Publikační činnost Katedry systémového inženýrství / Publications of Department of System Engineering (157)
http://hdl.handle.net/10084/100633
Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry systémového inženýrství (157) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.2018-04-21T11:58:18ZA simplification generalized returns to scale approach for selecting performance measures in data envelopment analysis
http://hdl.handle.net/10084/126099
A simplification generalized returns to scale approach for selecting performance measures in data envelopment analysis
Toloo, Mehdi; Allahyar, Maryam
Toloo and Tichy (2015) with the aim of holding the rule of thumb in data envelopment analysis, developed a pair of models which optimally chooses some inputs and outputs among selective measures under variable returns to scale assumption. Their approach involves a lower bound for the input and output weights in the multiplier model and a penalty term in the objective function of envelopment model. These models possess an epsilon which on the one hand turns the selecting envelopment model non-linear and on the other hand increases the required computational burden for solving the selecting multiplier models. Selecting an improper value for the epsilon may cause the infeasibility and unboundedness issues for the multiplier and envelopment model, respectively. This paper demonstrates that the method of Toloo and Tichy (2015) is valid even with excluding the epsilon. The method is extended to generalized returns to scale model which considers other returns to scale assumptions, i.e. non-increasing, constant, and non-decreasing. The obtained results point out that the simplified approach is more stable and more reliable and substantially reduces the required calculations.
2018-01-01T00:00:00ZCosts of quality or quality costs
http://hdl.handle.net/10084/125822
Costs of quality or quality costs
Řeháček, Petr
Costs of quality or quality costs do not mean the use of expensive or very highly quality materials to manufacture a product. The term refers to the costs that are incurred to prevent, detect and remove defects from products. There are four categories: internal failure costs (costs associated with defects found before the customer receives the product or service), external failure costs (costs associated with defects found after the customer receives the product or service), appraisal costs (costs incurred to determine the degree of conformance to quality requirements) and prevention costs (costs incurred to keep failure and appraisal costs to a minimum). Cost of quality is a methodology that allows an organization to determine the extent to which its resources are used for activities that prevent poor quality, that appraise the quality of the organization's products or services, and that result from internal and external failures. Having such information allows an organization to determine the potential savings to be gained by implementing process improvements.
2018-01-01T00:00:00ZIntegrated data envelopment analysis: Linear vs. nonlinear model
http://hdl.handle.net/10084/125753
Integrated data envelopment analysis: Linear vs. nonlinear model
Mahdiloo, Mahdi; Toloo, Mehdi; Thach-Thao Duong; Saen, Reza Farzipoor; Tatham, Peter
This paper develops a relationship between two linear and nonlinear data envelopment analysis (DEA) models which have previously been developed for the joint measurement of the efficiency and effectiveness of decision making units (DMUs). It will be shown that a DMU is overall efficient by the nonlinear model if and only if it is overall efficient by the linear model. We will compare these two models and demonstrate that the linear model is an efficient alternative algorithm for the nonlinear model. We will also show that the linear model is more computationally efficient than the nonlinear model , it does not have the potential estimation error of the heuristic search procedure used in the nonlinear model and it determines global optimum solutions rather than the local optimum. Using 11 different data sets from published papers and also 1000 simulated sets of data we will explore and compare these two models. Using the data set that is most frequently used in the published papers it is shown that the nonlinear mosel with a step size equal to 0.00001 reauires running 1,955,573 linear problems (LPs) to measure the efficiency of 24 DMUs compared to only 24 LPs required for the linear model. Similarly for a very small data set which consists of only 5 DMUs the nonlinear model requires running 7861 LPs with step size equal to 0.0001 whereas the linear model needs just 5 LPs.
2018-01-01T00:00:00ZRisk management standards for P5M
http://hdl.handle.net/10084/125684
Risk management standards for P5M
Řeháček, Petr
Risk can be managed, minimized, shared, transferred or accepted but it cannot be ignored. An effective and efficient risk management approach requires a proper and systematic methodology and, more importantly, knowledge and experience. Risk management are coordinated activities to direct and control an organization with regard to risk. Based on this definition, project risk management can be derivatively defined as coordinate activities to direct and control a project with regard to risk. In this way, it becomes an integral part of every aspect of managing the project. The goal of this paper is to present and compare the main standards for project risk management that are currently available today. Five international standards recognized world-wide were selected for comparison PMI, PRINCE2, IPMA, ISO 31000 and IEC 62198.
2018-01-01T00:00:00Z