Technique For Order Preference By Similarity To Ideal Solution Research Articles

Holistic approach for selecting chatbot development tools: combining AHP and TOPSIS methodologies

ABSTRACT The proliferation of chatbot development tools presents a significant challenge in identifying the most suitable ones for specific needs. This study addresses this issue by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to create a comprehensive methodology for selecting chatbot development tools. The study aims to provide a transparent, objective decision-making framework by evaluating tools across multiple dimensions including deployment characteristics, input processing capabilities, dialogue management, response generation, and economic considerations. Utilizing AHP for criteria weighting and TOPSIS for ranking alternatives, the methodology systematically evaluates various tools to identify the optimal choice. Key findings highlight the strengths and weaknesses of different tools, offering insights that guide developers and organizations in choosing the most suitable chatbot development tool aligned with their specific needs and strategic goals. The implications of this methodology are significant, enhancing decision-making processes within technological tool selection and contributing to the fields of artificial intelligence and automated systems development.

A novel energy-efficient and cost-effective task offloading approach for UAV-enabled MEC with LEO enhancement in Internet of Remote Things networks

The Internet of Remote Things (IoRT) involves networks of devices deployed in extensive and often remote areas, collecting data for transmission and processing. In such networks, Unmanned Aerial Vehicles (UAVs) gather data, which is then sent to Low Earth Orbit (LEO) satellites for processing. These systems often face significant challenges, particularly in task offloading. Conventional methods typically rely on static routing and scheduling algorithms that do not adapt to changing conditions and usually overlook the complexity of dynamic decision-making in harsh or isolated environments, thus failing to address the critical challenges of energy efficiency and latency. In this paper, we introduce a method comprised of a three-layer architecture. The first layer, the IoRT computing layer, uses Deep Q-Network (DQN) to optimize local decisions based on device constraints and task urgency. The second layer features UAVs serve as Mobile Edge Computing (MEC), which not only processes data but also decides whether to process tasks locally or offload them to LEO satellites, utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for this decision-making process. The third LEO Satellite Layer has a high computational capacity to handle offloaded tasks. Simulation results demonstrate notable improvements: compared to another method, the proposed model shows a 14.73 % reduction in energy consumption and a 23.13 % decrease in latency while reducing execution costs by an average of 28.7 %.

Performance Assessment of Public Hospitals with the Entropy-Weighted TOPSIS Method: The Case of Turkey

Background: Hospitals make up the main cost factor of health systems and also face increasing pressure to improve efficiency. Improving the performance of hospitals, which are vital for public health, is very important for developing countries such as Turkey. Aims: We conducted this study to assess the performance of 544 secondary-level public hospitals in Turkey by geographical region. Methods: In the study, we used the entropy weighting method to obtain the objective weights of indicators with the evaluation criteria, and then we utilized the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to rank the performance of hospitals by geographical region. Results: The comparison of hospital performances by geographical region indicated that the highest and the lowest mean scores belonged to the Mediterranean Region and the Eastern Anatolia Region, respectively. Conclusions: The socio-economic differences between geographical regions in Turkey were in parallel with the findings of the study. Although there is a need for professional management in resource allocation and use to improve the performance of public hospitals, it is necessary to focus on the origin of the problems first.

Identification of important nodes in multi-layer hypergraphs based on fuzzy gravity model and node centrality distribution characteristics

Hyperedge is a common structure that represents high-order interactions between nodes in complex networks, and multi-layer networks provide more diverse node interactions than single-layer networks. Therefore, multi-layer hypergraphs can more clearly represent the relationships between nodes. However, there are few studies on identifying important nodes in this framework. This paper proposes a method called HCT to fill this gap. It consists of three parts, namely: Hypergraph Fuzzy Gravity Model (HFGM), Layer Weight Calculation Method based on Node Centrality Distribution Characteristics (CDLW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). HCT progressively analyzes the importance of a node from three levels: local (the node itself), semi-local (the hyperedges and community to which the node belongs), and global (the layer to which the node belongs). By combining these three results, the global centrality of a node in the entire network can be calculated. Simulation experiments demonstrate that the important nodes identified by HCT exhibit stronger contagion capabilities in nine networks compared to nine combinatorial methods and removing these nodes will seriously damage the connectivity and robustness of the network. The centrality of each node calculated by HCT is also consistent with its actual importance.

Assessing Heterogeneity Among Cyclists Towards Importance of Bicycle Infrastructural Elements in Urban Areas

Promoting bicycling and making it attractive requires appropriate infrastructure. Sociodemographic characteristics, frequency and experiences of bike use, and purpose of bicycle trips can affect preferences towards bicycle infrastructure facilities in urban areas. Hence, this study aims to explore the heterogeneity in the perceived importance of bicycle infrastructure facility attributes in various cyclist groups based on gender, age, weekly biking frequency, daily cycling distance, cycling experience, and bicycle trip purpose. Data were collected from bicycle users through a questionnaire disseminated via social media platforms and QR code brochures distributed in Hasselt, Belgium. A 5-point Likert-type ordinal scale was used to collect data on the perceived importance of bicycle infrastructure facility indicators. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the indicators. At the same time, Mann–Whitney U and Kruskal–Wallis tests were utilized to verify the heterogeneity among the groups. The findings reveal that bicycle infrastructure, i.e., bicycle lanes or paths, is the most critical variable, while the slope was considered the least important. No heterogeneity was found regarding the importance of bicycle infrastructure indicators based on gender. However, heterogeneity was observed based on age, daily bicycle use, cycling experience, weekly bicycle use, and bicycle trip purpose. The findings of this research help urban and transport planners develop improvement strategies for the city’s existing bicycling facilities and prioritize future developments by considering various cyclist groups’ preferences.

A novel framework for production planning and class-based storage location assignment: Multi-criteria classification approach

Efficient warehouse management is essential for optimizing inventory, minimizing transportation costs, and enhancing overall performance. This research introduces a novel Mixed-Integer Nonlinear Programming (MINLP) model to address the Storage Location Assignment Problem (SLAP) in warehouse management. Integrating multi-criteria decision-making with strategic production planning, our model advances warehouse operations by allocating storage locations to products strategically, focusing on reducing transportation distances and maximizing storage efficiency. The distinctive innovation of this study is the nuanced application of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) results to strategic storage location assignments, enhancing the model's capability to consider a comprehensive evaluation of inventory attributes, including physical characteristics and perishability. This approach evolves TOPSIS's application in warehouse management, enabling it to consider both physical characteristics and perishability of products. The outcomes of TOPSIS, including product classifications and preferences, serve as vital inputs to the mathematical model, facilitating a comprehensive evaluation of storage locations that encompasses spatial, demand-related, and physical aspects of inventory. Additionally, our research introduces a versatile decision support system, adaptable to various operational requirements. This system enhances practical decision-making in warehouse management, accommodating scenarios based on single or multiple criteria, including the cube-per-order index (COI). The research results highlight the significant impact of this innovative approach in enhancing warehouse management. By addressing the complexities of storage location assignment and integrating multiple criteria, we achieve more efficient and cost-effective warehouse operations. The approach has been shown to be adaptable and practical, making it a valuable contribution to the field of logistics and warehouse management.

Recommendation method for selecting the rice seeds based on group decision support system

<p>In this paper, we provide group recommendations based on each decision makers (DMs) in choosing the best type of rice for replanting. This group decision support system (GDSS) aims to guide stakeholders who have a role in selecting rice types. In this method, we propose using technique for order preference by similarity to ideal solution (TOPSIS) to rank each DM, Borda to rank in groups, and then test it using Spearman's rank correlation to measure the relationship between system results and the method applied. The results of this study show that DM<sub>1</sub> ranks highest in selecting Gelagai rice seeds with a preference of 0.7786. Then DM<sub>2</sub> ranked highest with Ekor Payau rice seeds in preference 0.6529. Meanwhile, DM<sub>3</sub> ranked highest in Gelagai rice seeds with a selection of 0.7728. The final group voting system uses Borda, where Gelagai rice seeds occupy the highest rank with the most accumulated votes from each DMs. The best option or the highest rating based on the assessment of the three DMs, DM<sub>1</sub> as a farmer is the first rank A10 Gelagai with a score of Borda 26 in the decision group selection of superior rice seeds.</p>

Optimal scheduling of green heating systems in buildings considering economy and exergy efficiency

Green heating in buildings is of great significance to global energy conservation and emission reduction, and the design of optimal scheduling models is the key to achieve cost reduction and efficiency improvement in heating systems. For the building heating system containing multiple heterogeneous energy sources such as solar energy, air energy and electricity, a multi-objective optimal scheduling model is established with the goal of maximizing the exergy efficiency and minimizing the economic cost of the system, taking into account the quality difference of energy in the conversion process. Regarding the nonconvex and nonlinear constraints in the scheduling model, the incremental linearization method and Big-M method are used to linearize the transformation, and obtain the multi-objective mixed-integer linear programming model. Further, the ε-constraint method is used to solve the Pareto frontier of multi-objective optimization and the technique for order preference by similarity to ideal solution (TOPSIS) method is used to make decision. Finally, the simulation study of the model is carried out, and the results show that the proposed multi-objective optimal scheduling can balance the system operation economy and exergy efficiency compared with the belief rule base and evidential reasoning (BRB-ER) method, and the system’s daily economic cost are reduced by 9.66 %, 9.94 % and 17.24 %, respectively, and the system’s average daily coefficient of performance (COP) are improved by 4.66 %, 14.05 % and 13.22 %, respectively, on three typical days, it is verified that the proposed optimal scheduling model has better economy and exergy efficiency. The proposed multi-objective optimal scheduling model introduces exergy efficiency rather than energy efficiency as one of the optimization objectives of the heating system and reasonably solves the optimization model, which can provide a new idea and reference for the multi-objective optimal scheduling of the green heating system in buildings.

A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification

Microarray technology produces data with a large number of dimensions. The abundance of dimensions in the data makes it challenging for machine learning algorithms to extract meaningful information from it. To overcome this limitation, feature selection (FS) techniques can be applied to reduce the dimensionality of the data. FS is a crucial preprocessing step that allows for the handling of high-dimensional data and facilitates more effective and efficient processing. The primary objective of this paper is to develop an efficient FS method that can effectively reduce the dimensionality of microarray data. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized in the first filtering stage to extract informative features. The best feature subset is then determined by using a Particle Swarm Optimization (PSO) algorithm as a wrapper feature selector to identify ideal features. The proposed approach is evaluated using microarray datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared with other algorithms. The experimental findings indicate that the proposed approach outperforms the benchmark methods in terms of classification accuracy.

COMPARATIVE ANALYSIS OF MULTI-CRITERIA DECISION MAKING METHODS IN DETERMINING REDD+ PROJECT LOCATION

The Reducing Emissions from Deforestation and Forest Degradation (REDD+) mechanism is aimed at reducing global greenhouse gas (GHG) emissions. This study aims to develop a multi-criteria decision-making (MCDM) model specifically designed to prioritize locations for REDD + projects. The proposed research design focuses on developing a MCDM framework for determining the priority locations for projects using criteria such as climate impact reduction, contributions to local communities, and biodiversity conservation. The study utilized the Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to determine the prioritization of alternatives based on compromise solutions. The success of this research demonstrates that a systematic approach to determining priority locations can be effectively carried out using MCIM. This research is expected to aid policymakers and stakeholders in making more informed and effective decisions for environmental conservation and climate change mitigation.

Multi-objective optimization of L-shaped fins in rectangular phase change energy storage units based on genetic algorithm

Phase change energy storage technology holds immense potential in the field of energy storage, and enhancing the efficiency of energy storage systems has long been a focal point of industry attention. This study aims to optimize the design of an L-shaped fin structure to improve the melting rate of the phase change material (PCM) within a rectangular phase change energy storage unit and enhance the overall system performance. Through the utilization of numerical simulations and the response surface method (RSM), the influence of fin design parameters - specifically, the length and thickness of the main segment and the length and thickness of the branch segment - on the energy storage per unit mass (Em) and energy storage efficiency (Pt) of the energy storage unit is evaluated. The results reveal that the length of both the main and branch segments of the L-shaped fin significantly impacts the melting rate of the PCM, whereas the thickness of these segments has a comparatively lesser effect. Additionally, when the length of the main segment reaches a sufficient value, increasing the length of the branch segment effectively addresses the challenge of PCM melting difficulties at the bottom of the rectangular phase change cavity caused by natural convection. The Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is employed for the multi-objective optimization of the L-shaped fin shape, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with entropy weighting is applied for decision-making to determine the optimal solution. Notably, when the weights of Em and Pt are set at 44.9 % and 55.1 % respectively, the overall performance of the energy storage unit is optimized. The Pareto-optimal decision solution exhibits a significant improvement of nearly threefold in Pt compared to the case without fins, while the reduction in Em is only 5.24 %. This design approach presents a novel perspective for determining fin parameters in phase change energy storage devices and is expected to drive advancements in phase change energy storage technology.

Fuzzy Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution: A Hybrid Method for Assessing Vegetation Management Strategies under Electricity Distribution Lines to Prevent Deforestation Based on Ecosystem Service Criteria

This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, ten VM strategies were assessed based on 15 ES criteria. The FAHP results identified biodiversity, timber resources, and erosion control as the most crucial criteria due to their significant weights. The TOPSIS analysis determined that VM6 (creation and restoration of scrub edges) was the most effective strategy, achieving a value of 0.744 for reducing deforestation and enhancing energy security. VM6 helps preserve forest cover and protect infrastructure by creating a “V”-shaped structures within the EDLs corridor. This study underscores the importance of ES-oriented VM strategies for sustainable vegetation management and deforestation mitigation. It also highlights the need for incorporating scientific, ES-based decision support mechanisms into VM strategy development. Future research should expand stakeholder perspectives and conduct a comprehensive assessment of ESs to ensure that VM strategies align with ecological and socio-economic sustainability. This study provides a framework for improving VM practices and offers directions for future sustainable energy management research. This study focuses exclusively on ecological criteria for evaluating VM strategies, neglecting other dimensions. Future research should use methods like ANP and fuzzy cognitive maps to explore inter-dimension relationships and their strengths. Additionally, employing SWARA, PIPRECIA, ELECTRE, and PROMETHEE for ranking VM strategies is recommended.

Experimental investigation of machine vibration rate and machine sound level in MQL turning of AISI 1525 steel employing mango oil as lubricant

AbstractThe detrimental effects of mineral oil on the environment and machine shops have led to a surge in the usage of vegetable oil as cutting fluid. The most popular vegetable oil lubricants are edible, and they have a lot of potential to rival human consumption eventually. The study examined using mango oil, an inedible vegetable oil, as a lubricant during AISI 1525 steel turning with tungsten inserts. The best machining parameters were ultimately found using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) after the experimental studies were analyzed using the Taguchi process. Surface and contour plots were employed to investigate how different cutting settings affected the rate of vibration and sound level of the machine. Mango kernel seed oil outperformed its mineral oil counterparts by 2.3% and 57.7%, respectively, in terms of machine vibration rate and machine sound level. Moreover, feed rate (0.10 mm/rev), depth of cut (0.75 mm), and spindle speed (350 rev/min) are the ideal cutting settings to reduce machine vibrations and sound intensity. Mango oil holds significant potential as a substitute for cutting fluid derived from petroleum. The significance of this research is to formulate lubricants for industrial use that are more ecologically friendly and sustainable.

Wear behaviour analysis of thermo-mechanically processed AA7075 and AA7075/SiC/Graphite composite

The current work focuses on investigating the tribological performance of AA7075 alloy and AA7075 hybrid composite with SiC and graphite reinforcements of 2 and 0.5 wt percentages, respectively. The alloy and hybrid composite are fabricated through stir casting and thermo-mechanically processed through hot rolling routes. Dry sliding wear characterization has been carried out using a pin-on-disc wear testing setup for alloy and composite under various processing conditions using experiments designed according to Taguchi's L9 orthogonal array. The technique for order preference by similarity to ideal solution (TOPSIS) method has been adopted to achieve a single multi-response index (MRI) considering the minimization of multiple objective functions for specific wear rate (SWR) and coefficient of friction (COF). The analysis of variance (ANOVA) results of the MRI indicated that sample processing condition and load were the most significant parameters. The wear mechanism at different experimental conditions was studied using Scanning electron microscope (SEM) micrographs, energy dispersive spectroscopy analysis and 3D surface profile of wear track. The AA7075 composites are found to be comparatively good in all experimental conditions compared to the base alloy due to load-bearing SiC particles and solid lubricant graphite as reinforcing elements. The mechanically mixed layer (MML) formation in hybrid composites helped achieve good tribological properties. The AA7075 alloy and hybrid composites cross rolled at 350 °C yielded better results than other samples. The wear mechanism was abrasive and adhesive at low temperature and low load conditions in both alloy and hybrid composites. Whereas, at higher temperatures and high loads oxidative wear mechanism with delamination was prevalent. The transition of SWR from mild to severe value occurs at 250 °C in all experimental cases. The specific wear rate was improved by 85.64 % between worst and best conditions, i.e., annealed alloy and cross rolled composite.

Evaluation of Spare Parts Support Capacity of Civil Aircrafts Based on Type-2 Hesitant Pythagorean Fuzzy Sets and Improved Technique for Order Preference by Similarity to Ideal Solution

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant Pythagorean fuzzy sets and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, the basic definitions of Type-2 hesitant Pythagorean fuzzy sets were given, which were further promoted to Type-n hesitant Pythagorean fuzzy sets, and the basic order relation criterion of Type-2 hesitant Pythagorean fuzzy sets was introduced. Second, a complete evaluation system for spare parts supply support capacity was established with the spare parts of civil aircrafts as the study objects, and each evaluation indicator was introduced in detail. Then, the spare parts support solutions were preferentially sorted using the correlation coefficient formula of Type-2 hesitant Pythagorean fuzzy sets and improved TOPSIS. Finally, the reliability and reasonability of the proposed method were verified through an example calculation and comparative analysis. The experimental results indicate that the proposed method can acquire the evaluation results of spare parts support capacity more scientifically and can be referenced by relevant studies.

Emotional design and evaluation of children’s furniture based on AHP-TOPSIS

Children’s physical and mental health development is highly related to furniture and integrating the emotional design concept into children’s furniture design helps cultivate children’s personalities and promote healthy growth. The model framework of children’s furniture emotional design was constructed from the instinctive level, behavioral level, and reflective level. The comprehensive weight of the design factors was calculated using the Analytic Hierarchy Process (AHP), and three schemes were designed according to the priority of the design factors. The evaluation matrix was constructed by combining the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the distances of each scheme from the positive and negative ideal solutions were calculated, and the optimal scheme was selected according to the relative proximity ranking of the three schemes. The experimental results verified the importance of the reflective level in the three levels of the emotional design theory and emphasized the key role of emotional design in children’s furniture. Therefore, the evaluation system based on AHP and TOPSIS is feasible, which can effectively reduce the influence of subjective factors, make the design decision more scientific, and provide a new reference approach for furniture design.

Measuring heterogeneity in pedestrians’ perceived importance towards crossing facilities: An experience in Roorkee

Although several studies in the past discussed the effects of socio-demographic and trip characteristics on users’ preferences towards crossing facilities in urban areas, they were mostly confined to metro cities or million-plus cities. Adequate investigation has not been performed for the same in the small-sized cities where pedestrian activities are expected to be more frequent because of the higher share of shorter and walkable trip lengths. Accordingly, the present study aimed to investigate the heterogeneity in the perceived importance of crossing facility attributes among different pedestrian groups based on gender, age group, and walking preference. The study was conducted in a small-sized city, Roorkee, in the state of Uttarakhand, India. A face-to-face questionnaire survey of 554 pedestrians was conducted to collect their perceived importance towards 12 crossing facility attributes under study using a 5-point Likert-type ordinal scale. Collected data was analyzed using the non-parametric tests to measure the heterogeneity in the perceived importance of crossing facility attributes among aforementioned pedestrian groups. Subsequently, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to derive relative importance scores of attributes found statistically significant in heterogeneity tests for different pedestrian groups. The findings of the present study reveal that perceived importance towards crossing facility attributes is heterogeneous based on the pedestrians’ age group and walking preferences; however, it remains unaffected by gender. These findings would help policymakers, planners, and government authorities frame improvement strategies for the existing crossing facilities in the city.

Applying fuzzy logic to balanced scorecard for the performance evaluation of government e-services

Purpose The purpose of this study is to assess the performance of United Arab Emirates (UAE) Government e-services by using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methodology, integrating insights from the balanced scorecard (BSC) framework across financial, customer, internal business and learning and growth perspectives. Design/methodology/approach Using the fuzzy TOPSIS method, this paper evaluate three e-services in the UAE against 12 criteria representing the balanced scorecard perspectives. Expert evaluation and sensitivity analysis are used to identify the most sustainable e-service alternative. Findings The study findings emphasize the importance of prioritizing customer-centric metrics, improving service reliability and efficiency, and investing in employee training to enhance e-government service performance in the UAE. Sensitivity analysis reinforces the robustness of our results and identifies key criteria influencing decision-making. Research limitations/implications The data was collected only from experts in selected UAE Government departments. This may affect the generalization of the findings. Also, only three e-services were evaluated. Future studies could include various e-services not covered in this study and use different multi-criteria decision-making methods. Practical implications Prioritizing customer satisfaction: Priority should be given to customer satisfaction as it is a critical factor in evaluating e-services because of its importance. It also highlights the importance of considering user feedback and ensuring that e-services have a high level of friendliness and responsiveness to their needs. It follows that minimizing errors and ensuring quick and efficient transactions are crucial. Emphasizing reliability and transaction efficiency: Reliable services and transaction efficiency are also essential criteria for evaluating e-government services. This suggests that e-government services should be designed to minimize errors and ensure that transactions are completed quickly and efficiently. Managing IT costs: To deliver e-government services affordably, effective IT cost management is crucial. This emphasizes how crucial it is to effectively manage IT costs to guarantee the efficient delivery of e-government services. Social implications From a customer perspective, adopting BSC can create a favorable customer attitude, encourage long-term customer support, and increase customer satisfaction and loyalty. These factors have significant social implications for UAE and expatriate individuals who are using such e-government services. Originality/value This study contributes to the literature by showcasing the applicability of the fuzzy TOPSIS methodology in evaluating UAE e-government service performance. By examining multiple perspectives of the BSC, this paper provide valuable insights into enhancing the efficiency and effectiveness of e-services in the UAE Government sector.

Study on the optimal water−biochar management mode based on pan evaporation for solar greenhouse tomato (Solanum lycopersicum L.) in Northeast China

Biochar is commonly employed to enhance soil structure. However, the soil structure degradation due to multi-year continuous cropping poses a significant obstacle to the advancement of solar greenhouse agriculture. This study aimed to optimize the water−biochar management mode for seasonal solar greenhouse tomatoes (Solanum lycopersicum L.), which have been continuously cultivated over three years, with irrigation guided by the evaporation of a 20 cm evaporation pan (Ep-20). The experimental design employed a split–plot arrangement with two factors. The primary factor, irrigation amount, was tested at three levels: 0.6 Ep-20 (W1), 0.8 Ep-20 (W2), and 1.0 Ep-20 (W3). The secondary factor, biochar amount, was tested at three levels: 0 (B0), 20 t ha−1 (B1), and 40 t ha−1 (B2). The results revealed that biochar reduced soil bulk density and enhanced field capacity, tomato plant height, and photosynthesis. Increased irrigation improved tomato appearance quality but reduced nutritional quality, whereas higher biochar application improved both aspects. Principal component analysis (PCA) and grey relational analysis (GRA) results identified W2B2 as the optimal mode for the quality of spring tomato and autumn tomato. Compared to the control (W3B0), W2B2 exhibited an average increase of 18.0 % in yield and 47.5 % in irrigation water productivity (WPI) with a 20 % reduction in irrigation amount across two seasons. The technique for order preference by similarity to ideal solution (TOPSIS) comprehensive evaluation determined that the W2B2 mode maximized comprehensive benefits, enhancing both the overall quality of tomato and achieving higher yield and WPI. This study utilized the Ep-20 to characterize the reference crop evapotranspiration (ETo) and the crop evapotranspiration (ETc). The investigation demonstrated the feasibility of utilizing Ep-20 to guide irrigation under biochar application conditions and develop ETc estimation models for water−biochar management modes to optimize solar greenhouse tomato production.

A Decision-Making Framework for Sustainable Highway Restaurant Site Selection: AHP-TOPSIS Approach based on the Fuzzy Numbers

This research work indicates the site selection problem for a restaurant beside the highway. It is really challenging as well as demanding work. It mainly depends on the cost of the selected location, transportation, parking facility, hygienic atmosphere, distance from the city, etc. In this paper, the proposed site selection problem is constructed and solved by using a fuzzy MUlti-Criteria Decision Making (MCDM) process. The two methods of MCDM, that we use here namely the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for calculate criteria weight and rank the proposed restaurant locations, respectively. Multiple decision makers (DMs) opinions are considered for data collection and Trapezoidal Fuzzy Numbers (TrFNs) are taken as uncertainty tools to deal with uncertainty. Finally, we perform the sensitivity and comparative analysis performed to verify the system's vagueness and flexibility.