Multiple-attribute decision-making approach for an energy-efficient facility layout design

The Analytical Hierarchy Process (AHP) approach and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are two methods that are commonly used to handle multicriteria problems. The AHP technique uses a matrix of pairwise comparisons and a hierarchical approach to criteria in order to obtain the final criteria weights. In contrast, the TOPSIS approach incorporates the concepts of proximity to the ideal solution and preference. Next, TOPSIS uses the previously established criterion weights to compute each option's relative closeness score to the ideal answer. In order to determine how effectively the AHP and TOPSIS methodologies complement one another, this study aims to assess the protocols and underlying assumptions of the two approaches. When decision makers are aware of the benefits and drawbacks of each approach, they may choose the best course of action from a mix of the two. The results of the study indicate that the process may be streamlined, subjectivity can be reduced, and criterion weights can be stabilized by combining the AHP and TOPSIS approaches.The Analytical Hierarchy Process (AHP) approach and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are two methods that are commonly used to handle multicriteria problems. The AHP technique uses a matrix of pairwise comparisons and a hierarchical approach to criteria in order to obtain the final criteria weights. In contrast, the TOPSIS approach incorporates the concepts of proximity to the ideal solution and preference. Next, TOPSIS uses the previously established criterion weights to compute each option's relative closeness score to the ideal answer. In order to determine how effectively the AHP and TOPSIS methodologies complement one another, this study aims to assess the protocols and underlying assumptions of the two approaches. When decision makers are aware of the benefits and drawbacks of each approach, they may choose the best course of action from a mix of the two. The results of the study indicate that the process may be streamlined, subjectivity can be reduced, and criterion weights can be stabilized by combining the AHP and TOPSIS approaches.

PurposeThis paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.Design/methodology/approachThe optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.FindingsThe optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.Research limitations/implicationsIn this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.Practical implicationsThe study presents the case of an automotive component, which illustrates practical relevance.Originality/valueIn several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

The hesitant fuzzy β-covering rough set offers stronger representational capabilities than earlier hesitant fuzzy rough sets. Its flexibility makes it more suitable for hesitant fuzzy multi-attribute decision-making (MADM). As a result, it has become a popular research focus in decision analysis and has drawn significant attention from scholars. However, the existing hesitant fuzzy β-covering rough set based on t-norms cannot handle the overlap and correlation between hesitant information well. Addressing this problem, we propose the hesitant fuzzy overlap function and hesitant fuzzy β-covering (I,O) rough set (HFβCIORS) models based on the hesitant fuzzy overlap function. First, we establish the definition of the hesitant overlap function and representable hesitant fuzzy overlap function on a partial order relation. Based on proposed definitions, we provide examples of representable and unrepresentable hesitant fuzzy overlap functions and offer a detailed proof to explain the unrepresentable function. Second, we construct four types of HFβCIORS models and prove some of its important properties. Thirdly, we integrate the HFβCIORS models with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and apply them to solve MADM problems. The validity of the proposed method is demonstrated through a practical application, and its stability and effectiveness are confirmed via sensitivity and comparative analyses. Based on these validations, our method proves effective in addressing MADM problems, offering reliable decision-making support.

The aim of this research was to apply MCTs, Analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS), to reduce the EsSense25 and MemVOC vocabularies and evaluate them with coffee consumers in remote and sensory laboratory contexts. In the first stage, coffee experts evaluated both vocabularies to generate reduced lists. In the second stage, the response time for complete and reduced lists was analyzed using a pilot group of consumers. Finally, in the third stage, the complete and reduced lists were applied to coffee consumers in remote and laboratory contexts using the check-all-that-apply (CATA) technique, and their correlation was evaluated through Multiple factor analysis (MFA). The results showed that with AHP, the EsSense25 and MemVOC lists were reduced to 10 emotions and 11 memories, while TOPSIS yielded 16 emotions and 15 memories. Significant differences were found in response times between contexts and lists. In the remote context, the evaluation of the complete list took 340 s, while the reduced lists by TOPSIS and AHP required 244 and 160 s, respectively. In the sensory laboratory, the response time for the complete list was 485.8 s, while the reduced lists required 236.3 s for TOPSIS and 131.4 s for AHP, respectively. The correlation between complete and reduced lists ranged from 0.63 to 0.76 in the remote context and from 0.81 to 0.87 in the sensory laboratory. It is concluded that MCTs are an efficient alternative to reduce extensive lists of emotions and memories based on expert opinions, facilitating rapid and effective evaluations with consumers.

The interval‐valued q‐rung orthopair fuzzy set (IVq‐ROFS) and complex fuzzy set (CFS) are two generalizations of the fuzzy set (FS) to cope with uncertain information in real decision making problems. The aim of the present work is to develop the concept of complex interval‐valued q‐rung orthopair fuzzy set (CIVq‐ROFS) as a generalization of interval‐valued complex fuzzy set (IVCFS) and q‐rung orthopair fuzzy set (q‐ROFS), which can better express the time‐periodic problems and two‐dimensional information in a single set. In this article not only basic properties of CIVq‐ROFSs are discussed but also averaging aggregation operator (AAO) and geometric aggregation operator (GAO) with some desirable properties and operations on CIVq‐ROFSs are discussed. The proposed operations are the extension of the operations of IVq‐ROFS, q‐ROFS, interval‐valued Pythagorean fuzzy, Pythagorean fuzzy (PF), interval‐valued intuitionistic fuzzy, intuitionistic fuzzy, complex q‐ROFS, complex PF, and complex intuitionistic fuzzy theories. Further, the Analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) method are also examine based on CIVq‐ROFS to explore the reliability and proficiency of the work. Moreover, we discussed the advantages of CIVq‐ROFS and showed that the concepts of IVCFS and q‐ROFS are the special cases of CIVq‐ROFS. Moreover, the flexibility of proposed averaging aggregation operator and geometric aggregation operator in a multi‐attribute decision making (MADM) problem are also discussed. Finally, a comparative study of CIVq‐ROFSs with pre‐existing work is discussed in detail.

This paper proposed a concept of the Group Decision Support System (GDSS) to evaluate the performance of Information and Communications Technology (ICT) Projects in Indonesian regional government agencies to overcome any possible inconsistencies which may occur in a decision-making process. By considering the aspect of the applicable legislation, decision makers involved to provide an assessment and evaluation of the ICT project implementation in regional government agencies consisted of Executing Parties of Government Institutions, ICT Management Work Units, Business Process Owner Units, and Society, represented by DPRD (Regional People’s Representative Assembly). The contributions of those decision makers in the said model were in the form of preferences to evaluate the ICT project-related alternatives based on the predetermined criteria for the method of Multiple Criteria Decision Making (MCDM). This research presented a GDSS framework integrating the Methods of Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Copeland Score. The AHP method was used to generate values for the criteria used as input in the calculation process of the TOPSIS method. Results of the TOPSIS calculation generated a project rank indicated by each decision maker, and to combine the different preferences of these decision makers, the Copeland Score method was used as one of the voting methods to determine the best project rank of all the ranks indicated by the desicion makers.

This research aims to develop a Decision Support System (DSS) using the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assist the Ministry of Health in selecting the most suitable medical equipment distributor. The AHP method is employed to determine the weight of multiple evaluation criteria, including Quality Management System, Human Resources Management, Infrastructure, Inventory Handling, Traceability, Complaint Handling, FSCA, Returns, Disposal, Illegal Access, Internal Audit, Management Review, and Third-Party Activities. Once the weights are established, the TOPSIS method is applied to evaluate and rank the distributor alternatives based on their relative proximity to the ideal and anti-ideal solutions. The integration of AHP and TOPSIS ensures a more structured, objective, and data-driven decision-making process. The results show that the distributor labeled D4 has the highest preference value (0.64632), indicating the best performance among all alternatives evaluated. This combined method enhances decision-making accuracy, reduces subjectivity, and aligns selection outcomes with operational and regulatory standards. The study concludes that implementing a DSS using AHP and TOPSIS can significantly improve the efficiency, transparency, and effectiveness of medical equipment distributor selection within the healthcare logistics system.

Initial water rights allocation is a complex decision problem, and it is difficult for traditional methods to determine weights and solve the decision problem. In this study, an improved TOPSIS (Technique for order preference by similarity to ideal solution) method is proposed using vertical distance instead of Euclidean distance to calculate the closeness degree of schemes to the ideal solution, which overcomes the shortage of traditional TOPSIS method. In order to determine the weights of evaluation indexes reasonably, the improved method adopts game theory to harmonize and conjugate the subjective weight and the objective weight. On this basis, a comprehensive weight which eliminates the unilateral result can be obtained, and a model of initial water rights allocation in the watershed is established by the improved TOPSIS method. Finally, the proposed model is applied in the study of initial water rights allocation in Fuhuan River in Hubei Province, which shows rationality and validity when comparing with other models like traditional TOPSIS method, analytic hierarchy process and projection pursuit model.

PurposeThe paper develops a decision-making model for supplier selection combining quality function deployment (QFD), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The efficacy of the model was demonstrated by applying it for supplier selection of lithium ion batteries.Design/methodology/approachThe proposed methodology involved identifying customer requirements for lithium ion batteries and translating them to requisite technical characteristics using QFD. Further, separate sourcing, safety and sustainability-related supplier parameters were proposed taking into account the manufacturer's point of view. The relative weight of each parameter was then calculated using AHP, and finally, TOPSIS was used to select the best supplier.FindingsThe proposed methodology was applied to six suppliers of lithium ion batteries, and the obtained results were used to select the most and least preferred suppliers.Practical implicationsThe obtained results cannot be generalized and are valid to the case environment. However, the proposed approach can be used for any environment related to supplier selection after capturing the corresponding parameters. The proposed approach does not restrict the number of parameters to be considered.Originality/valueMany researches related to supplier evaluation are reported in literature, but few studies are available related to supplier performance evaluation for lithium ion batteries using QFD, AHP and TOPSIS. The study will provide a guideline for comparing and selecting supplier on the basis of performance in general and its application to lithium ion batteries in specific.

PurposeThe concept of agile manufacturing is becoming critically important to manufacturing industry due to rapid industrialization, fluctuating customer demand, and turbulent business environment. The aim of this study is to prioritize the attributes for successful implementation of agile manufacturing using a combined analytical hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach in Indian manufacturing industry.Design/methodology/approachThe current study identifies eight agile manufacturing attributes through literature review and discussion with subject experts. The experts from different manufacturing industry have been asked to provide data for pairwise comparison of attributes. Afterward, an integrated AHP and TOPSIS approach is employed. The AHP is used to drive the priority weights of the attributes, and TOPSIS is used for prioritizing the attributes for successful implementation of agile manufacturing.Findings“Information technology,” “human resource management-related issues,” “customer-related issues,” “leadership support,” and “organizationalc related-issues” have been ranked as the top five significant and contributing attributes, which can pave the path for top management to concentrate on the critical areas and allocate significant resources to ensure successful implementation of agile manufacturing.Originality/valueThis research integrates AHP and TOPSIS to prioritize the attributes for successful implementation of agile manufacturing, which are further validated by comparing the ranks obtained through respective approach and sensitivity analysis.

The name Setia Hati Terate Brotherhood means prioritizing relationships between others that grow from a sincere, sincere, and clean heart. In addition, it was also explained that all members of the Setia Hati Terate Brotherhood are one brother who does not distinguish between rank, degree, and dignity as well as ethnicity, religion, race, and inter-group (SARA). One of the constraining factors for the development of Pencak silat which is in the spotlight is the lack of trainers who are qualified in selecting athletes. Considering all the reasons for the problems at the research site, it is necessary to create a decision support system that can be used by coaches and organizers of an open selection that can assist in selecting athletes to get the most potential athletes to achieve maximum performance. The selection system for Pencak silat athletes will use the AHP method which can decompose problems into a hierarchy so that it is easy to solve, while TOPSIS is a ranking method that has the advantage that it considers the best solution not only the solution closest to the ideal solution but also the most far from a negative solution. The method used is the Analytical Hierarchy Process (AHP) and Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) with the PHP programming language. This study has applied the analytical hierarchy process (AHP) method to determine and test the consistency of the weight of the criteria which includes 4 criteria, namely physical, technical, psychological, and strategic. This research uses the application of the AHP and (TOPSIS) methods with the test results, it is concluded that the system built is more effective in selecting Pencak silat athletes than the test team without using the system.

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.

"Coal-to-electricity" project can effectively reduce the air pollution in the northern China, and their benefits evaluation can reasonably reflect the effect of the projects. According to the present situation of the "coal-to-electricity" projects in a northern city, the evaluation index system is put forward to estimate the benefits of the projects with the aspects of economic benefits, social benefits and operation status of the distribution networks. A statistic method - TOPSIS (technique for order preference by similarity to ideal solution) is used to evaluation and analysis. In this method, the index system is weighted by the combination of analytic hierarchy process (AHP) and entropy weighting method. In the evaluation system of "coal-to-electricity" project, combination weighting - TOPSIS method can sort the different projects based on the distance between their comprehensive benefits and the positive and negative ideal solution. After the analysis of "coal-to-electricity" region with poor benefits, some recommendations can be given. Three actual project examples are applied for the evaluation system, and then the evaluation result of the examples is compared. Finally, the result is analyzed, while some improvement measures are put forward in these examples, which can give the reference and the guidance for the further implementation of the projects.

This study is designed to provide paediatric surgeons' perspectives on the relative importance of the criteria for choosing a surgical mode for undescended testicle treatment between open surgery (orchiopexy) or laparoscopic surgery and determine the suitable surgical mode by using the multicriteria decision‐making method. The analytic hierarchy process (AHP), the technique for order preference by similarity to ideal solution (TOPSIS), and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods are utilized. A decision hierarchy is set with a goal, criteria, and alternatives. Our model is developed on the basis of four main criteria (time‐related, patient‐related, recovery‐related, and complication‐related factors) and 11 subcriteria (operation duration, length of stay in hospital, preoperative preparation time, recovery period after surgery, age of patient, weight of patient, aesthetic appearance, quality of life satisfaction, recurrence, possibility of infection, and vital function) as well as two alternatives, laparoscopy and open surgery. An online questionnaire was sent to the paediatric surgeons; responses from the 31 experts (i.e., paediatric surgeons) indicate that the most important criteria are found to be the time‐related factors, especially preoperative preparation time. On the other hand, the least important criteria are found to be complication‐related factors. Moreover, laparoscopy is chosen as the most suitable method for the undescended testicle using the AHP, TOPSIS, and VIKOR methods. This study helps paediatric surgeons' decision making by elucidating their priority criteria and representing their decision‐making process in terms of quantity with the help of AHP, TOPSIS, and VIKOR methodologies.

An extended compromise ratio method for fuzzy group multi-attribute decision making with SWOT analysis