Offshoring Location Decision in Fuzzy Environment

Traditionally, the planning and design of road infrastructure projects relied on conventional methods. However, with technological advancements, the InfraWorks tool, integrated into BIM, has become essential for planning these projects in a realistic environment. This process begins with the evaluation of multiple route alternatives, comparing them based on technical, environmental, and economic criteria, to select the optimal option. The primary objective of this research is to replace the most flood-prone road section among those connecting the city of Sidi Belattar to RN 90, located on the left bank of the Chellif River in Algeria. To achieve this, the multi-criteria decision-making (MCDM) methods VIKOR and TOPSIS are used to evaluate and rank the different options. The analysis is based on MCDM methods, combining two complementary approaches: VIKOR (VIsekriterijumsko KOmpromisno Rangiranje) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), to rank the alternatives. The results revealed a general consistency between the two methods, designating the first alternative as the optimal solution to replace the vulnerable section of the existing route. In conclusion, this approach represents a significant advancement thanks to the integration of InfraWorks and MCDM methods, offering an innovative approach to optimize the planning and design of road infrastructure from the early stages of development.

A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach

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.

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 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.

Compliance with MARPOL Annex VI regulation 14 by ships in the Gulf of Guinea sub-region: Issues, challenges and opportunities

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.

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.

Energy is a critical factor to obtain a sustainable development for countries and governments. Selection of the most appropriate energy alternative is a completely critical and a complex decision making problem. In this paper, an integrated multi-criteria decision-making (MCDM) methodology based on type-2 fuzzy sets is proposed for selection among energy alternatives. Then a roadmap has been created for Turkey.To overcome uncertainties in decision making process, the fuzzy set theory (FST) is suggested.For this aim, two of the most known MCDM methodologies are reconsidered by using type-2 fuzzy sets.Fuzzy Analytic Hierarchy Process (FAHP) based on interval type-2 fuzzy sets is constructed and is used to obtain the weights of the criteria affecting energy alternatives. To rank the energy alternatives, the other MCDM method that is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is fuzzified by interval type-2 fuzzy sets. The proposed integrated MCDM methodology based on interval type-2 fuzzy sets is applied to obtain a road map of energy policies for Turkey.

Post-disaster temporary shelter construction involves both humanitarian and technical dimensions. Evaluating different systems requires considering multiple criteria such as rapid deployment, space efficiency, and cost. This study aims to determine the most suitable construction system for temporary shelters using multi-criteria decision-making (MCDM) methods. A literature review based on the PRISMA protocol was conducted to identify evaluation criteria, and the Analytic Hierarchy Process (AHP) was applied to determine their relative weights based on expert opinions. The consistency ratio (CR) was calculated as 0.0303, indicating reliable judgments. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank four construction systems according to five criteria. In addition, a sensitivity analysis was conducted to examine the robustness of the results under different criterion weight scenarios. The results revealed that modular systems were the most suitable alternative, followed by membrane, compact and traditional systems. Modular systems stand out for rapid installation and durability; membrane systems perform well in early disaster stages but offer limited comfort. Compact systems provide portability at higher costs, while traditional systems promote local materials but lack speed. Overall, modular systems represent the most appropriate solution, while other systems may be used complementarily depending on context.

A famous multi-criteria decision-making(MCDM) method called technique for order preference by similarity to ideal solution(TOPSIS) was often fuzzy generalized into fuzzy multi-criteria decision-making(fuzzy MCDM). In generalizing TOPSIS, fuzzy preference relation on interval-valued fuzzy numbers(IVFNs) is to find the ideal solution and anti-ideal solution for reserving lots of information. Usually, the work is complicated on computation. Practically, IVFNs is more difficult than others. In order to resolve the above tie for the decision-making problem about airport performance evaluation, we utilize TOPSIS and relative preference relation(RPR) in fuzzy MCDM. By combining RPR with TOPSIS, the performance evaluating problem of airports could be easily solved.

This experimental study examines how cutting parameters influence surface roughness (Ra), kerf width (KW), heat-affected zone (HAZ), and material removal rate (MRR) during CO 2 laser cutting of polyethylene (PE) material. The aim is to determine the optimal cutting conditions by integrating fuzzy analytic hierarchy process (AHP) and multi-criteria decision-making (MCDM) methods. Control factors (i.e. focal length, power, and cutting speed) were varied in a full factorial experimental design. Criterion weights for the responses were calculated with the help of the AHP method in fuzzy environment, resulting in weights of 0.442 for Ra, 0.102 for KW, 0.287 for HAZ, and 0.169 for MRR. These weights were integrated into MCDM methods (i.e. combined compromise solution [CoCoSo], technique for order preference by similarity to ideal solution [TOPSIS], and Vlsekriterijumska Optimizacija I Kompromisno Resenje [VIKOR]) to rank the experiments and identify the optimal parameters. The optimal cutting condition was found to be a focal length of 8 mm, power of 90 W, and cutting speed of 12 mm/s. Spearman's rank correlation coefficients were applied for analysis of the consistency among the hybrid approaches, with results indicating a very strong positive relationship between them. This comprehensive approach ensures precise and efficient laser cutting of PE, contributing valuable insights to the field of manufacturing and materials processing.

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 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.

In this paper, project portfolio selection (PPS) is considered as one of the main steps in project portfolio management (PPM). PPS which depends on different criteria such as strategic, financial, as well as project specifications is also suggested for construction contractors. Although it is difficult to consider all these criteria, the decision will be inaccurate when some of them are ignored. The most common multi-criteria decision-making (MCDM) methodologies and geographic information system (GIS) are combined as a method, by which the potential projects obtain a strategic score by the analytical hierarchy process (AHP). Then, they are ranked according to technical criteria by the technique for order preference by similarity to ideal solution (TOPSIS). The proposed approach includes three ordinal phases. First, scoring potential projects due to the company strategy is conducted by the AHP to determine strategic-aligned projects (SAPs). Then, SAPs are ranked by TOPSIS. Finally, the final GIS sheet is prepared by the obtained technical score and communal criteria. This paper provides a clear and comprehensive insight to accept or reject SAPs by form a weighted sheet in GIS. It suggests that CCs select project portfolio according to all the financial and non-financial criteria. This approach can satisfy the PPM principals and resolve some of the shortcomings related to MCDM methods by GIS capacities to reduce complexity so that the decision-makers form project portfolios easily and effectively. This approach is described in a case study.