoral shocks as a triggering force for aggregate employment fluctuations. In all three countries, 45% to 55% ofaggregate employment variation is accounted for by sectoral innovations.     

以低碳为目标的集装箱拖车运输问题及其时间窗离散化算法

研究以低碳为目标的集装箱拖车运输问题. 该问题需同时调度隐含的运输资源和具有双重时间窗限制的运输任务. 基于扩展的确定的活动在顶点上(DAOV) 的图建立该问题的具有双时间窗约束的混合整数非线性规划模型,设计一个基于时间窗离散化的求解算法, 并将该模型转化为纯整数线性规划模型. 实验结果表明, 所提出的方法有很好的求解速度和精度, 与给定车辆行驶速度情形的对比进一步验证了所提出模型的有效性.

     

Design of general projection neural networks for solving monotone linear variational inequalities and linear and quadratic optimization problems.

Most existing neural networks for solving linear variational inequalities (LVIs) with the mapping Mx + p require positive definiteness (or positive semidefiniteness) of M. In this correspondence, it is revealed that this condition is sufficient but not necessary for an LVI being strictly monotone (or monotone) on its constrained set where equality constraints are present. Then, it is proposed to reformulate monotone LVIs with equality constraints into LVIs with inequality constraints only, which are then possible to be solved by using some existing neural networks. General projection neural networks are designed in this correspondence for solving the transformed LVIs. Compared with existing neural networks, the designed neural networks feature lower model complexity. Moreover, the neural networks are guaranteed to be globally convergent to solutions of the LVI under the condition that the linear mapping Mx + p is monotone on the constrained set. Because quadratic and linear programming problems are special cases of LVI in terms of solutions, the designed neural networks can solve them efficiently as well. In addition, it is discovered that the designed neural network in a specific case turns out to be the primal-dual network for solving quadratic or linear programming problems. The effectiveness of the neural networks is illustrated by several numerical examples.