Operational Analysis Approach Research Articles

TRAINING NEEDS ANALYSIS OF OPERATIONAL

A good organization is supported by good management and employees who have adequate knowledge and skills in carrying out functions and games. The organization provides a management system that makes it easy for members of the organization to run the system so that their performance is easily controlled. Employees with adequate quality and competence can bring the organization in a more advanced direction. They continue and have a desire to grow and develop into individuals needed by the organization in achieving its goals. The development of technology and industry today has resulted in many adjustments and system changes in management. In the new normal era where all work activities have been directed to be online-based, many organizations conduct a training needs analysis to obtain information on what training is suitable to be held in improving the performance of the organization and its employees. This study describes the theory of training needs analysis for organizations through operational analysis. And this study aims to obtain information about what is needed in analyzing training needs for organizations through operational analysis. The research method used is a qualitative method using a literature review approach. The results of this study were selected as a training needs analysis tool using the operational analysis approach of the Irwin Goldstein method which sets 4 indicators, namely: knowledge, skills, abilities, and other characteristics or often abbreviated as KSAOs

Proof-of-concept for methodological framework to evaluate operational strategies with travel demand models

Recently, limited available resources for physical capacity expansion have generated supports for short-term operational improvements. Yet, only a few studies have dealt with evaluating these operational strategies effectively within the traditional transportation planning process even though suitable operational strategies impact to not only specific corridors or regions but also the whole transportation network. This is because it is generally perceived that integrating travel demand models with operational analysis approaches is quite difficult due to different constraints, modeling structures, and required data sets. In this regard, the concept of methodological framework to evaluate operational strategies with travel demand models is developed and validated by the proper case study (i.e., high occupancy toll lanes deployment in the Hampton Roads area in Virginia, US) in this research. The proposed framework consists of three major components: (i) the selection of an appropriate operational analysis approach, (ii) the disaggregation of daily traffic volumes to peak period volumes, and (iii) the alignment of modeling elements between the travel demand model and operational tool. Key contributions from this research are that (i) the proposed methodology enables the evaluation of travel behavioral changes without microscopic simulation, especially in terms of capturing network flow pattern changes caused by behavioral shifts after operational strategy deployment, (ii) the proposed framework eliminates assumptions required when only operational tools are used to evaluate operational strategies, (iii) the disaggregation method of a daily trip distribution matrix into peak period matrices by using survey data are developed, (iv) specific details influencing integration in terms of data types, peak period link capacity, volume-delay functions, and link impedance are identified. Consequently, even though this research still has some limitations (e.g., inherent weakness of travel demand models), this can be a starting point to develop more detailed guidelines as well as a good reference for practitioners and researchers who wish to evaluate operation strategies within transportation planning process.

Operational Analysis Revisited: Error Measure Limits of Assumptions

AbstractThe assumptions used to develop operational analysis computer performance measures, such as number of jobs at a device or response times, are stated in terms of the data itself, rather than the underlying system which produces the data. In spite of claims of validity and as an aid in introducing queueing theory in teaching, little has been written about operational analysis in the past ten years. Accuracy of operational analysis performance measures depend on data behavior assumptions which can be validated with data based error measures. Increased soundness of the operational analysis approach may be obtained by determining the limits of assumption errors as the time period of observation increases. Part I of this paper is a review of operational analysis and addresses some of the previous concerns with its approach. Part II develops further understanding of operational analysis assumption errors by examining their limits. Limits are found for the assumption errors of job flow balance, homogeneous arrivals and homogenous services. While the job flow balance assumption error measure is shown to approach zero over time, the homogeneity assumption error measures, in general, do not.

Plantwide operability assessment for nonlinear processes using a microscopic level network analysis

The increase of raw material and energy costs has caused a shift in process design philosophy leading to more complex chemical plants utilising heat integration and material recycles. This warrants plantwide dynamic operability analysis in the process design stage. In our previous work, a networked plantwide operability analysis approach was developed, where the plantwide process is viewed as a network of process units connected via mass and energy flow. Such an analysis is based on the dissipativity of each process unit and the topology of the process network. However, to determine the dissipativity of multivariable nonlinear process units is often extremely difficult. In this work, we take the network approach to a microscopic level and treat each nonlinear multivariable process unit as a network of individual (single state) mass and energy balances (sub-systems). The plantwide process is then viewed as a network of such sub-systems rather than physical process units. The dissipativity of these simple sub-systems can often be determined more easily in comparison to that of multivariable sub-systems. The dissipativity property (in terms of supply rate) of the entire nonlinear process can be parametrised by the dissipativity of individual sub-systems, leading to a cluster of supply rates. The operability of the plantwide nonlinear process can then be determined based on the above parametrised dissipativity which can be much less conservative than existing nonlinear analysis. The effects of interactions caused by the interconnections are considered explicitly based on the network topology. The stability and stabilisability analysis problem is then converted into a feasibility problem with linear matrix inequalities which can be solved numerically. The application of the proposed approach requires successful determination of the dissipativity of nonlinear sub-systems.

Analysis of Interaction Effects on Plantwide Operability

Modern chemical plants are complex and consist of many process units interconnected in various configurations (e.g., recycle, bypass, and heat integration). Because of these interconnections, the unit interactions often impose limitations on the plantwide operability of chemical processes, especially on the performance of decentralized controller. In this article, a plantwide operability analysis approach that explicitly considers the interactions based on network perspective is presented. It provides a uniform framework for the assessment of plantwide stability, stabilizability, and achievable dynamic performance in the case of regulatory control using the concept of dissipative systems. To focus on the effects of process dynamics and the interaction between subsystems on the plantwide operability, this analysis was developed with the assumption of state-feedback control. The above analysis problem involves linear matrix inequalities (LMIs), which are convex and easy to solve.

Operational Analysis in Support of Armed Forces Transformation: The Role of Organizational Culture

This article starts with presenting a very comprehensive review of the process of armed forces transformation. The proposed framework offers a clear vi- sion for the main directions and parameters of armed forces transformation. It pro- vides opportunities to include lessons learned and to develop strategies for dealing with the military reform. In order to accomplish optimal reforms, these strategies have to be studied carefully and after that, based on the result, planned and imple- mented. Certainly, all capacity of the nation must be harnessed to that process, in- cluding the operational analysis approach. In performing operational analysis, the author considers a very important aspect - the role of organizational culture. The study performed by the author helps him conclude that organizational culture change has to go first and ―tow‖ operational analysis and transformation processes ahead. Therefore, the role of organizational culture can be defined as advanced, leading and determining.

A dynamic operability analysis approach for nonlinear processes

Current process operability indicators are mostly restricted to linear approximations of the process dynamics. Other operability analysis approaches that have the capability to include full nonlinear process models rely on mixed integer dynamic optimisation techniques which, in general, require large amount of computations. In this paper we propose a dynamic operability analysis approach for stable nonlinear processes that can be readily applied during process design and can be solved efficiently using a limited amount of computations. The process nonlinear dynamics are approximated by a series interconnection of static nonlinearities and linear dynamics, represented by the so-called Hammerstein–Wiener models. These type of models can often be obtained during process design where detailed steady-state nonlinear models are available, combined with some (usually limited) information on the process dynamics. Using an extended internal model control (IMC) framework, we investigate the interaction between the static nonlinearities and linear dynamics on the operability of the process. The framework extends the well-known equivalence between operability and invertibility of linear processes to nonlinear systems. In particular, by exploiting some results from the theory of passive systems we provide conditions that guarantee the existence of the inverse of the static nonlinearities. We show that the inverse can be attained inside a specific input/output region. This region imposes a constraint on the maximum magnitude of the signals that appear in the closed-loop and represents the effect of the static nonlinearities on the operability of the overall process. Dynamic operability is then quantified using a linear matrix inequality (LMI) optimisation approach that minimises a given performance criterion subject to the constraint imposed by the static nonlinearities.

Saturation Estimation In Interactive Computer Systems

AbstractThis paper presents a systematic method for estimating the saturation point of interactive computer systems in an environment of incomplete information (in particular, where per interaction information is unavailable). This method is an improvement over the ad hoc, highly subjective, and time-consuming iterative approach commonly used. Following an operational analysis approach and using only commonly available data, a simple model was constructed to estimate the mean response time of a task characterized by its ci’u and I/O requests running in a given level of system load. The system work load is characterized by the CPU, disk, drum, and channel load factors. The system is periodically measured (or the system log examined) to obtain the parameter values needed to estimate the mean response time ofthe task as well as the corresponding work load level. With sufficient samples, a mean response time vs. system load curve can be plotted. The system saturation load is taken to be that corresponding to ...

Computer system models

A system model is a tool used to predict system performance under changing conditions. There are two widely used modeling techniques: one based on discrete event simulation and one based on queuing theory models. Because queueing theory models are so much cheaper to implement and use, as compared to simulation models, there is growing interest in them. Users are developing and using queuing theory models to project system performance, project capacity, analyze bottlenecks and configure systems. This talk uses an operational analysis approach to develop system models. This approach, as presented in Denning and Buzen [1], provides an intuitive basis for analyzing system performance and constructing system models. Very simple calculations lead to estimates of bounds on performance - maximum job throughput rates and minimum message response times. The emphasis is on gaining an understanding of system models which reenforces intuition, not on mathematical formulae. Several examples are included. References to other works and publications are provided. Application areas and limitations of modeling techniques are discussed.