Search

This research focuses on small- and medium-sized businesses that provide machining or other process services but do not produce their own products. Their daily manufacturing schedule varies according to client needs. Small- and medium-sized businesses strive to operate in these circumstances by extending their customer base and creating adequate production planning targets. Their resources are limited, including the technical and technological components of their equipment, tools, people resources, time, and capacities. As a result, planning operations with the present resources of small- and medium-sized businesses in the midst of the global economic crisis is a widespread issue that must be addressed. This study seeks to offer a novel mathematical optimization model based on a genetic algorithm to address job shop scheduling and capacity planning difficulties in small- and medium-sized businesses, therefore improving performance management and production planning procedures. On the basis of the created optimization model, an appropriate software solution, and quantitative data concerning the job shop scheduling and capacity planning challenges of manufacturing operations in small- and medium-sized businesses, the study findings will be obtained. The practical implications include the establishment and development of a decision support system based on the genetic algorithm optimization method, which may improve the effectiveness of the flexible job shop scheduling problem and capacity planning in the production planning process. The given model and the application of the differential precedence preservative crossover operator within genetic algorithms are what constitute the novelty of this study.

The Big Data expansion is emerging alongside the development of information technology. Increasing the number of processes drastically increases the number of data. Data are in various forms from paper, audio, geological, digital and other forms. At the same time, computers are becoming increasingly accessible in all spheres of life. Computers take over the role of Big Data management, manipulating them, switching from one location to another and processing them. New technologies are being developed with the aim of managing data in order to improve business reporting, analysis and identifying opportunities to improve business. Therefore, digitization becomes an imperative in the data management strategy.

The paper presents the methods for prediction of the cutting parameters. In order to test the workability of the material by process of scraping, from the aspect of the cutting temperature, an natural thermopar was placed just below the cutting edge of the plate. In this way, a simple, reliable, accurate and economical method for determining the workability of material by cutting is obtained. The feasibility study of several semi-finished products by applying the realized experiments was carried out. Different materials in processing with cutting discs with different coatings give different results, which are used to form neural network and linear regression models.

This paper develops multirelational data base for major depression costs. It lists how data are collected and stored into the fact base and dimension base. Uncertain data is described linguistically and modelled by fuzzy sets. Linguistic expressions are stored in dimension base. Models of major depression treatment costs are developed for each patient and all population. On the basis of this model and multirelational data base MD-OLAP a model for major depression treatment costs is developed.

Managing defects in agricultural fruit processing is crucial for maintaining quality and sustainability in the fruit market. This study explores the use of edge devices, web applications, and machine vision algorithms to improve defect reporting and classification in the strawberry processing sector. A software solution was developed to utilize edge devices for detecting and managing strawberry defects by integrating web applications and machine vision algorithms. The study shows that integrating built-in cameras and machine vision algorithms leads to improved fruit quality and processing efficiency, allowing for better identification and response to defects. Tested in small organic and conventional strawberry processing enterprises, this solution digitizes defect-reporting systems, enhances defect management practices, and offers a user-friendly, cost-effective technology suitable for wider industry adoption. Ultimately, implementing this software enhances the organization and efficiency of fruit production, resulting in better quality control practices and a more sustainable fruit processing industry.

Since small and medium enterprises (SME) generate the most of gross domestic product and employment opportunities in developed countries, further progress of the Industry 4.0 strongly relies on the development of dedicated solutions for SMEs-specific problems. Dimensional inspection of extruded rubber profiles represents one such an open issue in the automotive industry, which currently requires a manual measurement and comparison of profiles' cross section with the corresponding technical drawings. Starting from the requirements acquired from the industry practice, this study proposes a novel solution that automates all steps during the inspection process allowing an operator to make the final decision with respect to his observations. The proposed workflow includes the following steps: 1) image acquisition, 2) system calibration, 3) profile segmentation, 4) landmark registration and 5) augmentation of the referent technical drawing over the acquired image. The overall solution was developed by using a single camera and dedicated algorithms for profile detection and augmentation of the referent technical drawing. The extensive validation showed that the solution increased operators' productivity and reliability by a considerable margin (~6%), while it remains affordable, user-friendly and generic.

The paper presents description methods of metal cutting and calculation of treatment costs based on model that is developed on Faculty of mechanical engineering in Kragujevac. Based on systematization and analysis of large number of calculation models of cutting with unconventional methods, mathematical model is derived, which is used for creating a software for calculation costs of metal cutting. Software solution enables resolving the problem of calculating the cost of laser cutting, comparison' of costs made by other unconventional methods and provides documentation that consists of reports on estimated costs.

Studies of small and medium-sized enterprise (SMEs) development around the world show that the most significant factor for increasing their numbers and improving business success is the free enterprise, as exogenous, and innovation as an endogenous variable. At the same time, the dominant view in economic theory is that innovation is a key generator of changes for which the SMEs can be considered as a kind of metaphors for a successful business over the last twenty years in a number of economies. Arguing that cooperation between SMEs is increasingly common generic strategy of their development, the paper first explains the importance of collaboration to increase innovation and competitiveness, and then provides possible models using information technology such as Workflow Management Systems (WfMS), Service Oriented Architecture (SOA) and Service-Oriented Cloud Computing Architecture (SOCCA) to support the collaboration of these business entities. Solutions provided are aimed at improving the innovativeness of SMEs and fully follow the requirements of the so-called fifth-generation innovation process whose key attributes are integration and flexibility.

Optimization of machining processes is one of the most important elements in the planning of metal parts production. In this paper, we have applied ABC methods to determine the cost of all processes that are used in production of homocinetical sleeve joint. After that we have used multy-criterion optimization technique based on genetic algorithms, in order to optimize the basic parameters of all the processes: the speed and feed. The objective function is given in a form of specific cost for each processes, for which minimization it is need to consider the appropriate mechanical and manufacturing constraints. The proposed model uses a genetic algorithm, so that after a certain number of iterations optimal result is reached that will satisfy the objective function and all anticipated limitations. Obtained results shows that GA solves the optimization problem in an efficient and effective manner, so that the results can be integrated into an intelligent manufacturing system for solving complex optimization problems in machine production processes.

The difference between the production cost and selling price of the products may be viewed as a criterion that determines an organization’s competitiveness and market success. In such circumstances, it is necessary to impact these criteria in order to maximize this difference. The selling products’ price, in modern market conditions, is a category which may not be significantly affected. So organizations have one option, which is the production cost reduction. This is the motive for business organizations and the imperative of each organization. The key parameters that influence the costs of production and therefore influence the competitiveness of organizations are the parameters of production machines and processes used to create products. To define optimal parameter values for production machines and processes that will reduce production costs and increase competitiveness of production organizations, the authors have developed a new mathematical model. The model is based on application of the ABC classification method to classify production line processes based on their costs and an application of a genetic algorithm to find the optimal values of production machine parameters used in these processes. It has been applied in three different modern production line processes; the costs obtained by the model application have been compared with the real production costs.

One of the basis of a series of standards JUS ISO 9000 is quality system documentation. An architecture of the quality system documentation depends on the complexity of business system. An establishment of an efficient management documentation of system of quality is of a great importance for the business system, as well as in the phase of introducing the quality system and in further stages of its improvement. The study describes the architecture and capability of software solutions to support and manage the quality system documentation in accordance with the requirements of standards ISO 9001:2001, ISO 14001:2005 HACCP etc.

Business incubators aim to maximize the chances of success of start - up companies by creating a supportive environment. Typically, this involves offering management assistance, mentoring, access to financing, flexible and low - cost leases, office services, etc. There is large number of business incubators in the world. In this paper issues concerned status, development and overview of existing practice in the world will be presented as well as general issues and perspectives for developing countries.