Forklift selection by multi-criteria decision-making methods

Selection of discrete multiple criteria decision making methods in the presence of risk and uncertainty

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.

Background: Uncertainty is a common factor in every real-life decision-making problem. Possibility theory is one uncertainty theory in Fuzzy sets (FS). The possibility-based decision-making under a fuzzy environment is a significant multi-criteria decision-making (MCDM) method. Methods: A bipolar FS is an extension of a fuzzy set. With the bipolarity concept, we can handle both positive and negative thoughts. In this study, we have provided a possibility mean of a bipolar fuzzy number. We have developed a ranking method for bipolar fuzzy numbers using this pos-sibility concept. A novel possibility MCDM method is suggested for solving the water resources management (WRM) in the Nagpur area, Maharashtra State, India. Results: The MCDM technique is an effective tool for solving WRM problems in an area. Many uncertainties and bipolarities occur together in Nagpur water resources. WRM technique with fuzzy is one approach that can be used to solve the area's water problem. We have used the pro-posed MCDM to address the water-related issues of this district. With this proposed MCDM method, numerically, we employed water resource problems under a bipolar fuzzy environ-ment. Conclusion: The Nagpur area is covered by Basaltic rock and faces water shortage. The district is experiencing severe water shortages. Groundwater, surface water, and rainfall are three water resources considered as alternatives. According to the proposed MCDM technique in Nagpur district, Groundwater is the best water source from three flanks: quality of water, affordability, and availability.

A shortage of land for waste disposal is one of the serious problems that faces urban areas in developing countries. The Sulaimaniyah Governorate, located in the north of Iraq, is one of the major cities in the Kurdistan Region of the country, covering an area of 2400 km2 with a population of 856,990 in 2016. Currently, there is no landfill site in the study area that meets scientific and environmental criteria, and inappropriate solid waste dumping is causing negative environmental impacts. The process of landfill site selection is considered a complex process and is restricted by numerous factors and regulations. This paper proposes multi-criteria decision-making (MCDM) methods in a model for landfill site decision. The model assumes the input of two groups of factors that need to satisfy the optimal values of weight coefficients. These groups of constants are natural factors and artificial factors, and they included thirteen selected criteria: slope, geology, land use, urban area, villages, rivers, groundwater, slope, elevation, soil, geology, road, oil and gas, land use, archaeology and power lines. The criteria were used in the geographic information system (GIS), which has a high capacity to process and analyze various data. In addition, multi-criteria decision-making (MCDM) methods followed by the weighted linear combination (WLC) method were used to derive criteria weightings using a matrix of pair-wise comparison. Finally, all the multi criteria decision methods were combined to obtain an intersection of the suitability index map for candidate landfill sites. Seven appropriate sites for landfill were suggested, all of which satisfied the scientific and environmental criteria which were adopted in this study.

A machine learning-integrated multi-criteria decision-making approach based on consensus for selection of energy storage locations

This study aims to analyze the research performance of 38 OECD countries in the last 10 years. For this purpose, the research performance of these countries was analyzed using multi-criteria decision-making (MCDM) methods with the productivity and publication impact data of 38 countries. The analyses were conducted both normal data and also with adjusted data based on the population and income level of the countries. Firstly, the importance level of research performance criteria was determined by three different MCDM methods. Using the weight values obtained from these methods, countries were ranked by three different MCDM methods used for ranking purposes. According to the results of the study, the publications in the highest cited 1% were determined as the most important and distinctive criterion. Secondly, the criterion expressing the relative impact of publications on the world was determined as the second most important criterion. In the analyses conducted without normalizing the data, USA showed the highest research performance. In the analyses conducted by considering the population of the countries, Switzerland showed the highest research performance. In the analyses conducted by taking into account the income level of the countries, Estonia has the highest research performance. As R&D expenditures, development level of countries and international collaboration increased, research performance of countries increased. However, with the increase in domestic collaboration, there has been a decrease in research performance. In addition, this research has shown how to use MCDM in the measurement of research performance by using objective methods with different backgrounds.

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.

Multi-criteria decision-making (MCDM) methods have gained increased attention in sustainable engineering, where complex decision-making problems require consideration of multiple criteria and stakeholder perspectives. This review paper provides a comprehensive overview of the different MCDM methods, their applications in sustainable engineering, and their strengths and weaknesses. The paper discusses the concept of sustainable engineering, its principles, and the different areas where MCDM methods have been applied, including energy, manufacturing, transportation, and environmental engineering. Case studies of real-world applications are presented and analyzed, highlighting the main findings and implications for engineering practice. Finally, the challenges and limitations of MCDM methods in sustainable engineering are discussed, and future research directions are proposed. This review contributes to the understanding of the role of MCDM methods in sustainable engineering and provides guidance for researchers and practitioners.

Financial performance evaluation provides information about a firm’s liquidity position, profitability, capital structure and asset utilization. Financial performance evaluation is considered as a multi-criteria decision making (MCDM) problem, as it is a multidimensional concept that is realized by bringing together multiple indicators. This study is aimed to evaluate the financial performance of the Fortune 500 companies by using the integrated data-driven weighting system (IDDWS) – combined compromise solution (CoCoSo) approach. The criteria weights were calculated with the IDDWS and the companies were ranked by the CoCoSo method. In the last stage, a three-stage sensitivity analysis was performed to test the robustness of the model. In the first stage, 15 scenarios were defined by changing the criteria weights. In the second stage, the rankings of the CoCoSo method were compared with the other MCDM methods [range of value (ROV), proximity indexed value (PIV), complex proportional assessment (COPRAS), Biswas and Saha’s method]. In the third stage, a sensitivity analysis was conducted under five different scenarios based on different δ parameters. It was determined that the rankings obtained as a result of the sensitivity analysis show small deviations and except for a few companies, the ranking of most companies remained the same. The results show that the proposed model is suitable for measuring financial performance and Alphabet performs best. The suitability of the proposed model for measuring financial performance was tested for the first time. It is thought that the comparative use of many MCDM methods through a comprehensive sensitivity analysis will contribute to the literature.

Multi-criteria decision-making models for smart city ranking: Evidence from the Pearl River Delta region, China

Addressing uncertainty is crucial when it comes to solving the problem of selecting cleaner suppliers. Uncertainty in this context stems from various sources, such as the lack of information regarding the specific details of the criteria and the absence of information about suppliers' performance in meeting the green/sustainable criteria. Multi-criteria decision-making (MCDM) methods are powerful tools for resolving cleaner supplier selection (CSS) problems. One common limitation faced by MCDM methods is their difficulty in handling uncertainty. This requires the inclusion of additional tools such as fuzzy logic, rough sets, and similar techniques. Nevertheless, their utilization introduces a certain level of risk due to fundamental disparities encompassing distinct scales, functions, operations, and philosophies. This paper outlines various scenarios involving a recently developed MCDM method known as rate-weight connected vectors processor (RWCVP), specifically Type I, to tackle these challenges within the context of CSS. By utilizing the computation of three distinct triangle areas established between two vectors of rate and weight, the original RWCVP is extended to four scenarios including 1. Solving fuzzy CSS; 2. Solving CSS problems with uncertain weights; 3. Solving CSS problems with uncertain rates; 4. Solving CSS with uncertain weights and rates. The new methods are applied to three real-world examples of CSS problems in which Supplier 1 with (Si˜) = 2.656, Supplier 4 with (Si˜) = 17.518, and Supplier 1 with (Si˜) = 1.007 are selected as the best suppliers for the first, second, and third examples, respectively. Employing Zakeri-Bratvold rank correlation; Zakeri-Bratvold what-if analysis, the obtained rankings underwent a comprehensive examination. The findings demonstrated that RWCVP method was insusceptible to rank reversals, exhibited distinct behavior in generating rankings compared to other MCDM methods, and provided reliable results.

Comprehensively examining costs and benefits in supplier selection makes the right choice challenging Globalization increases alternatives, yet selecting the most appropriate remains difficult. Therefore, the use of scientific methods is critical when choosing among alternatives. Such methods not only facilitate the decision-making process but also enable more accurate and reliable outcomes. In the public sector, outsourcing supplier selection is typically conducted through a tender-based process, where cost is primary; however, relying solely on it can lead to suboptimal decisions. Public procurement processes proceed by identifying needs, making announcements, and inviting companies to participate in the tender. Multi-Criteria Decision-Making (MCDM) methods are commonly used tools for addressing complex problems with multiple factors, such as supplier selection. MCDM methods allow decision makers to evaluate each criterion for all alternatives and determine the option that best meets the desired requirements. Furthermore, techniques such as fuzzy logic within MCDM methods go beyond numerical data and enable decision makers to evaluate the criteria and alternatives with more understandable linguistic expressions. Although supplier selection problems are frequently examined using Fuzzy AHP and Fuzzy TOPSIS methods, studies specifically focusing on the public sector remain limited. This study addresses the problem of selecting a cleaning company. It integrates Fuzzy AHP and Fuzzy TOPSIS, aiming first to determine criteria weights using Fuzzy AHP and then to identify the most suitable supplier via Fuzzy TOPSIS. The study was implemented as an alternative method to tender-based outsourcing company selection in the public sector and is deemed beneficial.

Supply chain management begins with supplier evaluation and selection. The supplier selection deals with various criteria with different contexts which makes it a complex multi-criteria decision-making (MCDM) method. In this paper, a novel MCDM method, called the alternative ranking process by alternatives’ stability scores (ARPASS), is proposed to solve supplier selection problems. ARPASS considers each alternative as a system that is constructed on integrated components. To perform properly, a system requires high integrity and stability. ARPASS utilizes the stability of alternatives as an effective element for ranking the alternatives. The ARPASS is developed in two forms, ARPASS and ARPASS*. The new method utilizes standard deviations and Shannon’s entropy to compute the alternatives’ stabilities. In this paper, in addition to the new MCDM methods, a new method called the grey equilibrium product (GEP) is introduced to convert grey linguistic variables into crisp values, using decision makers’ subjective perceptions and judgments. To highlight and validate the novel methods’ performance, they are applied to two sustainable supplier selection problems. For evaluation of the reliability of ARPASS and ARPASS*, their results were compared with the results of the popular MCDM methods. We compared the methods in terms of calculation time, simplicity, transparency, and information type.

The use of multi-criteria decision-making methods in the integrated assessment of climate change: implications for IA practitioners

Many problems such as delamination, cracking, fiber tearing, ovality, and surface roughness are encountered in the drilling of glass-fiber-reinforced composite (GFRP) materials. In this study, the percentage of multi-walled carbon nano tube (MWCNT), cutting tool type, feed rate, and cutting speed were selected as control factors during the drilling of MWCNT-reinforced GFRP nanocomposites. The quality characteristics of the drilling process were determined as surface roughness, delamination, torque, and thrust force. The experiments were carried out in accordance with the Taguchi L27 orthogonal array. The lowest values obtained because of the experiments were Ra = 4.95 µm, Dm = 1.099, T = 14.78 N, and F = 44.24 N, respectively. However, since each of these outputs were obtained from different experimental trials, different multi-criteria decision-making (MCDM) methods were used to optimize all outputs at the same time. First, the criteria were weighted using the fuzzy AHP method, and then the outputs were optimized using multi-criteria decision-making methods (i.e., GRA, WASPAS and VIKOR). Very close optimal ranking was obtained in all three methods. The best results were obtained for Ra = 4.86 µm, Dm = 1.13, T = 55.57 N, and F = 48.00 N. In the next step, the performance values obtained from each MCDM method were re-optimized using the Taguchi S/N ratio method. By comparing between these models, a single optimal condition for drilling is proposed. Accordingly, A2B3C1D1 (Ra = 4.86 µm, Dm = 1.10, T = 17.47 N and F = 48.33 N) for FAHP-GRA and FAHP-WASPAS and A2B3C2D2 (Ra = 5.02 µm, Dm = 1.09, T = 37.19 N and F = 45.01 N) for FAHP-VIKOR were determined as the best performing experiments. Finally, validation tests were conducted to compare the performance of the experiments. As a result, the FAHP-GRA and FAHP-WASPAS optimization with Taguchi S/N gave an unweighted improvement of 82.9% and a weighted improvement of 10.04% compared to the results of the experiment with MCDM. Compared to the results of the experiments with MCDM, S/N FAHP-VIKOR provided an unweighted improvement of 52.75% and a weighted improvement of 8.19%. According to the results obtained, for this study, FAHP-GRA and FAHP-WASPAS are more effective optimization methods than FAHP-VIKOR.