16 research outputs found
Dynamic systems in the supply of pellets and distribution of the pellet production process
System archetypes are models of behavior of a system, understood as generic structures or as an
overview of typical systems. There are recognized structures that show repetition in many different
situations. Archetypes are depicted as appearances of common combinations through amplifying and
balancing feedback loops. They are constantly used to facilitate a quick understanding of the system and
their knowledge and already learned features, their insight and insight. As analytical features, they help
people change their thinking for a much larger systemic perspective to understand a phenomenon or
dynamic, and in some situations when real corrective action is not taken
Modelling with Structural Equation Modelling β Application and Issues
Structural equation modeling (SEM) is a comprehensive statistical modeling tool for analyzing multivariate data involving complex relationships between and among variables. SEM surpasses traditional regression models by including multiple independent and dependent variables to test associated hypothesizes about relationships among observed and latent variables. SEM explain why results occur while reducing misleading results by submitting all variables in the model to measurement error or uncontrolled variation of the measured variables. SEM provides a way to test the specified set of relationships among observed and latent variables as a whole, and allow theory testing even when experiments are not possible. Structural Equation Modeling (SEM) is a powerful collection of multivariate analysis techniques, which specifies the relationships between variables through the use of two main sets of equations: Measurement equations and structural equations. Measurement equations test the accuracy of proposed measurements by assessing relationships between latent variables and their respective indicators. The structural equations drive the assessment of the hypothesized relationships between the latent variables, which allow testing the statistical hypotheses for the study. Additionally, SEM considers the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, and multiple latent independents each measured by multiple indicators.
In this paper will be presented application of relationship between reverse logistics and circular economy using some SEM fit indexes. The process of validating the measurement model requires testing each cluster of observed variables separately to fit the hypothesized CFA model. The statistical test uses the most popular procedures of evaluating the measurement model: Chi-square CMIN (Ο2), Goodness-of-Fit Index (GFI), and Percent Variance Explained
Vehicle routing and scheduling β The traveling salesman problem
The classification of routing and scheduling problems depends on certain characteristics of the
service delivery system, such as size of the delivery fleet, where the fleet is housed, capacities of the vehicles, and
routing and scheduling objectives. In the simplest case, we begin with a set of nodes to be visited by a single vehicle.
The nodes may be visited in any order, there are no precedence relationships, the travel costs between two nodes are
the same regardless of the direction traveled, and there are no delivery-time restrictions. In addition, vehicle capacity
is not considered. The output for the single-vehicle problem is a route or a tour where each node is visited only once
and the route begins and ends at the depot node. The tour is formed with the goal of minimizing the total tour cost.
This simplest case is referred to as a traveling salesman problem (TSP). An extension of the traveling salesman
problem, referred to as the multiple traveling salesman problems (MTSP), occurs when a fleet of vehicles must be
routed from a single depot. The goal is to generate a set of routes, one for each vehicle in the fleet. The characteristics
of this problem are that a node may be assigned to only one vehicle, but a vehicle will have more than one node assigned to it. There are no restrictions on the size of the load or number of passengers a vehicle may carry. The solution to this problem will give the order in which each vehicle is to visit its assigned nodes. As in the single-vehicle
case, the objective is to develop the set of minimum-cost routes, where βcostβ may be represented by a dollar amount,
distance, or travel time. If we now restrict the capacity of the multiple vehicles and couple with it the possibility of
having varying demands at each node, the problem is classified as a vehicle routing problem (VRP). In this paper
will be presenteds the TSP procedure for delivery and routing of new product L-carnitine from Koding β Skopje
which life development is in the introduction or development phase
Decision Making Using Sequential Equation Modelling Applied for Pellet Production
By means of learning experiences, students are expected to know, understand, and be able to demonstrate certain skills, behaviors, and attitudes. These learning experiences have been defined and described by several different learning theories. The 21th century the most common learning theories have been behavioral and cognitive learning theories. Behavioral learning theorists explain learning as relatively permanent change in βhierarchical, observable, and measurable behaviorsβ whereas cognitive learning theorists explain learning βas an internal change in mental associationsβ. The pellet production (PP) has the potential to improve the social, economic and environmental elements of the local community, as well as to expand the development of state economy growth. Work study examines community support for that development in the context of sustainable development. This topic is interesting for processing because it deals with specific and so far in Macedonia untreated problems and aspects arising from the relationship between the local community and the state efforts for better conditions for development and higher standard
A methodology for closing the gap between the competences of students and recent graduates and labour market needs. The case of the Republic of North Macedonia
The youth unemployment is one of the most pressing problems for every economy. For addressing
this issue in the Republic of North Macedonia, numerous project initiatives and activities are in the
phase of planning and implementation. Part of these is the implemented project: βBuilding capacities
for better employabilityβ. Through the project activities, it was proposed to be institutionalized the
stakeholder cooperation for matching education curricula according to labour market needs. In order
to increase the employability of students and graduates, the main aim which is also the goal of this
study, was developing a methodology for closing the gap between the skills of students and graduates
and the Labour market needs, by enhancing the entrepreneurial education dominance in high
education curricula. Through advanced understanding of the entrepreneurial mind-set, new
opportunities in teaching and learning can enhance the University provision. For that purpose, case
studies for the best UK practices in employability were developed and a survey for investigating the
needs and requirements of the Macedonian high-educational sector was conducted.
The research findings comprised rich informative set of recommendations that was a base for
developing the methodology for closing the skills gap. The methodology was organised in four main
levels with a number of institutions (actors) and activities (measures) related to them. The application
of this methodology resulted with an outline of a stakeholder plan that offers insights into other areas
of study and research possibilities.
The contribution of this study is twofold. It adds on the literature for high-sector education and
employability, but also it has practical implications for all stakeholders responsible in coping with the
unemployment issue. The proposed methodology assists in monitoring the labour market changes and
addressing them with improvements in the university curricula accordingly. It could be a powerful tool
in the hands of the stakeholders for better employability of the students/graduates and can facilitate
whole process. Overall, it will support the Republic of North Macedoniaβs future strategies at
Government, University and Faculty level in their strides toward creating more skilful and employable
youth
Innovativeness in Macedonian Companies: Evidence from the Community Innovation Survey
The importance of innovations for development of knowledgeβbased economies is widely acknowledged. However, certain challenges for researching the innovativeness in postβsocialist economies still exist. We analyse the most influential drivers for innovativeness in Macedonian enterprises. Based on the extended literature review and the firmβlevel dataset collected by the CIS 2012 (Community Innovation Survey 2012), the conceptual model for identifying the factors that drive innovation was developed and tested with standard multiple regression. The findings confirm that firm innovativeness could be improved by extending the number of collaborators and sources for information and knowledge. Also, further investments in research and development for innovation positively impact the variety of innovation activities in companies. In addition to the theoretical and practical implications, this study is significant because the proposed method could be adjusted and applied in many countries where CIS research is conducted
Generations of innovation models and their characteristics β towards creating a new innovation model
Innovation is a process that consists of phases and activities and requires resources and knowledge. Innovation models define the innovation process. Innovation models are mentioned in the literature reviews with different names such as work frame, paradigm, sequence, process, etc. In this paper we give a summary of six generations of innovation models in order to show their transformation from linear models to models of open innovation. Each generation of innovation models has a specific character. Independent of the chronology and typology that has been used to separate models into generations, the focus can be put on social, educational and organizational innovation on one side, and technological innovation on the other side. We focus on the company level innovation models. The first and second generation innovation models are very simple and they are predictors of innovation models of the third generation which confirm that innovation can occur in different places throughout the process. The fourth generation focuses on product and process integration and the fifth generation models accent system integration and networking. The sixth generation of innovation models is characterized by dynamism, integration, systematic approach and a high level of interactivity
Vehicle routing and scheduling β The traveling salesman problem
The classification of routing and scheduling problems depends on certain characteristics of the
service delivery system, such as size of the delivery fleet, where the fleet is housed, capacities of the vehicles, and routing and scheduling objectives. In the simplest case, we begin with a set of nodes to be visited by a single vehicle. The nodes may be visited in any order, there are no precedence relationships, the travel costs between two nodes are the same regardless of the direction traveled, and there are no delivery-time restrictions. In addition, vehicle capacity
is not considered. The output for the single-vehicle problem is a route or a tour where each node is visited only once and the route begins and ends at the depot node. The tour is formed with the goal of minimizing the total tour cost. This simplest case is referred to as a traveling salesman problem (TSP). An extension of the traveling salesman problem, referred to as the multiple traveling salesman problems (MTSP), occurs when a fleet of vehicles must be routed from a single depot. The goal is to generate a set of routes, one for each vehicle in the fleet
A summary of innovation models that promote clustering
The literature on innovation models shows six known and widely accepted generations of innovation models on both company and economy level. Three out of six generations of innovation models explain the importance of networking and
clustering. In this paper we give a summary of the generations of innovation models and show the transformation from linear to system, networking and open innovation models. The main goal is to give a framework that will be used as a foundation for creating a theoretical innovation model which should increase the companyβs innovation activity by using the concept of clustering and networking as a concept for improving the countryβs innovative performance. Companies can be clustered by regions (this will enable easier engagement and enrollment) and by industry (smaller and less competitive companies will be enabled to innovate) with a possibility of including government bodies and educational institutions in the process. Clusters have a certain dynamic and they need to be fit for long term adaptability within the regions, foster building trust and a continuous culture for innovation. The cluster policy also has an effect on the National Innovation System (NIS). For countries with low innovative activity as well as decreased funding and expenditures for research and development (R&D), it is of great importance that an innovation model is created which would help companies increase innovative activities, network and share not only the expenses, but knowledge and resources as well
ΠΠ½Π°Π»ΠΈΠ·Π° Π½Π° ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈΡΠ΅ ΠΈ ΠΌΠ΅ΡΠΊΠΈΡΠ΅ Π·Π° ΡΠ°Π·Π²ΠΎΡ Π½Π° ΠΏΡΠ΅ΡΠΏΡΠΈΠ΅ΠΌΠ½ΠΈΡΡΠ²ΠΎΡΠΎ ΠΈ Π·Π³ΠΎΠ»Π΅ΠΌΡΠ²Π°ΡΠ΅ Π½Π° Π²ΡΠ°Π±ΠΎΡΠ΅Π½ΠΎΡΡΠ° Π²ΠΎ Π Π΅ΠΏΡΠ±Π»ΠΈΠΊΠ° ΠΠ°ΠΊΠ΅Π΄ΠΎΠ½ΠΈΡΠ° - ΡΠΎ ΠΏΠΎΡΠ΅Π±Π΅Π½ ΠΎΡΠ²ΡΡ Π½Π° Π ΠΎΠΌΡΠΊΠ°ΡΠ° ΠΏΠΎΠΏΡΠ»Π°ΡΠΈΡΠ°
Π Π°Π·Π²ΠΎΡΠΎΡ Π½Π° ΠΏΡΠ΅ΡΠΏΡΠΈΠ΅ΠΌΠ½ΠΈΡΡΠ²ΠΎΡΠΎ ΠΈ ΠΏΡΠ΅ΡΠΏΡΠΈΠ΅ΠΌΠ°ΡΠΊΠ°ΡΠ° ΠΊΡΠ»ΡΡΡΠ° ΡΠ΅ ΠΏΠΎΡΠ΅ΡΡΠΎ ΡΠ΅ Π°ΠΊΡΡΠ°Π»ΠΈΠ·ΠΈΡΠ° ΠΈ ΠΌΠ½ΠΎΠ³Ρ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ°ΡΠΈ ΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ°ΡΠΈ Π·Π° ΡΠ°Π·Π²ΠΎΡΠΎΡ Π½Π° ΠΌΠ°Π»ΠΈΡΠ΅ ΠΈ ΡΡΠ΅Π΄Π½ΠΈ ΠΏΡΠ΅ΡΠΏΡΠΈΡΠ°ΡΠΈΡΠ° ΠΊΠΎΠΈ ΡΠ΅ ΠΈΠ½ΠΎΠ²Π°ΡΠΈΠ²Π½ΠΈ ΠΈ ΠΊΠΎΠΈ ΠΊΡΠ΅ΠΈΡΠ°Π°Ρ Π½ΠΎΠ²ΠΈ ΡΠ°Π±ΠΎΡΠ½ΠΈ ΠΌΠ΅ΡΡΠ°, Π³ΠΎ Π³Π»Π΅Π΄Π°Π°Ρ ΠΏΡΠ΅ΡΠΏΡΠΈΠ΅ΠΌΠ½ΠΈΡΡΠ²ΠΎΡΠΎ ΠΊΠ°ΠΊΠΎ Π΄Π²ΠΈΠ³Π°ΡΠ΅Π» Π½Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈΠΎΡ ΡΠ°Π·Π²ΠΎΡ ΠΈ ΠΌΠΎΠΆΠ½ΠΎΡΡ Π½Π° ΠΈΠ·Π»Π΅Π· Π½Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠ° ΡΠ΅ΡΠ΅ΡΠΈΡΠ° ΠΈ ΠΏΠΎΠ΄ΠΎΠ±ΡΡΠ²Π°ΡΠ΅ Π½Π° ΡΠΎΡΠΈΠΎ-Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠ°ΡΠ° ΡΠΎΡΡΠΎΡΠ±Π° Π½Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈΡΠ΅ ΡΡΠ±ΡΠ΅ΠΊΡΠΈ/Π³ΡΠ°ΡΠ°Π½ΠΈΡΠ΅. ΠΡΠ΅Π°ΡΠΎΡΠΈΡΠ΅ ΠΈ ΡΠΏΡΠΎΠ²Π΅Π΄ΡΠ²Π°ΡΠΈΡΠ΅ Π½Π° ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π²ΠΎ Π Π΅ΠΏΡΠ±Π»ΠΈΠΊΠ° ΠΠ°ΠΊΠ΅Π΄ΠΎΠ½ΠΈΡΠ° ΠΊΠ°ΠΊΠΎ Π΄Π΅Π» ΠΎΠ΄ ΠΌΠ΅ΡΠΊΠΈΡΠ΅ Π·Π° ΡΠ΅ΡΠ΅Π½ΠΈΡΠ° Π·Π° ΡΠΎΠ·Π΄Π°Π²Π°ΡΠ΅ Π½Π° Π½ΠΎΠ²ΠΈ ΡΠ°Π±ΠΎΡΠ½ΠΈ ΠΌΠ΅ΡΡΠ° ΠΈ ΡΡΠΈΠΌΡΠ»Π°ΡΠΈΠΈ Π·Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈ ΡΠ°ΡΡ Π³ΠΈ ΠΏΡΠΎΠΌΠΎΠ²ΠΈΡΠ°Π°Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈΡΠ΅ ΠΈ ΠΌΠ΅ΡΠΊΠΈΡΠ΅ Π·Π° ΠΏΡΠ΅ΡΠΏΡΠΈΠ΅ΠΌΠ½ΠΈΡΡΠ²ΠΎ ΠΈΠ»ΠΈ Π²ΡΠ°Π±ΠΎΡΡΠ²Π°ΡΠ΅. ΠΠΎ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡ Π½Π° Π½Π°ΡΠΈΠΎΡ ΠΏΡΠΎΠ΅ΠΊΡ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π°, ΠΈΠΌΠ°ΡΡΠΈ ΡΠ° ΠΏΡΠ΅Π΄Π²ΠΈΠ΄ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½Π°ΡΠ° (Π½Π΅ΠΏΠΎΠ²ΠΎΠ»Π½Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠ° ΠΈ ΡΠΎΡΠΈΡΠ°Π»Π½Π°) ΡΠΈΡΡΠ°ΡΠΈΡΠ° Π²ΠΎ ΠΊΠΎΡΠ° ΡΠ΅ Π½Π°ΠΎΡΠ° Π ΠΎΠΌΡΠΊΠ°ΡΠ° ΠΏΠΎΠΏΡΠ»Π°ΡΠΈΡΠ° Π²ΠΎ ΠΠ°ΠΊΠ΅Π΄ΠΎΠ½ΠΈΡΠ°, ΠΎΠ΄ ΡΡΡΠ΅ ΠΏΠΎΠ³ΠΎΠ»Π΅ΠΌΠΎ Π·Π½Π°ΡΠ΅ΡΠ΅ ΡΠ΅ ΠΎΠ²ΠΈΠ΅ ΠΌΠ΅ΡΠΊΠΈ, ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΈ ΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΊΠΎΠΈ ΠΌΠΎΠΆΠ°Ρ Π½Π°ΡΠΏΡΠ²ΠΈΠ½ Π΄Π° ΡΠ° Π·Π³ΠΎΠ»Π΅ΠΌΠ°Ρ ΡΠ²Π΅ΡΠ½ΠΎΡΡΠ°, Π° ΠΏΠΎΡΠΎΠ° Π΄Π° ΡΠ° ΠΏΠΎΠ΄ΠΎΠ±ΡΠ°Ρ ΠΈΠ½ΠΊΠ»ΡΠ·ΠΈΡΠ°ΡΠ° ΠΈ ΡΠΎΡΠΈΠΎ-Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠ°ΡΠ° ΠΏΠΎΠ»ΠΎΠΆΠ±Π° Π½Π° Π ΠΎΠΌΠΈΡΠ΅ Π²ΠΎ Π΄ΡΠΆΠ°Π²Π°ΡΠ°