Search Results (65)

The growing miniaturization of electronic systems and the expansion of sustainable, autonomous IoT technologies emphasize the need for efficient, ultra-low-power energy harvesting devices. This study evaluates fifteen devices from five industry leaders for use in small-scale autonomous seed germination systems. Its novelty lies in applying a competitive profile matrix within a flexible multicriteria evaluation framework based on the simple additive weighting (SAW) method that uses a comprehensive set of competitive technology factors (CTFs). The results demonstrate that a transparent and structured methodology can generate prioritized lists of suitable energy harvesters while accounting for technical, economic, and environmental trade-offs. The study also shows that device rankings depend on the scope and objectives of the project. If these change, then the CTF selection, classification, and weighting adjust accordingly. Therefore, the relevance of this study lies in the adaptability, replicability, and audibility of the proposed framework, which supports the selection of informed technology for autonomous, IoT-based germination systems and other technological projects. Full article

Background: Green logistics practices are crucial for achieving the EU’s Green Deal objectives, addressing environmental challenges, improving supply chain efficiency, and fostering business sustainability. This paper presents a conceptual framework for green logistics practices and their application for ensuring sustainable organisational development. Methods: Using the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methodologies, this study assesses the importance of green logistics practices in Lithuanian SMEs and their future application. The AHP method facilitates pairwise comparisons to determine the weights of green logistics criteria, while the SAW method evaluates the final sub-criteria by aggregating normalized scores according to the identified weights. Results: A survey of ten companies revealed that green transportation is the most developed green logistics practice, with the focus on infrastructure, skills and transport optimisation. Green warehousing is the second most significant practice, with SMEs considering it vital to green logistics because of its sustainable warehousing measures. Green packaging is considered third in terms of importance, due to the attention paid to the packaging materials used. Conclusions: The full potential of green logistics has yet to be realised. Adopting a more balanced approach could enhance environmental outcomes and bolster the resilience of the long-term supply chain. Full article

The rapid proliferation of e-commerce has reshaped the spatial logic and facility typologies of urban logistics. While the literature on logistics facility location selection is extensive, there is limited understanding of how the relative importance of location criteria varies across facility types shaped by e-commerce. This study addresses this gap by analyzing the location criteria of logistics facilities of different sizes using a multi-criteria decision-making (MCDM) approach. Twenty-five criteria, identified through a literature review and feedback from seven experts in the Istanbul e-commerce logistics sector, were analyzed using the Fuzzy Simple Additive Weighting (SAW) method. The relative weights of criteria were calculated for three facility scales, macro-, meso-, and micro-scales, to reveal how location priorities vary across scales. Proximity to main arteries ranks first across all scales (macro: 0.317, meso: 0.431, micro: 0.409). Land rental values are highly prioritized at both the macro- and meso-scale, while population density ranks prominently at the macro- and micro-scale. At the meso-scale, shopping mall proximity gains notable weight, whereas intermediate arteries stand out as a key factor at the micro scale. These findings advance the understanding of scale-sensitive dynamics in urban logistics and provide a framework for more adaptable and sustainable logistics planning. Full article

The selection of an appropriate formwork system represents a critical decision in the planning of reinforced concrete multi-story buildings. While this decision has traditionally been deferred to the construction phase, increasing evidence of time and cost overruns in construction projects has highlighted the necessity of addressing it during earlier stages, particularly in design and planning. Early identification and selection of the optimal formwork system enhances the likelihood of achieving significant improvements in both time efficiency and cost effectiveness. To facilitate this process, a decision-support framework based on the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods has been developed. This framework provides decision-makers with a structured and systematic approach for evaluating alternatives and selecting the most suitable formwork system for a given project. By offering an analytical foundation for the decision-making process, the framework assists designers and engineers in mitigating risks associated with delays and potential standstills during construction. The findings indicate that the proposed decision-support framework ensures both clarity and consistency in decision-making outcomes, irrespective of the analytical method employed. Consequently, it contributes to more robust planning and execution of construction projects. Full article

In today’s digital era, the need for a qualified workforce in the field of IT Support is in-creasing along with the rapid development of information technology. The selection of the right employees is very important to ensure the efficiency and effectiveness of companies’ operations. This research aims to develop a decision support system (SPK) in the IT Support division employee selection process using the Simple Additive Weighting (SAW) method. This method was chosen because of its ability to process qualitative and quantitative data to produce optimal decisions. The results of this study show that the proposed system can assist managers in selecting the most suitable candidates based on predetermined criteria. Thus, it is expected that this system can improve the quality of the recruitment process in companies. Full article

The complexity of smart home technologies and the need for personalized energy efficiency solutions highlight the importance of user-oriented decision-support tools. This study presents a Smart Home Management System (SHMS) selection solution that combines a web-based dashboard, a mobile application, and a relational database. A 54-question structured questionnaire was designed to capture user requirements, and four alternatives—KNX, JUNG Home, LB Management, and eNet Smart Home—were compared using the Simple Additive Weighting (SAW) method. Evaluation criteria included installation complexity, communication technology, integration and control capabilities, and user experience. The system was implemented with Next.js, React Native, and Post-greSQL, ensuring flexibility, scalability, and secure data management. Preliminary evaluation with specialists (system integrators, architects, designers) and students confirmed the coherence of the questionnaire, the adequacy of criteria, and the clarity of recommendations. Results showed that the tool improves user engagement, reduces decision-making uncertainty, and supports the adoption of energy-efficient residential solutions. The study’s main limitation is the small test sample, which will be expanded in future large-scale validation. Planned improvements include interactive product comparisons, cost estimation, adaptive questionnaire logic, and 3D visualizations. Overall, the system bridges the gap between technical SHMS solutions and user-oriented decision-making, offering practical and academic value. Full article

Key Performance Indicators (KPIs) play a crucial role in systematically evaluating employee performance. This study integrates the Simple Additive Weighting (SAW) method with Design Science Research (DSR) to develop and validate a web-based KPI assessment system for IT Support staff. This system categorizes performance into three key areas: supervisor evaluation, routine tasks, and request-based tasks. SAW is chosen due to its effectiveness in multi-criteria decision-making and the objective ranking of employees. This research presents a scalable and practical solution for performance management through a structured web-based application. A simulation utilizing Bootstrap 5.1 incorporates real-time monitoring, ticket tracking, and KPI dashboards, enhancing data visualization and reporting. The results indicate that the proposed system enhances transparency, minimizes evaluation bias, and supports objective, data-driven decision-making. By integrating SAW within a structured decision support system, this approach fosters standardized performance assessments, ensuring fairer and more consistent employee evaluations. Future work should focus on real-world implementation and empirical validation. Full article

SMK XYZ is a private school under Yayasan Pembina Pendidikan Doa Bangsa (YPPDB) which was established in 2011. The school has several expertise programs, including Software Engineering, Institutional Accounting and Finance, and Motorcycle Business Engineering. Assessing the success of a school is an important thing that greatly affects the development of students in the learning process to achieve their goals. Assessment of teachers’ work should be performed using appropriate and efficient methods. To improve teacher performance, the development of an agenda monitoring and assessment system based on the Simple Additive Weighting (SAW) method can be an effective alternative. This system is designed to assist school management in monitoring teacher activities objectively and measurably, as well as providing clear assessments based on certain criteria such as attendance, tardiness, student evaluation results, and innovation in learning. The SAW method is used to calculate the final score of teacher performance by summing up the weighted values of each normalized criterion. In this case study, the system helps decision makers to recognize the strengths and weaknesses of each teacher, so that related recommendations for competency development can be given. The implementation of this system demonstrates increased responsibility in appraisal and motivates teachers to improve their performance according to set standards. Full article

Project success is a complex and debated concept in construction project management, even more so for public sector infrastructure projects. This study proposes a new, data-driven methodology to assess the success of public infrastructure projects using a multi-criteria decision-making framework. By utilizing empirical data from 30 completed road infrastructure projects the study applies the Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method to evaluate performance across four key success criteria: cost, time, quality, and project management. An integrated Success Index (SI) was then calculated using the Simple Additive Weighting (SAW) method under two different weighting scenarios. Results show that projects with shorter durations and simpler scopes consistently achieved higher SI scores, while larger, more complex projects were more prone to delays, cost overruns, and quality issues. This study contributes to scientific research by utilizing real, archival project data rather than relying on expert opinions to quantify project success from the client contracting authority’s perspective rather than that of the contractor. Hence, the proposed model serves as a practical, adaptable tool for public contracting authorities seeking to benchmark and improve project performance. Full article

This paper presents HERMEES—Holistic Evaluation and Ranking Model for Energy Efficient Systems. HERMEES is based on a multi-criteria decision-making (MCDM) model designed to select the optimal combination of lightweight cryptography (LWC) and topology construction protocol (TCP) algorithms for wireless sensor networks (WSNs) based on user-defined scenarios. The proposed model is evaluated using a scenario based on a medium-sized agricultural field. The Simple Additive Weighting (SAW) method is used to assign scores to the candidate algorithm pairs by weighting the scenario-specific criteria according to their significance in the decision-making process. To further refine the selection, mean shift clustering is utilized to group and identify the highest scored candidates. The resulting model is versatile and adaptable, enabling WSNs to be configured according to specific operational needs. The provided pseudocode elucidates the model workflow and aids in an effective implementation. The presented model establishes a solid foundation for the development of guided self-configuring context-aware WSNs capable of dynamically adapting to a wide range of application requirements. Full article

The rich history of the olive tree is deeply connected to the heritage of the Mediterranean region. There are olive trees that are still productive and their age has been calculated by the use of methods of increment core sampling, radiocarbon dating (C14) and luminescence dating (OSL) to be over two thousand years old. However, the age of these trees is not usually known and it is not easy to calculate. As a result, deciding whether an olive tree is monumental is a rather complicated task. The goal of this paper is to present the design and implementation of an intelligent system that uses multi-criteria decision-making to evaluate olive trees and make the decision of whether they are monumental. This information is further used by a system to decide whether an olive grove is monumental or not. The methodology is implemented in a cross-platform application called “Olea App”. The system evaluates different olive trees and evaluates trees and olive groves to select the one that is considered the best to be promoted. The system uses and combines three different multi-criteria decision-making theories, namely, analytical hierarchy process (AHP), simple additive weighting (SAW), and multicriteria optimization and compromise solution (VIKOR) and evaluates olive trees based on tangible and intangible criteria. The method proposed was used to evaluate trees in the Ionian Islands and has proven very effective. The cross-platform application could be used by other researchers to evaluate their olive trees and groves if they cannot apply methods for the estimation of the tree’s age such as the methods of OSL. This work introduces a novel, technology-driven solution for the identification, evaluation, and preservation of monumental olive trees. By integrating scientific, cultural, and technological perspectives, the study provides a sustainable and accessible methodology to ensure these ancient natural landmarks are protected for future generations. The Olea app represents a significant advancement in heritage tree conservation, offering a structured, transparent, and scalable approach to preserving olive tree ecosystems while supporting sustainable tourism and economic incentives for their protection. Full article

This paper presents a method of choosing a single solution in the Pareto Optimal Front of the multi-objective problem of the spectral and energy efficiency trade-off in Massive MIMO (Multiple Input, Multiple Output) systems. It proposes the transformation of the group of non-dominated alternatives using the Box–Cox transformation with values of λ < 1 so that the graph with a complex shape is transformed into a concave graph. The Box–Cox transformation solves the selection bias shown by the decision-making algorithms in the non-concave part of the Pareto Front. After the transformation, four different MCDM (Multi-Criteria Decision-Making) algorithms were implemented and compared: SAW (Simple Additive Weighting), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), PROMITHEE (Preference Ranking Organization Method for Enrichment Evaluations) and VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The simulations showed that the best value of the λ parameter is 0, and the MCDM algorithms which explore the Pareto Front completely for different values of weights of the objectives are VIKOR as well as SAW and TOPSIS when they include the Max–Min normalization technique. Full article

Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This study aims to enhance the decision-making process by identifying optimal construction techniques, material selection, and ventilation window dimensions to promote sustainable energy use in buildings. A novel framework combining Building Information Modeling (BIM), computational analysis, and Multi-Criteria Decision-Making (MCDM) approaches is applied to assess the energy use intensity (EUI), annual electric energy consumption, and lifecycle energy cost across multiple sequences for each type of construction. Computational analysis in this research is combined in two main tools. Minitab is utilized for experimental design to determine the number and configurations of sequences analyzed. The Simple Additive Weighting (SAW) method, applied as an MCDM tool, is used to assess and rank the performance of sequences based on equally weighted criteria. Subsequently, 3D models of case study buildings are developed, and energy simulations are conducted using Autodesk Revit and Autodesk Green Building Studio, respectively, as BIM tools to compare the energy performance of various design alternatives. The results revealed that 3D printing surpassed other methods, where Sequence 7 achieved approximately 10.3% higher efficiency than modular methods and 40.5% better performance than conventional methods in the evaluated criteria. The findings underscore the higher energy efficiency of 3D printing, followed by modular construction as a competitive method, while conventional methods lagged significantly. Full article

The transition to a circular economy is important in achieving sustainability, promoting resource efficiency, and reducing environmental impact. This paper aims to assess the development of a CE in EU countries and highlight the use of environmentally friendly practices for its development. Decision-making methods based on various criteria provide a solid basis for evaluating complex and multidimensional circular economy (CE) initiatives. Simple Additive Weighting (SAW), the widely used MCDM method, facilitates sorting and selection according to the generalised results obtained according to weighted criteria. Due to its simplicity and ease of use, this method is particularly useful for assessing CE development in different countries. The evaluation will be based on a comprehensive overview of the available literature and empirical data, allowing for a comprehensive assessment of the CE’s development initiatives in the European context using the SAW method. The results show that while significant progress has been made in the EU’s transition to a circular economy (CE), disparities among Member States and data limitations hinder a comprehensive assessment. Italy, Germany, France, the Netherlands, and Spain are among the most advanced countries in achieving a circular economy’s objectives. The study proposes a novel MCDM-based framework that effectively evaluates CE performance, identifying key strengths and weaknesses across countries. By focusing on competitiveness and innovation indicators and incorporating environmental factors, the framework offers valuable insights for policymakers and stakeholders. Full article

In addressing asset-liability management (ALM) problems, goal programming (GP) has been widely applied to integrate multiple objectives. However, it is inadequate in handling data changes in ALM caused by interest rate fluctuations. Therefore, a more robust and improved ALM optimization method is needed to manage fluctuations in financial ratios in ALM. This study introduces a novel approach by combining a systematic literature review (SLR) with the preference reporting items for systematic reviews and meta-analysis (PRISMA) method and bibliometric analysis to investigate the application of robust goal programming (RGP) models in ALM. The methodology involved planning, search and selection, analysis, and result interpretation as part of the SLR process. Using PRISMA, seven relevant publications were identified. The results of this SLR present a new strategy to combine goal programming and robust optimization to enhance ALM. Model development steps include constructing weighted goal programming (WGP) or lexicographic goal programming (LGP) models, using factor analysis for financial ratios, applying the best-worst method or simple additive weighting (SAW) for prioritization, and modeling financial ratio uncertainty with robust counterparts. This research provides a foundation for further studies and offers guidance to non-financial companies on adopting RGP for strategic ALM decisions and optimizing ALM under uncertainty. Full article