Optimal Alternative Research Articles

A fuzzy logic-based risk assessment framework for the crude oil transportation supply chain

This study proposes a novel approach to increase the efficiency of crude oil transportation by integrating fuzzy logic and the TOPSIS method. The proposed technique addresses the uncertainties and imprecisions in decision-making processes to identify the optimal shipping alternatives. The study also evaluates the static and dynamic risk indicators related to the transportation of crude oil. The China-Pakistan Economic Corridor (CPEC) is found to be a promising option for crude oil shipments, reducing the risks associated with current routes. Using a multi-criteria decision-making (MCDM) methodology, the research determines China's most efficient crude oil supply channel using a fuzzy-based Improved TOPSIS approach. The economic feasibility of the CPEC option has been confirmed through a thorough cost-benefit analysis (CBA). Additionally, the optimal mode of transportation from CPEC seaports to Western China is determined using the fuzzy-based Improved TOPSIS method. The MCDM criteria considered in all scenarios include time, financial outlays, energy consumption, environmental impact, and security risks. The results indicate that the CPEC route and the maritime route via Myanmar outperform the conventional route that passes through the seaports of Eastern China. This study emphasizes the significant advancement in decision-making in crude oil transportation made possible by the fuzzy-based Improved TOPSIS technique.

Optimal Design Alternatives of Guide Signs for Expressway Segments with Consecutive Dense Exits

Optimal Design Alternatives of Guide Signs for Expressway Segments with Consecutive Dense Exits

A computationally intelligent framework for traffic engineering and congestion management in software-defined network (SDN)

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations.

Measurements of Soil Water Potential and Conductivity based on a Simple Evaporation Experiment using a Hydraulic Property Analyzer.

The measurement of soil hydraulic properties is critical in understanding the physical components of soil health as well as integrated knowledge of soil systems under various management practices. Collecting reliable data is imperative for informing decisions that affect agriculture and the environment. The simple evaporation experiment described here uses instrumentation in a laboratory setting to analyze soil samples collected in the field. The soil water tension of the sample is measured by the instrument, and tension data is modeled by software to return soil hydraulic properties. This method can be utilized to measure soil water retention and hydraulic conductivity and give insight into differences in treatments or environmental dynamics over time. Initial establishment requires a user, but data acquisition is automated with the instrument. Soil hydraulic properties are not easily measured with traditional experiments, and this protocol offers a simple and optimal alternative. Interpretation of results and options for extending the data range are discussed.

Influence of Cooking Technique on Bioaccessibility of Bioactive Compounds in Vegetable Lentil Soup.

Vegetables and legume soups contain various essential and bioactive constituents such as vitamin C, carotenoids, and phenolics. Antioxidant activity characteristics related to those compounds are well known to contribute profusely to human health. The cooking technique affects the bioavailability of nutrients and bioactive compounds, making it crucial to explore optimal alternatives to maximize them. The objective of this study was to explore the influence of different cooking techniques (boiling, pressure cooking, sous-vide, and cook-vide) on the physicochemical properties and bioactive characteristics of a ready-to-eat vegetable lentil soup. For this, the bioaccessibility of those compounds was assessed through an in vitro simulated gastrointestinal methodology. The firmness of vegetables was established to define treatments' cooking times, allowing subsequent comparison of the nutritional and functional properties of the soups. The color of vegetables was also evaluated as a quality parameter, which contributed to providing a global vision of the process impact. The results revealed that in vitro digestion (IVD) caused a decrease in all bioactive compound determinations for all cooking treatments of up to 72% for total phenols, 92% for lycopene, 98% for carotenoids, and 100% for vitamin C. Additionally, the antioxidant activity of the soups after thermal treatment improved up to 46% measured by the DPPH method. This study emphasizes the importance of considering the digestion process in the selection of the most adequate cooking technique. After IVD, traditional cooking (boiling) reached the maximum total carotenoid and lycopene contents; cook-vide and pressure-cooking techniques provided the highest total phenol content, showing these three techniques to have the maximum antioxidant capacity.

Audit and Remediation Strategies in the Presence of Evasion Capabilities

When companies or organizations can evade audits on their harmful incidents, how should the affected entities design their audit and penalty policies? In “Audit and Remediation Strategies in the Presence of Evasion Capabilities” by Wang, de Véricourt, and Sun, the authors find random audits may be needed in the optimal policy. Specifically, the optimal policy alternates between ascending monetary penalties (without any audits) and random audits at a constant rate (when the penalty reach its maximum level). Only when the evasion is ineffective or the self-correction is too costly do deterministic audits become optimal. They tackle the problem in a continuous-time principal-agent framework with both adverse selection and moral hazard.

Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA–WISP approach

Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers’ models, and the industry’s intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decision-making, IVFF–PIPRECIA and IVFF–WISP techniques. IVFF–PIPRECIA determines criteria importance weights, whereas IVFF–WISP identifies optimal alternatives. A case study in the textile industry validates the framework’s practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model’s credibility. The approach effectively handles decision-making uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.

Selection of renewable energy development path for sustainable development using a fuzzy MCDM based on cumulative prospect theory: the case of Malaysia

Malaysia's excessive energy consumption has led to the depletion of traditional energy reserves such as oil and natural gas. Although Malaysia has implemented multiple policies to achieve sustainable national energy development, the current results are unsatisfactory. As of 2022, only 2% of the country's electricity supply comes from renewable energy, which accounts for less than 30% of the energy structure. Malaysia must ensure energy security and diversified energy supply while ensuring sustainable energy development. This article uses the fuzzy multi-criteria decision-making(MCDM) method based on cumulative prospect theory to help decision-makers choose the most suitable renewable energy for sustainable development in Malaysia from four dimensions of technology, economy, society, and environment. The results show that solar power is the most suitable renewable energy for sustainable development, followed by biomass, wind, and hydropower, but the optimal alternative is sensitive to the prospect parameters. Finally, it was analyzed that efficiency, payback period, employment creation, and carbon dioxide (CO2) emissions are the most critical factors affecting the development of renewable energy in Malaysia under the four dimensions. Reasonable suggestions are proposed from policy review, green finance, public awareness, engineering education, and future energy. This research provides insightful information that can help Malaysian decision-makers scientifically formulate Sustainable development paths for renewable energy, analyze the problems encountered in the current stage of renewable energy development, and provide recommendations for Malaysia's future renewable energy transition and sustainable development.

Objective multi-criteria decision-making for optimal firefighter protective clothing size selection

Objectives. Addressing the prevalent issue of size misfits in large-scale issued firefighter uniforms due to diverse and complex body morphologies, this article presents an objective method for intelligent garment sizing matching without subjective weighting. Methods. The method employs criteria importance through inter-criteria correlation (CRITIC) and the catastrophe progression method (CPM) for more accurate and reliable sizing. Traditional methods, reliant on limited indicators such as height and chest girth and often subjectively determined by experts, are prone to reliability concerns. Matching decisions made based on our approach are evidence-based, transparent and reproducible, thus minimizing subjectivity and expert intervention. Results. A case study of 388 cases validates the method’s efficacy in providing garment size recommendations, surpassing traditional experience-based approaches by reducing subjective bias. Conclusion. Despite some differences, the optimal alternatives for examinees are almost consistent across the different methods. Compared with traditional subjective weighting methods, this method has potential advantages in situations such as large-scale matching of firefighter protective clothing where individual customization or direct try-on is not feasible.

A multiple criteria decision analysis approach for assessing the quality of hardwood species used by Greek timber industries

Abstract Timber industries make an essential contribution to economies worldwide, while the sustainable supply of timber generates revenue, supports employment, and contributes to economic activity. The strategic choice of wood species using specific criteria can have substantial economic outcomes for the timber industry in Greece. This study assessed the suitability of hardwood species most commonly used by Greek timber industries. The assessment was conducted with the use of a Multiple-Criteria Decision Analysis approach, taking into consideration specific criteria that affect the quality of timber. According to the findings, walnut was the optimal alternative that outranked the other examined species. Chestnut, oak, beech, ash, and hornbeam also achieved positive scores, and therefore, they are also acceptable alternatives as broadleaved species suitable for furniture manufacturing and sawn timber production. Greek timber industries can enhance their products’ value and market appeal by focusing on species that meet high-quality standards and consumer preferences.

Numerical Analysis of Structural Vibrations in Masts—A Practical Study Applied to a 28-Meter Tugboat

This article investigates the vibration analysis of the mast on the bridge top of a 28 m tugboat, in the quasi-static regime. The study aims to determine the natural frequencies using an equivalent dynamic model of the mast and considers the accelerations of different degrees of freedom of the vessel. Practical design and reinforcement strategies for the local bridge top structure are proposed, utilizing numerical and analytical tools. The investigation explores various alternatives to optimize the structural arrangement. By analyzing the modal behavior, insights are provided for efficient design and reinforcement. The results contribute to understanding the mast’s vibration characteristics, offering guidance for similar structures. This study employs numerical and analytical methods, enhancing the optimization of the mast’s behavior. It determines natural frequencies, proposes practical design strategies, and explores alternatives for structural optimization. The findings provide insights for shipbuilding and mast design applications in other vessels and industries such as radar mast, compass deck, and guyed mast.

An Improved Laplacian Gravity Centrality-Based Consensus Method for Social Network Group Decision-Making with Incomplete ELICIT Information

With the advancement of information technology, social media has become increasingly prevalent. The complex networks of social relationships among decision-makers (DMs) have given rise to the problem of social network group decision-making (SNGDM), which has garnered considerable attention in recent years. However, most existing consensus-reaching methods in SNGDM only consider local network information when determining the influence of DMs within the social network. This approach fails to adequately reflect the crucial role of key DMs in regulating information propagation during the consensus-reaching process. Additionally, the partial absence of linguistic evaluations in the decision-making problems also poses obstacles to identifying the optimal alternative. Therefore, this paper proposes an improved Laplacian gravity centrality-based consensus method that can effectively handle incomplete decision information in social network environments. First, the extended comparative linguistic expressions with symbolic translation (ELICIT) are utilized to describe DMs’ linguistic evaluations and construct the incomplete decision matrix. Second, the improved Laplacian gravity centrality (ILGC) is proposed to quantify the influence of DMs in the social network by considering local and global topological structures. Based on the ILGC measure, we develop a trust-driven consensus-reaching model to enhance group consensus, which can better simulate opinion interactions in real-world situations. Lastly, we apply the proposed method to a smart city evaluation problem. The results show that our method can more reasonably handle incomplete linguistic evaluations, more comprehensively capture the influence of DMs, and more effectively improve group consensus.

A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

AbstractThis study is aimed at identifying key indicators to increase knowledge-based process optimization for renewable energy projects. Within this context, a novel fuzzy decision-making model is introduced that has two different stages. The first stage is related to the weighting of the knowledge-based determinants of process optimization in investment decisions by using quantum picture fuzzy rough sets (QPFR)-based multi-step wise weight assessment ratio analysis (M-SWARA). On the other side, the second stage consists of ranking the investment alternatives for process optimization in renewable energy projects via the QPFR-based technique for order preference by similarity (TOPSIS) methodology. The main contribution of this study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. This situation provides an opportunity for the investments to implement appropriate strategies to increase the optimization of these investments. It is concluded that quality is the most essential indicator with respect to the process optimization of these projects. It can be possible to increase the efficiency of these projects by using better quality products. Innovation has an important role in ensuring the use of quality products in environmental sustainability. Owing to new technologies, it is easier to use more effective and innovative products. This condition also contributes to increasing the efficiency of the energy production process. Furthermore, the findings also denote that the most appropriate energy innovation alternative is the variety of clean energy sources. By focusing on different clean energy alternatives, the risk of interruptions in energy generation can be minimized. In other words, the negative impact of climatic conditions on energy production can be lowered significantly with the help of this situation.

Sistem Pemilihan Bakal Calon Legislatif oleh Partai Politik dengan Metode Simple Additive Weighting

The Regional Leadership Council of the Grinding Party experienced difficulties during the general election in 2019 where legislative candidates could not meet the party's target of 2 seats. So that in 2024 before the general election a decision support system-based legislative candidate selection system was created so that for future plans the party will use this system so it is necessary to start building the system. The purpose of this research is to be able to determine prospective candidates to become legislative candidates. This type of research is quantitative in criteria data collection techniques can also be through observation, interviews and literature studies, with an application development model using a prototype model. The prototype model with the stages of collecting data and case examples, designing prototypes, building prototypes, testing programmes, implementing and maintaining programmes. The research subject is legislative candidates and the object is the Gerinda party which conducts the selection of legislative candidates. Application design assisted by Unified Modelling Language (UML) modelling techniques can provide the best system design, then the calculation results will be ranked to get the optimal alternative. Then the data analysis technique uses the Simple Additive Weighting (SAW) method. This research produces the name of the candidate who will be elected by building a system with the application of the SAW method. Of the 9 people who registered as prospective candidates.

Determining the most useful renewable energy alternative for Turkey by combining MAUT and TAOV methods

This study focuses on various Multi-Criteria Decision Making (MCDM) methods developed in the academic literature to facilitate the decision-making process. These methods are primarily aimed at establishing a ranking of alternatives based on specific criteria, with the ultimate goal of determining the most optimal alternative. In this work, we propose a novel integrated MCDM approach that combines the Total Area Based on Orthogonal Vectors (TAOV) method with the Multi-Attribute Utility Theory (MAUT) method. Within this framework, criteria weights are determined using the MAUT method, while the TAOV method is employed to ascertain the ranking of alternatives. The primary objective of this approach is to identify the most suitable renewable energy resource for the context of Turkey. Based on a comprehensive review of relevant literature regarding the evaluation of renewable energy sources in Turkey, a set of critical criteria influencing the decision-making process is delineated. These criteria encompass a range of factors including efficiency, construction duration, cost, government incentives, economic lifespan, external dependence, employment opportunities, social acceptance, spatial requirements, and environmental considerations such as greenhouse gas emissions. The outcomes of this study reveal that, according to the proposed approach, the most beneficial renewable energy resources for Turkey are ranked as follows: hydroelectric, wind, solar, biomass, and geothermal energy sources, respectively.

Healthcare resource utilization patterns in psoriasis patients using biologic and conventional treatments in Finland.

Psoriasis vulgaris is associated with a significant healthcare burden, which increases over time as the disease progresses. The aim of this retrospective, population-based registry study was to characterize healthcare resource utilization (HCRU) in patients with psoriasis using biologics and oral immunosuppressants (conventionals) in Finland. The study cohort included all patients with a diagnosis of psoriasis vulgaris in the secondary healthcare setting between 2012-2018, who initiated a biologic (n=1,297) or conventional (n=4,753) treatment between 2013-2017. Data on primary and secondary HCRU were collected from nationwide healthcare registries. The results indicated a remarkable decrease in contacts with a dermatologist after the treatment initiation among patients starting biologic (mean annual number of contacts 5.4 per person before and 2.3 after the initiation), but not conventional (3.3 and 3.2) treatment. For conventional starters there was a high level of contacts with a dermatologist surrounding times of treatment switching, which was not observed for biologic starters. Overall, primary and other secondary care contacts did not decrease after the initiation or switch of treatment. The results highlight the importance of thorough consideration of the most optimal treatment alternatives, considering the overall disease burden to patients and healthcare systems.

DfMA Integrated Assessment Model for Selecting Optimal Design Alternatives in OSC Projects

To select the optimal design alternative in off-site construction (OSC) projects, the building industry has turned to design for manufacturing and assembly (DfMA). However, most DfMA developments in the OSC field until now have been on improving the production process in OSC projects and guideline strategies on how to apply them. The application of DfMA guidelines only provides background knowledge to designers on how to design. However, it cannot inspect whether the DfMA concept is fully reflected in a design draft to examine the suitability to the OSC production environment, and it cannot determine the optimal alternative from among multiple design alternatives. Thus, this study developed an integrated assessment model of OSC-DfMA consisting of the OSC-DfMA production suitability assessment model and the OSC-DfMA production efficiency assessment model to support decision-making for selecting the optimal design alternative of an OSC project. In this study, the scope of the main research was limited to precast concrete (PC)-based OSC projects. Firstly, we developed an OSC-DfMA production suitability assessment model to review whether design drafts are suitable in the OSC production environment by applying checklist and matrix techniques. Secondly, we developed an OSC-DfMA production efficiency assessment model to select an optimal alternative in terms of production efficiency among multiple design drafts. Thirdly, we conducted a case study to validate the usefulness of the OSC-DfMA assessment model developed in this study. Finally, we discuss the possibility of using AI technology to consider the facility capacity and resource constraints during the production of OSC building components. The study results are of practical value in providing the basis for expanding the applicability of DfMA by proposing a DfMA assessment model suitable for OSC contexts.

Comparison and Selection of Wet Waste Disposal Modes for Villages in Agriculture-Related Towns Taking Shanghai, China, as an Example

Under the increasingly stringent regulatory constraints, wet waste disposal in agriculture-related towns has become a significant issue. Based on fieldwork in three agriculture-related towns and nine villages located in Shanghai, this paper analyzes the economics and greenhouse gas emissions of three wet waste disposal methods, including centralized disposal, on-site disposal by biogas digesters, and on-site disposal by small-scale biochemical processors, and compares these alternatives by combining various factors, aiming to help decision makers in selecting the optimal alternative. The net present value approach was employed to evaluate the economic aspects of the three disposal methods. The greenhouse gas emissions were calculated using life cycle assessment, life cycle inventory, and the IPCC guidelines. The fuzzy analytic hierarchy process method was employed for a multi-criteria decision-making analysis based on five factors, including economics, greenhouse gas emission control, stability, compliance with environmental regulations, and location feasibility. The results revealed that although centralized disposal is not as good as on-site disposal by biogas digesters in terms of economics and greenhouse gas emissions, centralized disposal is still the optimal alternative according to the results of multi-criteria decision making. The tightening of regulatory constraints will internalize the negative externalities of on-site wet waste disposal. Thus, centralized disposal is the method of wet waste disposal that is most consistent with the regulatory constraints and most sustainable. It is worth emphasizing that policymakers should fully assess the impact of tightening regulations on the selection of wet waste disposal methods, establish a homogenous waste disposal system covering both urban and rural areas, and enhance the management of existing on-site disposal facilities.

Human Behavior in Suboptimal Choice Tasks: Defining Optimality.

Suboptimal choice behavior, or behavior that leads to a loss of resources over time, has been observed in a laboratory setting from multiple species. A procedure commonly used to capture this effect involves presenting two alternatives during choice trials, one of which is optimal whereas the other is suboptimal. The optimal alternative yields reinforcement more often than the suboptimal alternative, but often does not produce signals that indicate whether reinforcement will occur. The suboptimal alternative produces less reinforcement than the optimal alternative but may include reinforcement-predictive stimuli that indicate to the organism whether reinforcement will occur. This procedural framework has consistently produced a preference for the suboptimal alternative in pigeons and, to a lesser extent, rats. However, human participants have demonstrated preference for the optimal alternative. Following a review of past suboptimal choice research, we applied the reinforcement-derived definition of optimality to two sets of our previously published human data. We found that under multiple conditions, human choice behavior was consistent with what was predicted by the proportion of obtained reinforcement, thus supporting that the behavior was optimal. However, we found that participants in two conditions chose the suboptimal alternative more than expected. This finding could be considered as a demonstration of suboptimal choice in humans. We propose that comparing choice behavior to what past obtained reinforcement outcomes would predict might be a more accurate view of whether patterns of choice are within the parameters of this task.

Tropical Shrimp Biofloc Aquaculture within Greenhouses in the Mediterranean: Preconditions, Perspectives, and a Prototype Description

Biofloc technology (BFT) offers an innovative eco-friendly approach that is particularly applicable in shrimp farming. Penaeus vannamei is the most important seafood species in terms of global economic value. Nevertheless, its increasing global demand highlights the necessity for sustainable production of P. vannamei shrimps outside their native range, assuring the avoidance of genetic pollution risk. Towards this direction, the present study focuses on the feasibility of tropical shrimp species aquaculture in indoor systems evaluating BFT application in temperate zones. The achievability of P. vannamei cultivation inside greenhouses in temperate latitudes is thoroughly examined and a representative experimental biofloc setup for P. vannamei within a greenhouse in Northern Greece is demonstrated. Nevertheless, there are two major limitations, related to economy and ecology, namely the energy demand for high seawater temperature and the fact that most reared shrimps are non-indigenous species setting risk for genetic pollution, respectively. To overcome the former, energy-saving measures such as tank and greenhouse insulation in combination with a microclimate chamber construction were implemented to optimize water temperature at minimal cost. Concerning the latter, there is clear evidence that P. vannamei populations cannot be established in the Mediterranean, setting aside any environmental risk. Overall, based on the developed and tested pilot prototype, employment of optimal management practices, innovative manufacturing and clean energy alternatives, and the utilization of ecosystem services could reduce the environmental impact and maximize the profitability of biofloc operations. These actions could probably permit sustainable and economically viable farming of P. vannamei employing BFT within greenhouses in the Mediterranean.