Search Results (2,744)

The construction industry is growing with the shortfall issues of productivity, functionality, and cost. Precast construction has significant potential to address these issues by incorporating lean principles. Lean focuses on enhancing value at every stage of the construction process. By combining these two approaches, the construction industry can effectively tackle these challenges. This research aims to achieve two main objectives: (1). To establish a connection between lean tools and non-value added (NVA) activities, (2). To prioritize these lean tools based on their relevance to major NVA activities. To accomplish this, an extensive review of the literature was conducted to examine the adoption of lean tools in various NVA tasks. A questionnaire survey was then employed to identify the root causes of NVA activities (criteria) and determine the most suitable lean tools for addressing each specific criterion. The findings from multi-criteria decision decision-making (MCDM) analysis highlight that total quality management (TQM) is ranked first in two methods while continuous improvement (CI) ranked first in one method. Comparing all the scenarios, it is observed that 5S and CI have been fluctuating between two and three rankings, and the remaining ranks have very minute changes. Based on all these lean tools are prioritized as TQM > CI > 5S > JIT > VSM > PY. Full article

Resource conflicts constitute a major global issue in areas rich in natural resources. The modeling of factors influencing natural resource conflicts (NRCs), including environmental, health, socio-economic, political, and legal aspects, presents a significant challenge compounded by inadequate data. Quantitative research frequently emphasizes large-scale conflicts. This study presents a novel multilevel approach, SEFLAME-CM—Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management—for advancing understanding of the relationship between NRCs and drivers under territorial and rebel-based typologies at a community level. SEFLAME-CM is hypothesized to yield a more robust positive correlation between the risk of NRCs and the interacting conflict drivers, provided that the conflict drivers and input variables remain the same. Local knowledge from stakeholders is integrated into spatial decision-making tools to advance sustainable peace initiatives. We compared our model with spatial multi-criteria evaluation for conflict management (SMCE-CM) and spatial statistics. The results from the Moran’s I scatter plots of the overall conflicts of the SEFLAME-CM and SMCE-CM models exhibit substantial values of 0.99 and 0.98, respectively. Territorial resource violence due to environmental drivers increases coast-wards, more than that stemming from rebellion. Weighing fuzzy rules and conflict drivers enables equal comparison. Environmental variables, including proximity to arable land, mangrove ecosystems, polluted water, and oil infrastructures are key factors in NRCs. Conversely, socio-economic and political factors seem to be of lesser importance, contradicting prior research conclusions. In Third World nations, local communities emphasize food security and access to environmental services over local political matters amid competition for resources. The synergistic integration of fuzzy logic analysis and community perception to address sustainable peace while simultaneously connecting environmental and socio-economic factors is SEFLAME-CM’s contribution. This underscores the importance of a holistic approach to resource conflicts in communities and the dissemination of knowledge among specialists and local stakeholders in the sustainable management of resource disputes. The findings can inform national policies and international efforts in addressing the intricate underlying challenges while emphasizing the knowledge and needs of impacted communities. SEFLAME-CM, with improvements, proficiently illustrates the capacity to model intricate real-world issues. Full article

This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential for real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm Optimization (HGWOPSO) and Hybrid World Cup Optimization with Harris Hawks Optimization (HWCOAHHO)—designed to symmetrically balance global exploration and local exploitation, thereby enhancing model training and adaptation in IoT environments. These methods leverage complementary search behaviors, where symmetry between global and local search processes enhances convergence speed and detection accuracy. The proposed approaches are validated using real-world IoT datasets, demonstrating significant improvements in anomaly detection accuracy, scalability, and adaptability compared to state-of-the-art techniques. Specifically, HGWOPSO combines the symmetrical hierarchy-driven leadership of Grey Wolves with the velocity updates of Particle Swarm Optimization, while HWCOAHHO synergizes the dynamic exploration strategies of Harris Hawks with the competition-driven optimization of the World Cup algorithm, ensuring balanced search and decision-making processes. Performance evaluation using benchmark functions and real-world IoT network data highlights superior accuracy, precision, recall, and F1 score compared to traditional methods. To further enhance decision-making, a Multi-Criteria Decision-Making (MCDM) framework incorporating the Analytic Hierarchy Process (AHP) and TOPSIS is employed to symmetrically evaluate and rank the proposed methods. Results indicate that HWCOAHHO achieves the most optimal balance between accuracy and precision, followed closely by HGWOPSO, while traditional methods like FFNNs and MLPs show lower effectiveness in real-time anomaly detection. The symmetry-driven approach of these hybrid algorithms ensures robust, adaptive, and scalable monitoring solutions for IoT networks characterized by dynamic traffic patterns and evolving anomalies, thus ensuring real-time network stability and data integrity. The findings have substantial implications for smart cities, industrial automation, and healthcare IoT applications, where symmetrical optimization between detection performance and computational efficiency is crucial for ensuring optimal and reliable network monitoring. This work lays the groundwork for further research on hybrid optimization techniques and deep learning, emphasizing the role of symmetry in enhancing the efficiency and resilience of IoT network monitoring systems. Full article

Water, energy, food, and ecosystem (WEFE) components constitute fundamental dimensions contributing to human well-being, poverty alleviation, and sustainable development. Despite the prevalent specialization among WEFE professionals, there is a lack of multidisciplinary approaches in their work, with limited attention given to carbon footprint management. Against this backdrop, this study aims to explore the potential role of standardization and multi-criteria decision analysis (MCDA) in implementing the WEFE approach within the food sector. The research entails a comprehensive examination of the International Standard Organization (ISO) 22000 certifications in Balkan countries, coupled with an analysis of the ISO 14067 standard and its alignment with food safety requirements. Finally, this study proposes a novel MCDA framework for integrating food safety considerations with criteria, factors, and indicators aimed at addressing both food safety and carbon footprint management. A hierarchical structure composed of influential criteria and factors was used to rank activities in sustainable, preferably carbon-neutral food production. Group decision making was applied in the fuzzy domain using triangular numbers, and the influence of experts was determined based on their experience. Practical recommendations aimed at managing trade-offs between the requirements of two elaborated standards are provided, emphasizing key environmental, societal, and economic insights to identify critical indicators for addressing biases in food safety and carbon footprint management. Full article

The prioritization of energy renovations is critical to achieving sustainability goals and addressing socio-economic disparities in building stock. This study proposes a novel hybrid MultiCriteria Decision-Making framework for identifying and prioritizing residential buildings for energy efficiency upgrades. By integrating granular building-level data, such as energy performance and construction year, with socio-economic indicators like energy poverty and municipal income, the framework provides a comprehensive and equitable approach. Using Python for data integration and analysis, the methodology applies weighted factors to calculate the Building Priority Factor and the Municipal Energy Poverty Factor. A prioritization analysis for Slovenia demonstrates significant regional disparities in energy savings potential and renovation priorities, with some regions emerging as high-priority targets due to their aging infrastructure and elevated energy poverty levels. Conversely, densely populated urban regions with larger cities show lower prioritization needs. The proposed framework addresses limitations in existing methods by incorporating socio-economic and spatial data, enabling a dynamic and scalable approach to financial incentives. This approach aligns with the Energy Performance of Buildings Directive, providing actionable insights for national renovation plans. The findings highlight the importance of targeted, regionally tailored interventions to maximize energy savings, reduce inequities, and support sustainable development goals. Full article

The development of biogas technology is essential as a renewable energy source, aiding global initiatives in sustainable energy production and waste management. Geographical, technological, and economic factors significantly vary the efficiency and viability of biogas facilities by area. This study compares the techno-economic, social, and environmental impacts of biogas plants in Germany and Pakistan using a multicriteria decision-making method that combines the Analytic Hierarchy Process and SMARTER. This research has determined the weighting factors and then assessed the comparative performance of six selected biogas facilities based on five different scenarios: (i) comprehensive base-case, (ii) environmental performance, (iii) economic performance, (iv) social performance, and (v) per-kW energy efficiency. Three of these biogas facilities are in Pakistan (a low–medium-income developing country) and three in Germany (a high-income developed country). The findings of the study indicate that technical performance is the most heavily weighted criterion, playing a crucial role in determining the overall sustainability scores. Germany’s Bioenergie Park Güstrow stood out as the leading performer, achieving sustainability scores of 63.1%, 72.9%, and 73.0% across the comprehensive base-case, environmental, and per-kW efficiency scenarios, respectively. In the same scenarios, the Gujjar Colony Biogas Plant in Pakistan recorded the lowest scores of 25.4%, 43.2%, and 53.0%. The plants selected from a developed country showed a progressive score of high impact towards sustainability in most of the scenarios. In contrast, plants selected from a developing country showed low bioenergy deployment due to various factors, highlighting the gaps and flaws in achieving optimized energy generation and sustainable growth. The critical techno-economic and socio-environmental findings of the study are vital for policymakers, industry, engineers, and other relevant stakeholders seeking to enhance the performance, scalability, and sustainability of biogas technologies across developing and developed economies. Full article

This paper aims to effectively tackle decision-making problems on interval-valued Fermatean fuzzy sets; the current research suggested an integrated approach based on the WASPAS method. The criteria weights were determined by combining the objective weights obtained by the similarity measure approach with the subjective weights provided by decision-makers. This combination made achieving more realistic weights possible. Interval-valued Fermatean fuzzy sets were subjected to improved scoring functions and novel similarity measures concerning objective and subjective weights. The application of green supply chain management is discussed to show that the created approach fully applies to multi-criteria decision-making issues in the actual world. Green supply chain management was examined using critical success factors to control and audit enterprises’ processes as a numerical example of the developed method. In the examinations, it was seen that the companies implementing the selected GSC applications achieved close results and thus acted appropriately to the situation. To validate the stability of the developed technique, this study also includes a sensitivity analysis utilizing different weights of criterion and different values of the method’s parameters. According to the investigation, merging subjective and objective weights enhanced the method’s stability created with different criteria weights. The outcomes of the approach developed here were compared with those of other approaches previously put forth in the literature to assess its performance accurately. Full article

The operational condition of fire water supply aims to ensure the continuous and reliable supply of high-pressure water in emergency situations. Assuming a fire breaks out in a mountain village located far from the city center, due to the significantly higher flow rate and velocity of the water supply pipeline compared to normal operating conditions, any malfunction or shutdown of the pump caused by improper operation could result in catastrophic damage to the pipeline system. In response to the call for sustainable development, addressing this urgent academic challenge means finding a way to safely and economically maintain a continuous water supply to the target water demand point, even under extreme accident conditions. In this paper, drawing on engineering examples, we considered air tanks with varying process parameters installed at multiple locations within a water conveyance system to prevent water hammer and ensure water supply safety. To ensure that air tanks are of high quality and cost-effective after procurement and use, a multi-objective optimization design model comprising fitting, optimization, and evaluation plates was constructed, aimed at selecting certain process parameters. In the multi-objective optimization design model, Latin hypercube sampling improved by simulated annealing (LHS-SA), stepwise regression analysis (SRA), the Multi-Objective Whale Optimization Algorithm (MOWOA), and the Multi-Criteria Decision Analysis (MCDA) method with various weight biases are used to ensure the rationality of the optimization process. By comparing the optimization results obtained using these different MCDA methods, it is evident that the results output after AHP-EWM evaluation tend to be economic indicators, whereas the results output after FN-MABAC evaluation tend to be safety indicators. In addition, according to the sensitivity analysis of weight distribution, it can be inferred that the changes in maximum transient pressure head caused by water hammer have the most significant impact on final decision-making. Full article

Solid waste management in Jordan is still following a linear model, where more than 90% of solid waste, including organic waste, is collected and disposed into landfills. Such practices are not sustainable and may lead to adverse public health and environmental impacts. Therefore, there is a pressing need to look for alternative organic waste management by adopting circular economy principles through which the adverse impacts are minimized and the benefits from the resources are maximized. The main objective of this study is to select the appropriate treatment technology for organic solid waste management in Jordan. To achieve this objective, an analytical hierarchy process was used as a decision making tool. A hierarchy model that consists of four levels was employed with 3 main criteria and 10 sub-criteria to assess 4 alternatives of organic waste treatment. Based on the experts’ opinions and the pairwise comparison, the AHP model results showed that the environmental and public health criterion is the most important. On the other hand, the most sustainable treatment option of the organic waste treatment is composting with a weight of 0.373, followed by landfilling with a weight of 0.203. Anaerobic digestion ranked third as an alternative, with a weight of 0.201, while the least-preferred treatment technology was found to be the mechanical biological treatment, with a weight of 0.193. Sensitivity analysis based on varying the main criteria weights under different scenarios showed the robustness of the AHP model, where composting continued to be the first ranked under most of the considered scenarios. Since the national solid waste management strategy is currently subject to review, the findings of the current study provide a valuable information for the decision makers in Jordan to update their strategic plans and move towards a circular economy option. Full article

Natural and man-made disasters threaten humans. Effective emergency management is essential to minimize disasters and their harmful effects. Prevention, preparation, response, and recovery are the basic phases of emergency management. Emergency assembly places are very important in emergency management during the preparation phase, as these are the first places to be reached during and after the disaster. This study aims to identify the most suitable locations for emergency assembly points, which play a critical role in sustainable disaster management. The location of emergency assembly points is influenced by many criteria. In this study, suitable locations for emergency places were investigated on the basis of criteria. The Best–Worst Method (BWM), a relatively new multi-criteria decision-making (MCDM) method that requires fewer pairwise comparisons and yet provides consistent results, is used to calculate the weights of the criteria after comparing results with the Analytical Hierarchy Process (AHP). The weighted criteria were then used to perform spatial analyses using Geographic Information Systems (GIS). In this study, a two-phase approach was used to determine suitable locations for assembly points: In the first phase, suitable areas were identified by applying raster-based analyses, and in the second phase, vector-based analyses were performed. The results of the two phases were evaluated together, and suitable locations for disaster assembly places were determined. In Atakum District, which is the study area, 41 emergency assembly places were identified, and suitable assembly places were ranked by the Preference Ranking Technique with Similarity to Ideal Solution (TOPSIS) method. Results showed that the first three highest-ranked assembly points (AP) were AP20, AP15, and AP25, while the last three lowest-ranked assembly points were AP2, AP7, and AP6. The identification of these locations will provide crucial decision support for local governments, disaster management authorities, urban planners, etc. in ensuring a more sustainable city. Full article

Sub-watershed prioritization using morphometric analysis and multi-criteria decision-making (MCDM) techniques is a systematic approach to identifying and ranking sub-watersheds based on their susceptibility to soil erosion. This helps in implementing targeted soil conservation measures. In this study, sub-watersheds in the Narangi basin are prioritized by employing morphometric analysis integrated with advanced MCDM techniques, including additive ratio assessment (ARAS), complicated proportional assessment (COPRAS), multi-objective optimization by ratio analysis (MOORA), and the technique for order preference by similarity to ideal solution (TOPSIS). Weights for various MCDM methods are determined using the criteria importance through an inter-criteria correlation approach (CRITIC: criteria importance through inter-criteria correlation method), while geospatial techniques ensure precise spatial analysis. The results provide a unified ranking of sub-watersheds, revealing that sub-watershed 3 (SW3) and SW9 are in the high-priority soil erosion category; SW1, SW2, SW5, and SW8 are medium-priority; and SW4, SW6, SW7, and SW10 are low-priority. This comprehensive and sustainability-oriented approach equips decision-makers with robust tools to identify and manage sub-watersheds at risk of soil erosion, ensuring the long-term sustainability of land and water resources. This study aligns with sustainable development goal 15 (life on land) and promotes sustainable land use practices to combat soil degradation. Full article

Background: Nowadays, traditional supply chain management (SCM) processes are undergoing a profound transformation enabled by advanced technologies derived from Industry 4.0. The rapid adoption of these technologies has led to the emergence of smart SCM, which integrates modern technologies in sourcing, production, distribution, and sales. Supplier evaluation and selection (SES) in smart SCM is a strategic decision impacting the entire supply chain. Organizations must also incorporate sustainability principles into their strategic decisions alongside smart production and efficiency. Methods: The main objective of this study is to develop a multi-criteria decision-making (MCDM) approach under uncertainty to address sustainable–smart supplier evaluation and selection problems. The approach integrates polygonal fuzzy numbers (POFNs), Z-numbers, expected interval (EI), and expected value (EV) to develop methods such as the logarithmic methodology of additive weights (LMAW) and the weighted aggregated sum product assessment (WASPAS), which are used to prioritize criteria and rank suppliers. Furthermore, novel approaches are introduced for calculating membership functions, $\mathcal{a}$-cut formulations, and the crispification process in POFNs. Results: A real case study in the home appliance industry revealed that cost reduction through smart technologies, green and smart logistics and manufacturing, and smart working environments are the most critical evaluation criteria. Suppliers three and four, excelling in these areas, were identified as top suppliers. Conclusions: The proposed approaches effectively addressed hybrid uncertainty in SES problems within smart SCM. Finally, sensitivity and comparative analysis confirmed their robustness and reliability. Full article

The management of public institutions is focused not only on providing and improving public services but also on managing the physical infrastructure that these institutions use—buildings for provision of such services. The focus of this paper is on decision support to the management of individual buildings and the set of such buildings (portfolio) during the planning phase. More precisely, it is directed towards support towards both the decision-maker (DM) and decision-making process (DMP) when planning construction activities/projects such as maintenance, renovation, reconstruction, extension, construction, design/preparation of project-technical documentation, etc. The aforementioned DMP includes the processing of a large amount of diverse data (technical, economic, social, etc.) expressed differently—numerically or descriptively, as well as in different units of measurement, simultaneously taking into account the different wishes and attitudes of stakeholders (consequently meeting their often conflicting goals and criteria). The above indicates that it is a complex and ill-defined multi-criteria problem faced by the DM/planner. On top of that, and knowing that the DM usually does not have all the necessary knowledge and skills, this paper proposes how to overcome these issues by supporting the DM within the DMP during such a planning process. The proposed concept promotes an integral (considering relevant aspects of this management problem) and inclusive (taking into account the views of relevant stakeholders) approach to managing complex construction projects and their portfolios. It is methodologically based on the logic of decision support systems and multi-criteria analysis. The multi-criteria methods used include the Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE) for the evaluation and comparison of alternatives in an integral manner, as well as the Analytic Hierarchy Process (AHP) for determining the weights of criteria and achieving an inclusive and consistent approach to relevant stakeholders (based on the goal tree approach). The concept was tested on the planning of infrastructure management at a university in the Republic of Croatia, and it was proven to be useful because it provided the DM with a basis for decision making. The usefulness of the concept was confirmed by the concordance of the plan obtained using the concept and the activities/projects actually realized. Full article

Ordered functional weighted averaging (OFWA) operators are a generalization of the well-known ordered weighted averaging (OWA) operators in which functions, instead of single values, are considered as weights. This fact offers an extra level of flexibility; for example, in multi-criteria decision-making, it can be used to aggregate available information and provide recommendations. This paper furthers the analysis of these general operators, studying how they can be combined to obtain conservative and aggressive perspectives from experts and studying the algebraic structure of the whole set of these operators. 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