Application of machine learning to storage allocation decision making system within air cargo terminals

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.

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.

Purpose - The purpose of this study, Borsa Istanbul 100 (BIST100) of the financial performance of energy companies traded multi-criteria decision making methods, TOPSIS (Technique for order preference by Similarity to ideal solution) method is measured. Methodology - For this purpose, 5 energy companies traded in BIST in 2021 and whose data are regularly accessed have been included in the scope of the study. The annual balance sheet and income statements of the companies were used in the study. The balance sheets and income statements of the companies within the scope of the research for the year 2021 were taken from the official website of the Public Lighting Platform (KAP). During the period when the application part of this study was conducted, the data of the year 2022 could not be used due to the fact that the data of the year 2022 has not yet been published. The obtained data were measured by TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which is one of the multi-criteria Decisionmaking methods, and the performance of the companies were analyzed and compared among themselves. Findings - Ipek Natural Energy Resources Research and Production A. One of the companies included in the study as a result of the analyzes performed by the TOPSIS method.Sh. ((IPEK) was found to be the company with the best data in terms of financial performance in the analyzes carried out in accordance with the criteria included in the evaluation. After this company, the second best company in terms of financial performance is Koza Anadolu Metal Madencilik Işletmeleri A.Sh. He appeared as a (COCOON). Conclusion - In the light of the analyzes carried out in the study; IPEKE company ranked first as the best company in terms of financial performance. Ardın KOZAA of IPEKE company ranked second, ASELS company ranked third, AKSEN company ranked fourth and VESTL company ranked last in terms of financial performance among the 5 companies included in the study. Dec. Apart from these data, it can be said that changing the December period to be based on different studies, using different financial ratios, the degree of importance given to criteria, using other multi-criteria decision-making methods (MSCS) such as MOORA other than TOPSIS may give results that may change the performance ranking. Keywords: BIST-100, TOPSIS, financial performance, energy, MCDMM JEL Codes: C61, M41, M49

This study evaluates the financial performance of nine information technology (IT) firms, listed in National Stock Exchange of India, for a period of 5 years from 2011 to 2015. A multi-criteria decision-making technique (MCDM) - Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) - is used to rank the firms according to their financial performance, by considering 10 frequently used financial ratios as multiple criteria. The results indicate that TCS firm has been the top performer for the 4 years out of a total 5-year period of the analysis, whereas the firm Wipro is found to have the lowest rank during the same period. The lower ranking firms can revise their strategies by analysing the policies of better-performing firms. These results provide useful insights to identify the consistent and stable players in terms of financial performance. These findings could be useful for the managers at different levels, various stakeholders in the firms, investors and investment analysts.

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.

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.

This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.

Purpose – This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items directly relates to cost and quality of raw materials purchased from the supplier. In an increasingly competitive environment, firms are paying more attention to selecting the right suppliers for procurement of raw materials and component parts for their products. The present research work focuses on this issue of supply chain management. Design/methodology/approach – This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches. Findings – The supply chain network is witnessing a changing business environment due to government policies aimed at promoting new small manufacturing enterprises (small and medium-sized enterprises) for intermediate parts and components. Hence, the managers have an option to select a new group of suppliers and allocate the optimal multi-period demand among the new group of suppliers to maximize their purchase value. In this context, the proposed hybrid model would be beneficial for the managers to operate their supply chain effectively and efficiently. The present research work will be helpful for the managers who are interested to reconfigure their supply chain under the failure of any supply chain partner or in a changing business environment. The model provides flexibility to the managers for evaluation of the different available alternatives to take a decision of optimal demand allocation among the suppliers. The proposed hybrid (fuzzy, TOPSIS and MILP) model provides more objective information for supplier evaluation and demand allocation among suppliers in a supply chain. The managers can use the proposed model to the analysis of other management decision-making problems. Originality/value – This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. This hybrid algorithm prioritizes the suppliers and then allocates the multi-period demand among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

The objective of this research is to determine the financial performance of Turkish cement firms using quarterly data from the period between 1997 and 2022 through the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The research takes into account company mergers that occurred in 2020 and analyzes two distinct periods: from 1997 to 2020, and post-2020. Fourteen companies were examined in the first period, while ten companies were assessed in the latter period. To measure financial performance, ten common financial ratios selected based on literature were utilized. Determining the importance of these financial ratios is a crucial step in multi-criteria decision-making techniques; hence, the entropy method was employed to address this issue. According to empirical results, before the company mergers, UNYE and MARDIN cement firms were observed to consistently perform well financially, often ranking at the top based on the financial parameters considered. However, after the merger, it has been noted that BUCIM consistently held the top position in every quarter.

In this research work, aluminium oxide (Al 2 O 3 ), copper oxide (CuO) and gold (Au) nanofluids are prepared with the volume concentrations of 0.1%, 0.2%, 0.3% and 0.4% nanoparticles and tested in solar flat plate collector to estimate the heat transfer characteristics and collector efficiency. The influence of the input variable such as material (type of nanofluid), nanoparticle concentration and the mass flow rate (such as 0.016 kg/s, 0.033 kg/s and 0.05 kg/s) are studied experimentally. With the aim of determining the best possible heat transfer and the collector efficiency with minimum pressure drop, the parameters were optimised using multi-criterion decision-making (MCDM) optimisation techniques. Considering the rate of heat transfer, collector efficiency and drop in pressure are the objective functions, the prime ranks of the optimised variables were obtained using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) techniques. Finally, the prediction accuracy of the models and the confidence levels were evaluated and analysed through ELECTRE (ELimination Et Choix Traduisant la REalite) method to create the hypothesis of the experiment. Al 2 O 3 nanofluid with 0.1% and 0.2% volume concentrations of nanoparticle at 0.05kg/s mass flow rate was obtained as best alternatives from others and it shows good agreement between experimental analysis and optimisation techniques. While using, when compared to water, Al 2 O 3 nanofluid with 0.05kg/s containing 0.1% and 0.2% nanoparticle concentrations, the enhancements were found to be 11.25% and 14.45%, respectively, for heat transfer rate; 11.16% and 14.34%, respectively, for collector efficiency; and 22.7% and 37.7%, respectively, for pressure drop across the collector.

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.

"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.

The prime requirements of vertical handoff in the heterogeneous vehicular network are to ensure the quality of service requirements and to provide seamless connectivity to all mobile stations. In this research paper, Multiple Attribute Decision Making (MADM) classical methods are used like the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to make the handoff decisions process quickly and efficiently. Our method called ‘MADM-TOPSIS Scheme’ is the fusing of TOPSIS method with Fuzzy Logic Sets and Rules, in order to lessen the Blocking Probability. Performance results are thus compared with the classical fuzzy TOPSIS and with an approach of the base paper. Our work is able to determine properly that by reducing the blocking probability, the handoff probability will increase that will improve the network performance in the heterogeneous vehicular system. After differentiating to classical Fuzzy TOPSIS, our work scheme shows an upgrade of 0.10 and 20% with base paper.

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.

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