Multi-criteria Decision-making Process Research Articles

Development of a heavy vehicle torque rod applying continuous fiber reinforced thermoplastic composite materials instead of forged steel material: Design, analysis and optimization

The torque rod is an important component of the suspension system that connects the axle to the chassis in heavy commercial vehicles. The main motivation of this study is the development of a torque rod made of (1) plus cross-section, (2) continuous carbon and glass fiber reinforced hybrid thermoplastic composites, which can replace a forged steel torque rod used in heavy vehicles, has superior mechanical properties, provides minimum cost and weight. This study aims to develop a torque rod before its production within the framework of integration with Computer Aided Design (CAD), Finite Element Analysis (FEA), and Multi-Criteria Decision Making (MCDM). In this study, the design data and the properties of the composite materials were chosen as control factors. The minimum displacement, the best mechanical properties such as tensile stress, compressive stress, torsional shear stress, maximum critical buckling load, and the minimum part weight minimum production cost per piece were selected as quality characteristics. As a result of the FEA study, considering the experimental set of the Taguchi Method L25 orthogonal array, the data on mechanical properties, weight, and production cost per piece were subjected to the MCDM process using the Entropy Weighted TOPSIS method. As a result of the MCDM study, a torque rod made of continuous fiber-reinforced thermoplastic composite material, instead of a torque rod produced by forged steel, had the highest mechanical properties produced, the weight of the torque rod can be reduced by 64.76%, and the production costs per piece can be reduced by 37.5%. This study’s findings have shown that the torque rod produced from continuous fiber-reinforced thermoplastic composite materials in a new geometry with a plus cross-section can be substituted for the torque rod produced by steel forging. Thus, it will contribute to reducing the fixed vehicle weight, especially in heavy commercial vehicles, reducing CO2 emissions, and increasing the range of the vehicles.

Development of RAMDOE: a new method for rapidly ranking alternatives with supplementary options and considering changes in criteria values

This paper delves into the development and validation of the RAMDOE method, a pioneering approach in multi-criteria decision making (MCDM) that seamlessly integrates the root assessment method (RAM) and design of experiments (DOE) techniques, addressing the inflexibility of traditional MCDM methods in accommodating adjustments in criteria ranges and the addition of new alternatives without necessitating a complete overhaul of the decision framework. Through empirical analysis, the study demonstrates the RAMDOE method's remarkable efficacy in precisely ranking alternatives, as illustrated through a practical case study focused on the selection of a supplier from a pool of seven candidates. One of the most notable aspects of the RAMDOE method lies in its capacity to formulate a regression equation that accurately captures the intricate relationship between alternative scores and criteria values, enabling decision-makers to seamlessly integrate new alternatives into the decision-making process without the cumbersome task of recalibration, thereby distinguishing it from conventional MCDM techniques such as TOPSIS (technique for order of preference by similarity to ideal solution), COPRAS (complex proportional assessment), MOORA (multiobjective optimization on the basis of ratio analysis), EDAS (evaluation based on distance from average solution) and CODAS (combinative distance-based assessment). The practical implications of these findings are profound, offering decision-makers across various domains a more efficient and adaptable framework to navigate complex decision scenarios. Particularly in contexts like supplier selection, where criteria ranges may vary significantly, the RAMDOE method provides decision-makers with a robust toolset to make informed decisions, presenting a promising avenue for addressing the dynamic nature of decision-making environments and enhancing the overall robustness and flexibility of MCDM processes in real-world applications

Spherical Fuzzy Multicriteria Decision Making for Evaluating Healthcare Service Quality of Hospitals During the Global Pandemic

AbstractThis study identifies hospitals in Iran that have demonstrated exceptional performance in service quality during the COVID-19 pandemic based on the proposed integrated multicriteria decision-making (MCDM) process. Although the coronavirus has been eradicated in most countries, occasional outbreaks of COVID-19 variants have occurred, affecting many individuals, particularly in Iran. The pandemic caused an influx of hospital visits, with people seeking treatment for various illnesses. However, the abrupt onset of the pandemic and its global impact challenged hospitals’ ability to provide timely care, leading to a noticeable decline in service quality. Identifying the top-performing hospitals is crucial for benchmarking and enhancing healthcare quality. To assess hospital service quality, the study employed a customized SERVQUAL model, which helped identify key factors that served as criteria and subcriteria for the evaluation process. The priority weights of these factors were then obtained using the spherical fuzzy analytic hierarchy process. For each SERVQUAL criterion, the hospitals were evaluated using the spherical fuzzy weighted aggregated sum product assessment method, resulting in respective rankings of the hospitals. Finally, an integrated Borda−Copeland method was utilized to generate the aggregate evaluation ranking, a feature that serves as an important departure from the literature. The contribution of this work lies in developing an integrated approach that intends to serve as a benchmark not only for hospitals in different countries but also for those confronting similar challenges and offers guidance for seeking insights from top-performing hospitals in comparable situations.

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

Fermatean fuzzy Z-analytic hierarchy process: An application to third party logistics providers

To decrease the impact of ambiguity, fuzzy sets are frequently used to decide if an element belongs to a set. Various fuzzy set extensions differ from others in how uncertainty is expressed in the definition of membership functions. This paper presents a novel extension named Fermatean fuzzy Z-numbers, introducing fuzzy restriction and fuzzy reliability functions with Fermatean fuzzy membership functions. By utilizing fuzzy Z-numbers to delineate membership degrees, considering both restriction and its reliability, in tandem with Fermatean membership functions offering an expanded value spectrum, the assessment of linguistic evaluations using Fermatean fuzzy Z-numbers can yield more pragmatic outcomes in decision evaluations amidst high uncertainty. Moreover, the interval-valued Fermatean fuzzy-Z-analytic hierarchy process is developed to explore the impact of proposed interval-valued Fermatean fuzzy-Z numbers on the multi-criteria decision-making process, demonstrated in the context of third-party logistics provider selection. Adding fuzzy reliability functions to the decision-making process improves how uncertainty is represented by considering variations in confidence levels associated with each decision, thus aiding in the clearer expression of decisions. Additionally, the proposed method's outcomes are compared with the outcomes of the current approaches.

Analysis of the Post-Cyclonic Physical Flood Susceptibility and Changes of Mangrove Forest Area Using Multi-Criteria Decision-Making Process and Geospatial Analysis in Indian Sundarbans

Tropical cyclones, one of the most extreme and destructive meteorological incidents, cause extensive damage to lives and livelihoods worldwide. This study utilized remotely sensed data along with multi-criteria decision-making, geospatial techniques, and major cyclonic events Aila, Amphan, and Yaas to identify the changes in the vulnerability of cyclone-induced floods in the 19 community development blocks of Indian Sundarbans in the years 2009–2010, 2020–2021, and 2021–2022 (the post-cyclonic timespan). The Sundarbans are a distinctive bioclimatic region located in a characteristic geographical setting along the West Bengal and Bangladesh coasts. In this area, several cyclonic storms had an impact between 2009 and 2022. Using the variables NDVI, MNDWI, NDMI, NDBI, BSI, and NDTI, Landsat 8 Operational Land Imager, Thermal Infrared Sensor, Resourcesat LISS-III, and AWiFS data were primarily utilized to map the cyclonic flood-effective zones in the research area. The findings indicated that the coastline, which was most impacted by tropical storms, has significant physical susceptibility to floods, as determined by the AHP-weighted overlay analysis. Significant positive relationships (p < 0.05, n = 19 administrative units) were observed between mangrove damage, NDFI, and physical flood susceptibility indicators. Mangrove damage increased with an increase in the flood index, and vice versa. To mitigate the consequences and impacts of the vulnerability of cyclonic events, subsequent flood occurrences, and mangrove damage in the Sundarbans, a ground-level implementation of disaster management plans proposed by the associated state government, integrated measures of cyclone forecasting, mangrove plantation, coastal conservation, flood preparedness, mitigation, and management by the Sundarban Development Board are appreciably recommended.

Assessing and enhancing the regional sustainability of electricity generation technologies in an energy-importing megaregion in China

Decision-makers are increasingly concerned about the sustainability of power generation technologies to achieve a secure and sustainable electricity supply in the future. This study aims to assess the sustainability of the eight key electricity generation technologies in the energy-importing Yangtze River Delta region of China and further enhance the regional sustainability of the electricity generation mix. We employed the multi-criteria decision-making process to rank the sustainability of the eight electricity generation technologiesfrom the perspective of various decision-makers. First, the results revealed that no technology is absolutely most sustainable. Second, the subjective findings show that hydropower is the most sustainable technology among the assessed ones, followed by nuclear and onshore wind power. Third, policymakers regard fossil fuel energy as more sustainable than investors and experts. Furthermore, local renewable diffusion and substantial electricity imports from renewable-rich areas can enhance the sustainability of the localelectricity system. In addition,an interregional electricity transmission grid with the neighbor Anhui province as the primary backup power supplier can sufficiently enhance the sustainability of the electricity system in the Yangtze River Delta region.

Material Selection for Artificial Femur Bone Using PROMETHEE-GAIA Method

Abstract When replacing bones and implants, choosing the right materials for the artificial bone and orthopedic implants is crucial to the procedure’s success. In this work, a thorough assessment of the literature was followed by a thorough and rigorous evaluation of prospective materials for prosthetic femurs using a multicriteria decision-making process known as PROMETHEE-GAIA (Preference Ranking Organization METHod for Enrichment Evaluation and Geometric Analysis for Interactive Assistance). The proposed approach was validated using a total of 12 assessment parameters, including density, tensile strength, and ultimate tensile strength, and 17 candidate materials. The significance of the chosen criteria is well described. These 17 candidate implant materials and the 12 assessment criteria were used to develop a choice matrix. Rankings over the prepared matrix were produced using the PROMETHEE-GAIA program. Ti-6Al-7Nb, Ti-6Al-4V, and ASTM F1537, Standard Specification for Wrought Cobalt-28Chromium-6Molybdenum Alloys for Surgical Implants (UNS R31537, UNS R31538, and UNS R31539), Co-Cr-W emerged as the top contenders and were demonstrated as possible materials for effective artificial femur materials because of the assessment. With a large number of pertinent criteria and a wide range of materials, this study offers a framework for the selection of implant materials. It also emphasizes how choosing materials carefully may increase the durability and efficiency of orthopedic implants.

Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making

The rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their thermophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanofluids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparticle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques, TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al2O3-based nanofluid, around 90 % of the optimal points were within 40–50 °C. Conversely, for nanofluids containing CeO2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO2/ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications.

Agricultural Implement Industry Using WPM Method

Agricultural implements are agricultural Human labor in activities that Reduce field crop yield Tools that can be used to improve is reapers, Traction, disc harrows, Cultivators, seed drills, Harrows, Spades, Baggage, Plows, and other agricultural Tools are very common. In Modern Agricultural Practices Agricultural implements play an important role play These are commercial and Widespread in organic farming are used. This Tools are for sowing, field preparation, Planting, threshing, and irrigation and are used for harvesting. Agricultural machinery industry or Agricultural engineering is a profession as part of the industry, it is in agriculture or other agriculture used tractors, Agricultural machinery and Manufacturing agricultural implements maintain. This branch is mechanically Considered part of the profession.Agricultural implements are agricultural to carry out procedures The necessary tools are: In today's farming operations Many agricultural implements are used. Agriculture means crops and livestock Production, Aquaculture, Aquaculture and food and food Forests for non-food products Includes. Seated man Agriculture at the Rise of Civilization A major development was Raised by this Cultivation of species is food Generated surpluses, which helped people live in cities. Humans are at least 105,000 grains years ago Although started to collect, New farmers are about 11,500 years ago They started planting. Sheep Goats, pigs, and cattle About 10,000 years ago were raised. World's lowest Plants are native to 11 regions and Cultivated as fodder. twentieth in century, large-scale Based on monocultures Industrial agriculture with dominated agricultural production.The weighted product method is a multi-criteria decision-making process is there are many alternatives, and based on several criteria we must determine the best alternative.DuPont India, Rallis India Limited, Nuziveedu Seeds Limited, Lemken India Agro Equipments Private Limited, Advanta Limited.Technical capability, Product quality capability, Delivery capability, Financial/cost capability from the result, it is seen that Lemken India Agro Equipments Private Limited is got the first rank where as Nuziveedu Seeds Limited is having the lowest rank

Addressing a decision problem through a bipolar Pythagorean fuzzy approach: A novel methodology applied to digital marketing

Decision-making in real-world scenarios faces uncertainty. Fuzzy theory has been a means to represent such uncertainty. In this study, we propose an approach that incorporates bipolarity into multi-criteria decision-making processes applied to digital marketing. The proposal considers both the positive and negative dimensions of data, leading to better-informed decisions. Our contribution integrates bipolarity into Pythagorean fuzzy matrices, a framework that broadens the utility of bipolar fuzzy theory. Through computational experimentation, we identify the most effective strategy for digital marketing platforms. When compared to existing techniques, our approach shows advantages, underlining its potential to improve decision-making in uncertain scenarios and offering insights for businesses that refine their digital marketing strategies.

Fuzzy Predictive Modeling for the Hierarchical Structure of Strategic Innovation Management

Total interpretive structural modeling (TISM) constructs a hierarchical structure with interpretations of nodes and interrelationship links. While TISM performs qualitative research when conceptual modeling is preferred, it lacks quantitative capabilities at present. Conducting a quantitative study on TISM is difficult, especially when the model elements are qualitative. TISM enables the development of a predictive model based on fuzzy set theory. The proposed methodology benefits from using surrogate variables to quantify qualitative variables and fuzzy set theory to simulate the hierarchical model. The proposed method is exercised to conduct a simulation-based study on the existing hierarchical model of strategic innovation to generate multiple scenarios. Additionally, through simulation and scenario building, relevant decisions can be made to enhance productivity and performance in particular functional areas. In this article, multiple scenarios generated by the simulation provide a comprehensive view of the system's behavior in new ventures, which aids in the multicriteria decision-making process.

The selection of washing machine programs with fuzzy dematel and moora-ratio multi-criteria decision-making methods considering environmental and cost criteria

The washing machine is the prevalent white household equipment in contemporary society. These machines provide consumers with a range of program options that encompass several variables, including temperature and detergent type. Nevertheless, the selection made by individual customers about the washing machine program they opt for carries substantial environmental consequences during the use stage of textile products. According to studies on the life cycle of clothes, it has been established that the use stage, following the extraction of raw materials, exerts the most substantial influence on environmental impacts. The objective of this research is to assess the washing machine programs provided by the manufacturer through the application of a comprehensive systematic approach for analysis. The evaluation of scenarios for washing machine programs was conducted using the MOORA-Ratio multi-criteria decision-making process. This evaluation considered various parameters, including environmental impact and cost. The life cycle assessment methodology was employed to quantify the environmental impact of the specified criteria. Based on the comprehensive study conducted by integrating criteria across numerous dimensions, it has been determined that the most favorable scenario wass scenario 1, which was developed for the Cotton 20 C program. The primary objective of this research endeavor is to fill a significant need in the current body of literature by undertaking a comprehensive review of washing machine programs that have not been previously recorded. This study employs a comprehensive methodology to investigate the environmental and economic implications linked to these activities, with the objective of delivering significant insights to producers and users.

Urban flood hazard assessment in Awka metropolis using multi-criteria decision process

Urban flooding is an escalating global threat, and Awka Metropolis in Anambra state Nigeria is no exception. The city's rapid urbanization and inadequate infrastructure have left it increasingly vulnerable to flooding, exacerbated by the impacts of climate change. Anticipated flood effects include disruption of daily life, infrastructure damage, and threats to public safety, necessitating urgent attention. This study presents a comprehensive assessment of urban flood hazards in Awka Metropolis, utilizing an integrated approach combining the Analytical Hierarchy Process (AHP) and Flood Hazard Index (FHI) to gauge the city's flood vulnerability. The primary objectives were to identify key geophysical factors contributing to flooding, categorize these factors by their vulnerability, determine their reliability indices, delineate flood-prone zones, and generate a spatial flood extent map. The methodology encompassed data acquisition, pre-processing, Flood Modeling (evaluating slope, Elevation, Drainage Network, and flow accumulation), the application of Analytical Hierarchy Process, and the utilization of the Flood Hazard Index. The results classified Awka Metropolis into three flood risk zones: high (837.84 hectares, 14.70%), moderate (2182.65 hectares, 38.30%), and low (2678.45 hectares, 46.99%). These findings provide indispensable insights into the distribution of flood hazards across the city, enabling urban planners and authorities to strategically allocate resources, prioritize mitigation initiatives, and formulate targeted flood risk reduction strategies. Importantly, the incorporation of stakeholder input enhances the study's practical relevance and applicability in real-world urban planning and disaster management scenarios. In conclusion, this research offers a robust and multidimensional framework for assessing urban flood hazards in Awka Metropolis. The synergy of the Analytical Hierarchy Process and Flood Hazard Index equips urban planners, policymakers, and disaster management agencies to proactively tackle the mounting urban flooding challenges. The study's outcomes contribute to the development of sustainable flood risk reduction measures, ultimately enhancing the resilience of Awka Metropolis to future flood events.

A novel multicriteria decision-making process for selecting spot welding robot with removal effects of criteria techniques

A novel multicriteria decision-making process for selecting spot welding robot with removal effects of criteria techniques

Prioritizing the antecedents of employer branding using fuzzy AHP: an extent analysis approach

Purpose One of the most decisive objectives of human resource management (HRM), essential for business performance, is to attract, maintain and engage a qualified workforce. This study aims to identify and prioritize the antecedents of employer branding, which are considered extremely important by potential applicants when they choose to join a particular employer. Design/methodology/approach The authors have adopted a popular multicriteria decision-making technique fuzzy analytical hierarchy process, to prioritize the identified antecedents. The modified Delphi techniques with the 22 experts have been conducted to validate the identified antecedents. Structured questionnaires were discussed, and their reliability was assessed using the expert’s opinion. Findings Results of this study indicate that potential applicants consider career advancement opportunities as the most important enabler that persuades them to join a particular employer. Future employees are also motivated to join if they are offered attractive compensation packages, which are distinct from competitors. Furthermore, corporate social responsibility is evolving as an antecedent of employer branding as potential candidates are attracted to employers who are socially responsible. Practical implications Employer branding can serve as a strategic HRM technique for any business seeking to attract, recruit, retain and involve outstanding employees, as the success of the organization is determined by how its image is built, communicated and promoted. This study provides insights for HR managers and practitioners who can think of developing an effective employer brand communication that offers a distinct and in-imitable image and reputation as an employer of choice. Originality/value This study is unique, as it offers meaningful visions to HR practitioners and experts for designing employer branding strategies for attracting potential applicants to join their organizations.

Some T-spherical fuzzy dombi hamy mean operators and their applications to multi-criteria group decision-making process

Renewable energy sources play an influential role in the world’s climate and reduce the rate of harmful gasses such as carbon dioxide, methane, nitrous oxide, and many other greenhouse gasses that contribute to global warming. The theoretical concept of the T-spherical fuzzy (T-SF) set (T-SFS) is the most suitable model to evaluate energy resources under uncertainty. This article illustrates appropriate operations based on Dombi triangular norm and t-conorm. We derived a series of new aggregation approaches, such as T-SF Dombi Hamy mean (T-SFDHM) and T-SF weighted Dombi Hamy Mean (T-SFDWHM) operators. Further authors illustrated a list of new approaches such as T-SF Dual Dombi Hamy mean (T-SFDDHM), and T-SF Dombi weighted Dual Hamy mean (T-SFDWDHM) operators. Some exceptional cases and desirable properties of our derived approaches are also studied. We illustrate an application of renewable energy resources to be evaluated using a multi-attribute group decision-making (MAGDM) method. A case study was also studied to choose appropriate energy resources using our proposed methodology of the T-SFDWHM and T-SFDWDHM operators. To show the effectiveness and validity of our current methods, we compared the existing results with currently developed aggregation operators (AOs).

MCDM-AHP and PROMETHEE methods integrated for base service strategy vendor evaluation and selection

Business competition is very important in controlling product-savvy customers. Strong basic service techniques will be the main factor that binds vendors as the final destination in the supply chain through the strength of business network processes. This research aims to create a strategic basis for evaluating and selecting vendors through the integration process services of the multi-criteria decision-making method analytic hierarchy process (MCDM-AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) methods. Empirical studies show how this approach can provide optimal decision support for the vendor evaluation and selection process. Eight different types of criteria are required in its apps and must be realized as a barometer of the strategic basis for selecting vendors so that business processes are of high quality. These criteria include quality of goods, payment methods, payment terms, minimum transactions, discounts, delivery times, inventory, and service. The optimal weight for each criterion will be determined based on its importance to the synthesis process and its feasibility tested using mathematical algebra matrices and expert choice apps. Decision-making was based on the results of ranking evaluation of selected vendors through the development of 342 preference matrices, ten vendors were deemed worthy of acceptance and nine other vendors were rejected.

A novel MCDM approach for design concept evaluation based on interval-valued picture fuzzy sets.

The assessment of design concepts presents an efficient and effective strategy for businesses to strengthen their competitive edge and introduce market-worthy products. The widely accepted viewpoint acknowledges this as a intricate multi-criteria decision-making (MCDM) approach, involving a multitude of evaluative criteria and a significant amount of data that is frequently ambiguously defined and subjectively influenced. In order to tackle the problems of uncertainty and fuzziness in design concept evaluation, our research creatively combines interval-valued picture fuzzy set (IVPFS) with an MCDM process of design concept evaluation. Firstly, this study draws on the existing relevant literature and the experience of decision makers to identify some important criteria and corresponding sub-criteria and form a scientific evaluation indicator system. We then introduce the essential operational concepts of interval-valued picture fuzzy numbers (IVPFNs) and the interval-valued picture fuzzy ordered weighted interactive averaging (IVPFOWIA) operator. Thirdly, an entropy weighting method based on IVPFS is proposed in this research to calculate the weights of criteria and sub-criteria, and based on this, an integrated IVPF decision matrix is further constructed based on the presented IVPFOWIA operator. Finally, the best design concept alternative is selected by applying the extended TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach with IVPFS. The IVPFS combined with improved MCDM method have been proven to be superior in complex and uncertain decision-making situations through experiments and comparative assessments. The information ambiguity in the evaluation of design concept is well characterized by our augmentation based on IVPFS.

Generation of material handling system alternatives: A constraints satisfaction problem approach

In the context of Industry 4.0, the design of efficient Material Handling Systems (MHS) plays a critical role in optimizing industrial operations and enhancing productivity. The integration of advanced technologies, automation, and complex systems has revolutionized industrial processes, emphasizing the need for defining coherent MHS alternatives prior to the deployment of a specific solution. This paper presents an integrated MHS alternative generation approach that combines MHE allocation, fleet sizing, and key performance indicator (KPI)-based evaluation to enhance MHS. The proposed approach deploys a Constraint Satisfaction Problem (CSP) framework, which defines the conditions that must be satisfied by the values assigned to the MHE allocation and fleet sizing variables. The constraints capture the physical and operational characteristics of MHE and MHS, ensuring that the generated alternatives adhere to the specific requirements and limitations of the system. Once a set of feasible MHS alternatives complying with various constraints is established, further assessments can be carried out to gauge the dynamic behavior of these systems using methods like Discrete Event Simulation. The paper emphasizes the importance of incorporating a broader range of data categories, such as layout constraints, product characteristics, and Industry 4.0 challenges, to enhance the effectiveness and accuracy of the MHS design process. To evaluate the generated alternatives, a set of KPIs is proposed, encompassing operational, economic, environmental, safety, and security aspects. These KPIs enable decision-makers to assess alternative performance across multiple metrics, aiding in the identification of relevant solutions aligned with company objectives. The incorporation of KPIs provides a pre-evaluation of MHS alternatives, streamlining subsequent simulation and multicriteria decision-making processes. This research acknowledges the importance of alternative generation as an integral part of the broader MHS design problem.