A Study on the Relationship Between School Members’ Intellectual Capital, Organizational Learning, Leadership Behavior, and School Performance: A Structural Equation Modeling Approach

The purpose of this study was to identify the relationship structure among school members’ intellectual capital, organizational learning, principals’ leadership behavior, and school performance, taking 288 teachers from elementary and high schools in Taiwan as a research sample. The validated questionnaires were employed to survey school teachers’ perspectives on these four constructs. All participants completed 53 items of validated instruments including Organizational Learning Inventory (OLI), Intellectual Capital Inventory (ICI), Leadership Behavior Inventory (LBI), and School Performance Inventory (SPI). The construct as well as the significant relationship between variables examined using SPSS 21 and Amos software package to conduct structural equation modeling (SEM). The result of a confirmatory factor analysis confirmed several fixed factors of the variables. The second findings of the study indicated that there was a significant and positive correlation among organizational learning, intellectual capital, principals’ leadership behavior, and school performance. In the light of the findings, this paper discusses the importance of organizational learning and principals’ leadership behavior in order to improve school performance. Implications, suggestions, and recommendations for teachers, policy makers, and educational stakeholders were discussed.

Improving the Efficiency of Physical Examination Services

The objective of our project was to improve the efficiency of the physical examination screening service of a large hospital system. We began with a detailed simulation model to explore the relationships between four performance measures and three decision factors. We then attempted to identify the optimal physician inquiry starting time by solving a goal-programming problem, where the objective function includes multiple goals. One of our simulation results shows that the proposed optimal physician inquiry starting time decreased patient wait times by 50% without increasing overall physician utilization

An Insight into the Data Structure of the Dynamic Batch Means Algorithm with Binary Tree Code

Batching is a well-known method used to estimate the variance of the sample mean in steady-state simulation. Dynamic batching is a novel technique employed to implement traditional batch means estimators without the knowledge of the simulation run length a priori. In this study, we reinvestigated the dynamic batch means (DBM) algorithm with binary tree hierarchy and further proposed a binary coding idea to construct the corresponding data structure. We also present a closed-form expression for the DBM estimator with binary tree coding idea. This closed-form expression implies a mathematical expression that clearly defines itself in an algebraic binary relation. Given that the sample size and storage space are known in advance, we can show that the computation complexity in the closed-form expression for obtaining the indexes c j ( k ) , i.e., the batch mean shifts s , is less than the effort in recursive expression

Three pseudo-utility ratio-inspired particle swarm optimization with local search for multidimensional knapsack problem

In this study, a three-ratio self-adaptive check and repair operator-inspired particle swarm optimization (3R-SACRO-PSO) with neighborhood local search is developed to solve the multidimensional knapsack problem (MKP). The proposed 3R-SACRO-PSO systematically alters substitute pseudo-utility ratios as the PSO method is executed. In addition, a local search scheme is introduced to improve solution quality. The proposed 3R-SACRO-PSO algorithm is tested using 168 different widely used benchmarks from the OR-Library to demonstrate and validate its performance. The control parameters for the performance test are determined through the Taguchi method. Experimental results parallel those of other PSO algorithms, and statistical test results show that the quality and efficiency of the proposed 3R-SACRO are better than those of the two-ratio SACRO method. Moreover, the proposed 3R-SACRO-PSO is on par with state-of-the-art PSO approaches. Thus, introducing the third pseudo-utility ratio into SACRO improves the performance of SACRO-based PSO. The neighborhood local search scheme further improves the solution quality in handling MKPs

An Insight into the Data Structure of the Dynamic Batch Means Algorithm with Binary Tree Code

Batching is a well-known method used to estimate the variance of the sample mean in steady-state simulation. Dynamic batching is a novel technique employed to implement traditional batch means estimators without the knowledge of the simulation run length a priori. In this study, we reinvestigated the dynamic batch means (DBM) algorithm with binary tree hierarchy and further proposed a binary coding idea to construct the corresponding data structure. We also present a closed-form expression for the DBM estimator with binary tree coding idea. This closed-form expression implies a mathematical expression that clearly defines itself in an algebraic binary relation. Given that the sample size and storage space are known in advance, we can show that the computation complexity in the closed-form expression for obtaining the indexes () , i.e., the batch mean shifts , is less than the effort in recursive expression

Extended dynamic partial-overlapping batch means estimators for steady-state simulations

Estimating the variance of the sample mean from a stochastic process is essential in assessing the quality of using the sample mean to estimate the population mean, which is the fundamental question in simulation experiments. Most existing studies for estimating the variance of the sample mean from simulation output assume that the simulation run length is known in advance. An interesting and open question is how to estimate the variance of the sample mean with limited memory space, reasonable computation time, and good statistical properties such as small mean-squared-error (mse), without knowing the simulation run length a priori. This paper proposes a finite-memory algorithm that satisfies the above good estimation criteria. Our findings show that the proposed algorithm improves over its competitors in terms of the mse criterion.Simulation Variance of the sample mean Mean-squared-error