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Volume 14, Number 1
Ahmed Mohammed ABDELALIM, Ahmed Mohamed KENAWY, Khaled AMAN
Abstract: Efficient planning for construction site layout is pivotal for the successful execution of a project, contributing to enhanced productivity and safety on the site. This involves identifying temporary structures or facilities required to support construction activities, choosing their size and arrangement, and strategic placing within the available space on the site. The problem of site layout planning is a challenging issue in combinatorial optimization, especially as it involves multiple objectives. Its complexity escalates with the increasing number of facilities and constraints. While existing research has proposed various analytical, heuristic, and meta-heuristic approaches to address this problem, many prior studies focused on a limited number of facilities, emphasizing the minimization of travelling distances while neglecting other pertinent cost-related and decision-making factors. This study aims to create practical and effective solutions for site layout by employing a realistic representation that takes into account not just travelling distance but also considers cost and safety relationships. A model for optimization with two objective functions has been developed to minimize travelling distance between facilities in order to minimize cost functions derived from various factors such as construction costs associated with different facility locations and transportation costs between locations, as well as to minimize risks based on the quantitative flow matrix and distance between facilities, as increasing in the frequency of interaction flow between facilities results in a higher probability of collision. In this research, a genetic algorithm (GA) is used as a heuristic optimization approach. A case study was applied to the model to highlight the benefits of the suggested approach, illustrating its effectiveness and comprehensive solutions for construction site layout planning.
Keywords: Construction Site Layout Planning (CSLP); Genetic Algorithm (GA); Temporary Facility (TF.); Fixed Facility (FF.); Access Road (AR.).
THE IMPORTANCE OF PROBLEM-SOLVING COMPETENCIES IN ACHIEVING CUSTOMER SATISFACTION
Zorana ANTIĆ, Srđan BOGETIĆ
Abstract: In today's business environment, in which customer satisfaction plays a crucial role in an organization's ability to survive, problem-solving competencies are considered essential, since they enable organizations to efficiently identify, analyse and solve problems that could have an impact on the customer experience. It is no longer enough for employees in direct contact with customers just to be kind and helpful, as today it is necessary for them to respond quickly and efficiently to challenges that affect customer satisfaction. Problem-solving competencies are widely regarded as a fundamental skill necessary for success in the modern world and are of key importance for all business activities, particularly in customer relations. Along with the primary objective of satisfying customers, successful problem-solving also fulfils the requirements and principles of quality standards linked to improvements, resulting in a better quality of products and services, more efficient business processes, greater customer trust and enhanced business reputation. The authors of this paper point to the issue of insufficient employee motivation in the area of developing problem-solving competencies. There are several reasons for this attitude among employees: the accelerated development of digital technologies encourages the acquisition of digital skills; employees have insufficient awareness of the advantages of gaining competencies; managers lack understanding of the benefits of problem-solving competencies; and young people encounter insufficient opportunities to acquire problem-solving skills within their formal education.
Keywords: Competencies; Problem-solving; Customer satisfaction; ISO 10002.
COMPLEX PROBLEM-SOLVING IN ENTERPRISES WITH MACHINE LEARNING SOLUTIONS
Luka ĐORĐEVIĆ, Borivoj NOVAKOVIĆ, Mića ĐURĐEV, Velibor PREMČEVSKI, Mihalj BAKATOR
Abstract: This paper explores the application of machine learning (ML) in solving complex problems within enterprises across various industries. By leveraging ML, businesses can enhance operational efficiency, customer experience, and risk management. The study reviews existing literature to develop a theoretical model that integrates ML applications into business processes. Key findings indicate that ML significantly improves quality control and predictive maintenance in manufacturing, leading to reduced costs and increased productivity. Additionally, ML-driven personalized marketing and customer support enhance customer satisfaction and loyalty. In financial management, ML enhances fraud detection and credit risk assessment, contributing to financial stability and security. The paper provides suggestions for effectively implementing ML strategies to optimize business performance and addresses the implications for future business operations in a rapidly evolving technological landscape.
Keywords:Complex problem solving; Machine learning solutions; Enterprises; Improvement; Competitiveness.
EXCAVATOR DOWNTIME’S DIFFERENCES BETWEEN TYPES AND COMPARISON WITH OTHER MINING EQUIPMENT
Vesna SPASOJEVIĆ BRKIĆ, Ivan MIHAJLOVIĆ, Mirjana MISITA, Martina PERIŠIĆ, Nemanja JANEV
Abstract: Mining equipment working time is critical for ensuring output and fulfilling mining project deadlines. In order to find trends and possible downtime minimization measures, this article analyses numerous types of excavators, dump trucks, loaders, backhoe loaders, bucket wheel excavators, and surface-top hammer drill rigs in Serbian mining sites. The study collects data on mechanical, electrical, technological, organizational, abuse and meteorological downtimes types with the aim to highlight efficient ways for minimizing non-operational periods, hence improving the overall productivity and safety of mining operations in the industry. Special emphasis is put on excavators whose role in the mining industry is pivotal, directly impacting project timelines and financial outcomes. The methodology includes analysing the duration and implications of different downtime categories. Collected data showed non-parametric features, according to the descriptive statistics. To compare the data, the non-parametric Mann-Whitney U-test is implemented. The results of the research showed that, in most cases, there is a difference in the duration of downtimes between different categories of excavators` downtimes. However, a comparison between excavator downtimes and other machinery downtimes has not shown any statistically significant differences. This analysis aims to contribute to the optimisation of mining equipment usage, offering valuable insights for mining industry stakeholders.
Keywords: Mann-Whitney U-test; Excavator; Downtime; Mining equipment
LOGISTICS IT SUPPORT SOLUTIONS IN ZALA COUNTY
Károly SZABÓ
Abstract: Industry 4.0 solutions such as the use of AI are becoming increasingly visible in everyday logistics processes. Many publications have already dealt with what technological innovations could be introduced to improve the flow of materials, but relatively few have dealt with the extent to which these theoretical solutions are also present in practice. In connection with this gap, the current research aims to examine the effectiveness and impact of AI use in the logistics field. This paper is a follow-up study on the topic as the logistics AI use was already analyzed in Pest County in the framework of this study direction. Since this study is still the second step of the already commenced research work, the very study does not try to draw conclusions leading to generalization but rather aims to draw causal conclusions of a pilot nature. In the research tools, the paper uses the qualitative design from the previous study (applied in another geographical territory – Zala County) but at the same time examines other analyzing methods like SEM modelling as a potential tool to be integrated.
Keywords: Logistics; AI; ERP system; Strategy; Planning.
ADDITIVE TECHNOLOGY AND 7R METHODOLOGY IN CIRCULAR ECONOMY FOR WEARABLE SENSORS PRODUCTION
Miloš VORKAPIĆ, Stefan ILIĆ, Marko SPASENOVIĆ, Miloš VASIĆ, Dragan ĆOĆKALO
Abstract: The paper presents the 7R algorithm of the circular economy principle in realizing wearable sensors. The application of additive manufacturing in the realization of sensors is essential from the point of view of sustainable production, which starts from the material and ends with its recycling process. All seven principles and their connection with additive manufacturing as a critical element in the circular economy are presented. The paper defines the theoretical framework for realizing a sustainable wearable sensor. The production of such sensors primarily refers to the application of flexible 3D printing and electronic components that can be quickly replaced, modified, disassembled, and recycled.
Keywords: Wearable sensors; 7R model; Additive manufacturing; Algorithm; Circular economy.
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