Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. ...Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.展开更多
This paper presents the formulation and practical implementation of positioning methodologies that compensate for the nonholonomic constraints of a mobile microrobot that is driven by two vibrating direct current(DC) ...This paper presents the formulation and practical implementation of positioning methodologies that compensate for the nonholonomic constraints of a mobile microrobot that is driven by two vibrating direct current(DC) micromotors. The open-loop and closed-loop approaches described here add the capability for net sidewise displacements of the microrobotic platform. A displacement is achieved by the execution of a number of repeating steps that depend on the desired displacement, the speed of the micromotors, and the elapsed time. Simulation and experimental results verified the performance of the proposed methodologies.展开更多
A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent ...A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.展开更多
A vector autoregressive model was developed for a sample of container carrier time charter rates. Although the series of time charter rates are themselves found non-stationary, thus precluding the use of many modeling...A vector autoregressive model was developed for a sample of container carrier time charter rates. Although the series of time charter rates are themselves found non-stationary, thus precluding the use of many modeling methodologies, evidence provided by co-integration tests points to the existence of stable long-term relationships between the series. An assessment of the forecasts derived from the model suggests that the spec-ification of these long-term relationships does not improve the accuracy of long-term forecasts. These results are interpreted as a corroboration of the efficient market hypothesis.展开更多
The assumption of static and deterministic conditions is common in the practice of construction project planning. However, at the construction phase, projects are subject to uncertainty. This may lead to serious sched...The assumption of static and deterministic conditions is common in the practice of construction project planning. However, at the construction phase, projects are subject to uncertainty. This may lead to serious schedule disruptions and, as a consequence, serious revisions oft.he schedule baseline. The aim of the paper is developing a method for constructing robust project schedules with a proactive procedure. Robust project scheduling allows for constructing stable schedules with time buffers introduced to cope with multiple disruptions during project execution. The method proposed by the authors, based on Monte Carlo simulation technique and mathematical programming for buffer sizing optimization, was applied to scheduling an example project. The results were compared, in terms of schedule stability, to those of the float factor heuristic procedttre.展开更多
Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribut...Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.展开更多
基金Supported by the National 863 Project (No. 2003AA412010) and the National 973 Program of China (No. 2002CB312201)
文摘Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.
基金supported in part by the National Science Foundation(IIS1318638 and IIS1426752)the Shenzhen Science and Technology Project(ZDSY20120617113312191)
文摘This paper presents the formulation and practical implementation of positioning methodologies that compensate for the nonholonomic constraints of a mobile microrobot that is driven by two vibrating direct current(DC) micromotors. The open-loop and closed-loop approaches described here add the capability for net sidewise displacements of the microrobotic platform. A displacement is achieved by the execution of a number of repeating steps that depend on the desired displacement, the speed of the micromotors, and the elapsed time. Simulation and experimental results verified the performance of the proposed methodologies.
文摘A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.
文摘A vector autoregressive model was developed for a sample of container carrier time charter rates. Although the series of time charter rates are themselves found non-stationary, thus precluding the use of many modeling methodologies, evidence provided by co-integration tests points to the existence of stable long-term relationships between the series. An assessment of the forecasts derived from the model suggests that the spec-ification of these long-term relationships does not improve the accuracy of long-term forecasts. These results are interpreted as a corroboration of the efficient market hypothesis.
文摘The assumption of static and deterministic conditions is common in the practice of construction project planning. However, at the construction phase, projects are subject to uncertainty. This may lead to serious schedule disruptions and, as a consequence, serious revisions oft.he schedule baseline. The aim of the paper is developing a method for constructing robust project schedules with a proactive procedure. Robust project scheduling allows for constructing stable schedules with time buffers introduced to cope with multiple disruptions during project execution. The method proposed by the authors, based on Monte Carlo simulation technique and mathematical programming for buffer sizing optimization, was applied to scheduling an example project. The results were compared, in terms of schedule stability, to those of the float factor heuristic procedttre.
基金Project supported by the National Natural Science Foundation of China(Nos.61473259,61502335,61070074,and60703038)the Zhejiang Provincial Natural Science Foundation(No.Y14F020118)the PEIYANG Young Scholars Program of Tianjin University,China(No.2016XRX-0001)
文摘Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.
