Technique For Order Preference By Similarity To Ideal Solution Approach Research Articles

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS

The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.•The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.•The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.•The method is easiest and suitable for short term operation research.

INVESTIGATIONS ON THE MACHINABILITY CHARACTERISTICS OF Al–Zn(5.4)–Mg(2.4)–Cu(1.4) ALLOY COMPOSITE USING TOPSIS APPROACH

In this research, stir casting was used to create the aluminium alloy (AA7075) composite filled with 10[Formula: see text]wt.% zirconia (ZrO2) particles as reinforcement. Die sinking electric discharge machining (EDM) was used to examine the machining characteristics of the proposed composite. The machining experiments were executed in accordance with the [Formula: see text] orthogonal layout. Here, the material removal rate (MRR) and surface roughness (SR) were taken into account as the output responses, and the discharge current ([Formula: see text]), pulse on-time ([Formula: see text]), and pulse off-time ([Formula: see text]) were taken as the machining parameters. To determine the optimal parameter conditions for the output responses, the technique for order preference by similarity to ideal solution (TOPSIS) approach was applied. According to the experimental findings, the optimum settings of the parameters are determined at 15[Formula: see text]amps of ‘[Formula: see text]’, 750[Formula: see text][Formula: see text]s of ‘[Formula: see text]’ and 50[Formula: see text][Formula: see text]s of ‘[Formula: see text]’. ANOVA results stated that ‘[Formula: see text]’ has the most notable factor with a contribution of 90.85%. The interaction effect of parameters on the responses was shown by the contour plots. The confirmatory experiment was finally conducted at the optimum machining parameter settings, and it was found that the error only occurred in 8.4%.

Quality Improvement Project Selection Using Fuzzy AHP and TOPSIS to Support Lean Six Sigma at PT ABC

This research aims to improve project quality at PT ABC by integrating the Fuzzy Analytical Hierarchy Process (F-AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within the Lean Six Sigma framework. This method assesses and selects quality improvement projects by considering uncertainty and complexity in decision-making. The Fuzzy AHP approach is used to flexibly assign criteria weights, while TOPSIS is used to rank projects based on performance scores relative to the ideal solution. This research aims to select the most suitable Lean Six Sigma project using the MCDM fuzzy AHP and TOPSIS approaches at PT ABC. There are three alternative quality improvement projects and three assessment criteria. The data processing results using Fuzzy AHP and TOPSIS showed that the most influential criterion was the impact criterion, with a value of 0.665. Overall, the order of influencing criteria is cost (0.260) and time (0.075). Based on a review of all criteria, the project to increase the drive plate production line (A) capacity was ranked first with a value of 0.772. Second place is the project to increase capacity and consolidate rear axle assemblies A and B with a value of 0.5529. Finally, the third is the process innovation and integrated manufacturing project to strengthen the business with a value of 0.4053.

Multi-Objective Optimization of Tribological Characteristics for Aluminum Composite Using Taguchi Grey and TOPSIS Approaches

In this study, a multi-objective optimization regarding the tribological characteristics of the hybrid composite with a base material of aluminum alloy A356 as a constituent, reinforced with a 10 wt.% of silicon carbide (SiC), size 39 µm, and 1, 3, and 5 wt.% graphite (Gr), size 35 µm, was performed using the Taguchi method, gray relational analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making methods. Tribological tests were carried out on a “block on disc” type tribometer with lubrication. Load, sliding speed, and graphite mass concentration were analyzed as input parameters. As output parameters, wear rate and coefficient of friction were calculated. An analysis of variance (ANOVA) was conducted to identify all parameters that have a significant influence on the output multi-response. It was found that the normal load has the highest influence of 41.86%, followed by sliding speed at 32.48% and graphite addition at 18.47%, on the tribological characteristics of composites. Multi-objective optimization determined that the minimal wear rate and coefficient of friction are obtained when the load is 40 N, the sliding speed is 1 m/s, and the composite contains 3 wt.% Gr. The optimal combination of parameters achieved by GRA was also confirmed by the TOPSIS method, which indicates that both methods can be used with high reliability to optimize the tribological characteristics. The analysis of worn surfaces using scanning electron microscopy revealed adhesive and delamination wear as dominant mechanisms.

Experimental investigation and optimization of heat pipe heat exchanger performance under two distinct heat transport mediums through TOPSIS approach

This paper presents a comprehensive thermal performance analysis of the heat pipe heat exchanger by varying the input process parameters and optimizing the best condition for waste heat recovery through the TOPSIS technique. In this work, the heat pipe is constructed with a copper tube and segmented into evaporator and condenser sections carrying hot and cold fluids with a half-fill ratio. The heat pipe is oriented in 0° horizontally with the wick material made from stainless steel and works based on the capillary principle. The efficiency of the HPHE is evaluated by varying the input operational parameters, namely heat input, mass flow rate of hot fluid, inlet temperature of hot fluid, and mass flow rate of cold fluid, on response variables, specifically thermal resistance, energy balance ratio, universal heat transfer coefficient, and effectiveness. The experiment was designed for four factors at three levels as per Taguchi’s L27 orthogonal array, and the best optimising condition was determined by employing the multi-objective technique for order preference by similarity to ideal solution (TOPSIS) approach. The best input condition found was A3B1C1D3, in which the heat input was at 90 W, the mass flow rate of hot fluid was at 0.2 kg min−1, the mass flow rate of cold fluid was at 0.1 kg min−1, and the inlet temperature was at 60 °C. The effect of process parameters and their significance on output responses was analysed by executing an analysis of variance (ANOVA) statistical method. The mathematical regression model was generated, and validation was made to investigate the prediction error percentage.

Safety resilience evaluation of hydrogen refueling stations based on improved TOPSIS approach

The traditional use of TOPSIS evaluation for assessing the resilience of hydrogen refueling stations results in overly one-sided outcomes and fails to identify the indicators that have a significant impact on the resilience of the stations. Therefore, this paper proposes an improved method for evaluating the safety resilience of hydrogen refueling stations based on CRITIC-DEMATEL and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The CRITIC-DEMATEL combination weighting method is employed to determine weights, thus improving the TOPSIS method. This method is then applied to assess the resilience level of hydrogen refueling stations. Then, introducing a Conditional Logistic Regression model identifies indicators with significant impact on resilience. The improved TOPSIS method enhances the reliability and accuracy of evaluation results. The Conditional Logistic Regression model provides a more accurate delineation of the impact size of indicators on the safety resilience of hydrogen refueling stations compared to traditional methods.

Determination of multi-UAVs formation shape: Using a requirement satisfaction and spherical fuzzy ANP based TOPSIS approach

Multiple unmanned aerial vehicles (multi-UAVs) formation shape refers to the geometric shape when multi-UAVs fly in formation and describes their relative positions. It plays a necessary role in multi-UAVs collaboration to improve performance, avoid collision, and provide reference for control. This study aims to determine the most appropriate multi-UAVs formation shape in a specific mission to meet different and even conflicting requirements. The proposed approach introduces requirement satisfaction and spherical fuzzy analytic network process (SFANP) to improve the technique for order preference by similarity to ideal solution (TOPSIS). First, multi-UAVs capability criteria and their evaluation models are constructed. Next, performance data are transformed into requirement satisfaction of capability and unified into a same scale. Qualitative judgments are made and quantified based on spherical fuzzy sets and nonlinear transformation functions are developed for benefit, cost, and interval metrics. Then, SFANP is used to handle interrelationships among criteria and determine their global weights, which takes decision vagueness and hesitancy into account and extends decision-makers’ preference domain onto a spherical surface. Finally, alternative formation shapes are ranked by their distances to the positive and negative ideal solution according to the TOPSIS. Furthermore, a case study of 9 UAVs performing a search-attack mission is set up to illustrate the proposed approach, and a comparative analysis is conducted to verify the applicability and credibility.

Streamlining apartment provider evaluation: A spherical fuzzy multi-criteria decision-making model

In the context of the thriving real estate market in developing countries like Vietnam, understanding consumer preferences and effectively addressing them through a comprehensive multi-criteria decision-making (MCDM) framework is paramount for real estate providers. This study presents a two-stage MCDM model that integrates the Delphi technique and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on Spherical Fuzzy Sets (SFSs). Initially, the SF-Delphi technique validates critical criteria influencing customers' apartment selection in Vietnam. Secondly, the SF-TOPSIS method evaluates the top ten apartment providers. To ensure robustness and validity, a comparative analysis compares the results with those from the Intuitionistic Fuzzy TOPSIS (IF-TOPSIS) and Fuzzy TOPSIS (F-TOPSIS) methods. Subsequently, five rank correlation coefficients (Spearman, Kendall, Goodman-Kruskal, Weighted rank measure of correlation, Weighted Similarity) are used to assess the relationships between various TOPSIS techniques applied to apartment suppliers in Vietnam. The correlation coefficients demonstrate strong agreement among the TOPSIS methods, with the smallest coefficient being 0.7778, surpassing the threshold of 0.7. This high level of consistency confirms the efficacy of the proposed TOPSIS approach with different Fuzzy Sets in reliably evaluating customers' preferences for apartment suppliers. Notably, the legal aspect's prominence underscores its critical role in shaping customer choices, emphasizing the significance of considering legal factors in the context of apartment supply and demand in Vietnam. Furthermore, using SFSs makes this approach particularly suited to capture consumer perceptions within the dynamic and uncertain business environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA).

Improvement on the wire EDM characteristics of zirconia filled Al7050 (Al-Zn-Cu-Mg) alloy composite using Taguchi coupled TOPSIS method

Wire electrical discharge machining (WEDM) is an cutting edge non-traditional techniques for producing complicated 3D forms in rigid materials with greater dimensional accuracy. The aim of the investigation was, to predict the WEDM characteristics of ZrO2 ceramic addition of Al7050 alloy composite by employing Taguchi coupled Technique for order preference by similarity to ideal solution (TOPSIS) approach. The work material was manufactured by stir casting process utilizing 12 wt.% of ZrO2 particulates in Al7050 alloy matrix. The experimental work was carried out as per the L9 (33) orthogonal array design by taking input parameters like pulse current ( Ip), pulse on-time ( Ton) and pulse off-time ( Toff), respectively. The impact of the WEDM parameters on the material removal rate (MRR) and surface roughness (SR) were determined by analysis of variance (ANOVA). Based on the TOPSIS approach, the higher MRR (0.32251 g/min) with lesser SR (3.982 µm) were achieved at the ‘ Ip’ of 25 amps, ‘ Ton’ of 115 µs and ‘ Toff’ of 60 µs. ANOVA results explored that the ‘ Ton’ was identified as the primary impact factor on MRR and SR, subsequently by ‘ Ip’ and ‘ Toff’ with contribution of 44.23%, 36.53%, and 12.16%, respectively. Moreover, the surface topography of the machined composite surface was analyzed through scanning electron microscopy (SEM). It was clearly revealed the formation of surface defects such as craters, micropores, cracks and also the appearance of a recast layer on the machined surface.

An integrated fuzzy AHP and fuzzy TOPSIS approach for screening backfill materials for contaminant containment in slurry trench cutoff walls

The inclusion of backfill material is crucial for the effective containment of contaminants in a slurry trench cutoff wall. However, selecting the optimal backfill material is complex and involves multiple criteria. This study aimed to develop a new approach to assist in the selection process by rationally dealing with the subjective judgment of decision makers. The approach employed fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods. Initially, consistent judgment matrices were established based on a unique consulting questionnaire. The fuzzy AHP method used Buckley's method to calculate the fuzzy weights of pairwise comparisons. The fuzzy TOPSIS method was then employed to rank and evaluate four different backfill materials. The proposed methodology was successfully implemented at a real contaminated site in China to select the appropriate backfill material for construction of a slurry trench cutoff wall. Based on the proposed integrated methodology, fiber reinforced reactive magnesia-activated slag-soil-bentonite (FMSB) was determined as the most appropriate backfill materials for contaminant containment in slurry trench cutoff walls at the site reported in this study.

An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India

Fossil fuels cause air pollution and climate change, impacting human health. Mumbai imports and spends heavily on petroleum. Therefore, to reduce the amount of fossil fuel and for mitigating environmental issues, the use of electric vehicles (EVs) is an effective solution. The first priority towards supporting the widespread adoption of EVs is the availability of convenient charging stations. This research aims to delineate the optimal places for new electric vehicle charging stations (EVCS) in study area. The interrelationship of thirteen parameters have been used to determine the Multi Influencing Factor (MIF) weights. These MIF weights were then integrated into Geographical Information System (GIS) for weighted overlay analysis. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) used to assign ranks based on suitability index values. The result shows that the zone falling between 297.587 to 488.520 suitability index has suitability for EVCS. The proposed methodology offers a more precise solution for EVCS problems with a high level of uncertainty and assists policymakers and administrators in making effective decisions for future planning and strategies.

Possibilistic simulation based interactive fuzzy MAGDM under discrimination q-rung picture linguistic information. Application in educational programs efficiency evaluation

In certain Multi-Attribute Group Decision-Making (MAGDM) approaches an aggregation of decision-making matrices of experts’ evaluations/ratings of possible alternatives by their attributes into an etalon decision-making matrix is an important task. Based on possible great experience and deep knowledge of the subject, the expert tries to dominate other experts in the decision-making process. Besides, every expert has a possibility of a certain degree to influence on the decision-making process. In such cases experts’ evaluations aggregation into an etalon matrix by the additive or linear aggregation instruments is unacceptable. We present a new two-stage MAGDM approach to solve this task. On the first stage: In the evidence theory environment the connection between experts’ pair interaction indexes and possibility levels of their influence on decision-making process is constructed. For this, a Monte-Carlo simulation algorithm is created, the result of which is the estimated class of associated probabilities of the possibility distribution. Experts’ evaluations in decision-making matrixes are represented by the q-rung orthopair fuzzy numbers (q-ROFNs). Before aggregation of experts’ data into an etalon decision-making matrix, evaluations are transformed into discrimination q-rung picture linguistic numbers (q-RPLNs). New definitions are introduced for the Score and Accuracy functions of the q-rung picture linguistic numbers. q-rung picture linguistic numbers contain as well quantitative as qualificative information on experts’ reflections on objects, while discrimination q-rung picture linguistic numbers describe interactive phenomena between arguments. Based on constructed possibility distribution on the experts’ group, the ordered weighted averaging (OWA) and Choquet averaging (CA) aggregation operators under q-rung picture linguistic environment are extended (q-RPLOWA and q-RPLCA, respectively). By the q-RPLOWA and q-RPLCA operators decision-making matrices of experts’ evaluations/ratings are condensed into an etalon decision-making matrix. On the second stage: The concept of Euclidean distance and the entropy of De Luca–Termini are defined for q-rung picture linguistic set. The new relative closeness parameter for the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach under q-rung picture linguistic information is also defined. The technique for the TOPSIS approach for the ranking of possible alternatives in MAGDM by aggregating discrimination q-rung picture linguistic data of etalon matrix is developed. For illustration of the obtained results, Educational Programs Efficiency Evaluation problem is considered. Our constructed MAGDM approach complies with the principle of expertise in educational programs evaluation commission. The numerical results obtained for the q-RPLNs environment and the ranking of alternatives are compared with the numerical results and the ranking of alternatives obtained by the same approach for the initial expert data in q-ROFNs environment. A comparison with the results obtained by other existing methods is given.

Intuitionistic fuzzy three-way ranking-based TOPSIS approach with a novel entropy measure and its application to medical treatment selection

Intuitionistic fuzzy set (IFS) is one of the tools to address the uncertainties in the data by using the two membership degrees. On the other hand, entropy is a useful index for characterizing the uncertainty of fuzzy information. This paper aims to propose an entropy to measure the uncertainty of IFSs from a new perspective and apply it to multi-criteria decision-making (MCDM). To accomplish this, firstly, a new score function (SF) for intuitionistic fuzzy numbers is proposed based on a probabilistic perspective to mitigate the deficiencies of existing SFs. The merits of the proposed SF are also highlighted. Then we construct an entropy index by using the proposed SF. The various axioms and properties are described in detail. Two numerical examples are shown, in detail, to illustrate the performance of the proposed entropy by comparing it with other existing ones. Finally, by combining the proposed entropy with Three-Way Ranking-based method (TWR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a decision-making framework called IFTWR-TOPSIS, is presented to address the MCDM problem in medical treatment selection. Through sensitivity and comparative analyses, the IFTWR-TOPSIS method is feasible and effective. More importantly, it follows that our proposed method is superior to some classical MCDM methods and has broad application prospects.

Integrating TOPSIS and ELECTRE-Ⅰ methods with cubic $ m $-polar fuzzy sets and its application to the diagnosis of psychiatric disorders

<abstract><p>Many real-world decision-making issues frequently involve competing sets of criteria, uncertainty, and inaccurate information. Some of these require the involvement of a group of decision-makers, where it is necessary to reduce the various available individual preferences to a single collective preference. To enhance the effectiveness of multi-criteria decisions, multi-criteria decision-making is a popular decision-making technique that makes the procedure more precise, reasonable, and efficient. The "Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)" and "Elimination and Choice Transforming Reality (ELECTRE)" are prominent ranking methods and widely used in the multi-criteria decision-making to solve complicated decision-making problems. In this study, two $ m $-polar fuzzy set-based ranking methods are proposed by extending the ELECTRE-Ⅰ and TOPSIS approaches equipped with cubic $ m $-polar fuzzy (C$ m $PF) sets, where the experts provide assessment results on feasible alternatives through a C$ m $PF decision matrix. The first proposed method, C$ m $PF-TOPSIS, focuses on the alternative that is closest to a C$ m $PF positive ideal solution and farthest away from the C$ m $PF negative ideal solution. The Euclidean and normalized Euclidean distances are used to determine the proximity of an alternative to ideal solutions. In contrast, the second developed method is C$ m $PF-ELECTRE-Ⅰ which uses an outranking directed decision graph to determine the optimal alternative, which entirely depends on the C$ m $PF concordance and discordance sets. Furthermore, a practical case study is carried out in the diagnosis of impulse control disorders to illustrate the feasibility and applicability of the proposed methods. Finally, a comparative analysis is performed to demonstrate the veracity, superiority, and effectiveness of the proposed methods.</p></abstract>

Intuitionistic fuzzy-based TOPSIS method for multi-criterion optimization problem: a novel compromise methodology

<abstract><p>The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles.</p></abstract>

Spherical Linear Diophantine Fuzzy TOPSIS Algorithm for Green Supply Chain Management System

Enhancing competitive pressure is one of the most significant roles of supply chain management. The competitive environment and customer perception have shifted in favour of an ecological mentality. As a result, green supplier selection (GSS) has emerged as a critical problem. The challenge of green supplier selection striving for agility, durability, ecological sensitivity, leanness, and sustainability is tackled in this paper. In terms of recycling applications, environmental applications, carbon footprint, and water consumption, the environmental parameters evaluated in GSS and traditional supplier selection differ. Because of the form of the problem, a resolution is defined, which comprises an algorithm entrenched in the spherical linear Diophantine fuzzy sets (SLDFSs) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique. Before discussing the approach of the SLDF model, some background information on SLDF sets is provided. To assure the uniqueness of this robust extension, different operations on SLDFSs are described, along with some concise interpretations to help the reader comprehend these ideas. A robust TOPSIS approach has been utilized in the issue of GSS by taking into consideration the multicriteria decision-making (MCDM) technique particularly useful in several areas, like analyzing and choosing traditional and environmental conventionalities. Due to linguistic criteria and the inability to assess all criteria, the fuzzy technique must be used with the TOPSIS method to lessen the consequences of instability and ambiguity. The spherical linear Diophantine fuzzy TOPSIS approach is employed, as it simplifies the evaluation of decision-makers and criteria. The hybrid technique resulting from integrating the SLDFS and TOPSIS is extremely successful in selecting which provider is more suited among the alternatives established on the criteria set by the order of significance, and this method may also be incorporated into similar issues.

An integrated fuzzy approach based failure mode and effects analysis for a risk assessment

This paper provides to cope with the limitations of traditional FMEA by using an integrated fuzzy multi-criteria decision making method, which considers fuzzy extension of AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and a linear programming. The proposed method is shown for an application to failure mode and effects analysis (FMEA) based risk assessment of a construction firm. Firstly, fuzzy extension of AHP approach is utilized to define the weights of criteria in risk evaluation. Secondly, fuzzy TOPSIS approach is used to determine the most important failure mode in the construction firm. This work handles a sensitivity analysis and a comparison with the other methods. FMEA related papers in the literature presents only ranking of failure modes by using various methods. This study aims to handle the limited resources such as budget and time in a linear programming to establish a suitable occupational health and safety policy.

Decision-Making Framework for Prioritizing the Improvement of Pedestrian Facilities in Urban Areas Using Integrated Delphi, AHP, and TOPSIS Approach

The existing pedestrian facilities in urban areas can be improved by prioritizing the areas based on their infrastructure performance, location, safety, mobility, and liveability aspects. This paper aims to present a combination of Delphi, analytic hierarchy process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing the improvement of pedestrian facilities in an urban area. The approach has been broken into three stages. Firstly, the significant factor assessment was done by the Delphi method. Secondly, AHP was applied to estimate the relative weightage of each factor. Finally, the TOPSIS method was used to rank the alternatives in preparation for the final decision. For better understanding, the proposed method was demonstrated by applying the framework in a case study. The analyses revealed that the proposed method is effective for prioritizing the improvement of pedestrian facilities in an urban area. The method described in this paper is intended to assist decision makers in ranking different sidewalk stretches in the urban areas that need to be improved based on various attributes. This paper makes a major impact by incorporating a novel approach for the decision making of pedestrian facilities improvement, which considered both subjective and objective judgments of multiple decision makers in the procedure at various stages using different multi-criteria decision making techniques. This approach can be combined with a scheduling methodology and optimization technique to increase the efficiency of the resource allocation process.

AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India.

Flooding is one of the major natural catastrophic disasters that causes massive environmental and socioeconomic destruction. The magnitude of losses due to floods has prompted researchers to focus more on robust and comprehensive modeling approachesfor alleviating flood damages. Recently developed multi-criteria decision making (MCDM) methods are being widely used to construct decision-making process more participatory, rational, and efficient. In this study, two statistical MCDM approaches, namely the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), have been employed to generate flood risk maps together with hazard and vulnerability maps in a GIS framework for Navsari city in Gujarat, India, to identify the vulnerable areas that are more susceptible to inundation during floods. The study area was divided into 10 sub areas (i.e., NC1 to NC10) to appraise the degree of flood hazard, vulnerability and risk intensities in terms of areal coverage and categorized under 5 intensity classes, viz., very low, low, moderate, high, and very high. A total of 14 flood indicators, seven each for hazard (i.e., elevation, slope, drainage density, distance to river, rainfall, soil, and flow accumulation) and vulnerability (i.e., population density, female population, land use, road network density, household, distance to hospital, and literacy rate) were considered for evaluating the flood risk. Flood risk coverage evaluated from the two approaches were compared with the flood extent computed from the actual flood data collected at 36 random locations. Results revealed that the TOPSIS approach estimated more precise flood risk coverage than the AHP approach, yielding high R2 values, i.e., 0.78 to 0.95 and low RMSE values, i.e., 0.95 to 0.43, for all the 5 risk intensity classes. The sub areas identified under "very high" and "high" risk intensity classes (i.e., NC1, NC4, NC6, NC7, NC8, and NC10) call for immediate flood control measures with a view to palliate the extent of flood risk and consequential damages. The study demonstrates the potential of AHP and TOPSIS integrated with GIS towards precise identification of flood-prone areas for devising effective flood management strategies.

Supplier Performance Evaluation on Regular Raw Material Suppliers by Applying AHP and TOPSIS Approaches (Evidence from the Apple Agroindustry)

The supplier is one of the supply chain entities that serves as a provider of raw materials, and poor supplier performance will harm the company's smooth production system. This study aims to identify supplier criteria, analyze priority weights, and evaluate supplier performance in the apple Agro-industry. Respondents in this study were business owners chosen through the judgmental sampling method. The Analytical Hierarchy Process (AHP) method was used to test the priority weights of supplier performance criteria, while the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to test the priority weights of supplier performance priorities. The results showed that the service criteria received the highest priority weighting criteria, followed by price, quality, and delivery. Good service will positively impact integration, making it easier to achieve an effective and efficient supply chain. Meanwhile, in terms of the company's fixed supplier performance priorities, supplier 1 received the highest rating, followed by suppliers 2 and 3. Thus, supplier performance is quite good, but several things could be improved, specifically fruit size and responsiveness to supplier 3 and prices to suppliers 1 and 2.