Complex Proportional Assessment Research Articles

Rethinking and redesigning storage, packaging and distribution practices for food supply chain

PurposeThe study aims to add to the existing literature on food supply chains by specifically taking into the redesigning of the alignment of storage, packaging and distribution practices in the modern complex supply chain. The redesign of the food supply chain’s storage, distribution and packaging is a transformative endeavor ultimately aimed at enhancing efficiency, sustainability and reliability.Design/methodology/approachIn order to identify, classify and prioritize the main challenges, this study conducted an extensive analysis of the literature and experts’ opinions in the areas of academia, information technology and the food supply chain (FSC) using combined compromise solution method (CoCoSo) and complex proportional assessment (COPRAS).FindingsThe top three classes of key indicators revealed in this study are dynamic route optimization and on-demand delivery pods (RD4), implementation of active packaging with nanotechnology (RP3) and collaborative last-mile (RD2). The findings reveal that dynamic route optimization and on-demand delivery pods (RD4) and collaborative last-mile (RD2) are maintaining a balance between collaborative delivery networks through route optimization which is a very discussable theme in recent literature.Originality/valueThe research provides fresh insights into how perishable food shelf life parameters and the use of distribution networks within the short supply chain can be taken into consideration when redesigning the storage, packaging and distribution system for food supply chains.

Interval-valued q-rung orthopair fuzzy complex proportional assessment-based approach and its application for evaluating the factors of Blockchain Technology in various domains

Blockchain technology (BT) is a digitally decentralized, distributed and public ledger, which considers a secure and viable solution for storing and accessing the record transactions in a public or private peer-to-peer network and assures a smart world of automation of complex services. This paper aims to evaluate the factors persuading the BT adoption and also to assess the possible application areas for the adoption of BT. Implementation of the BT process considers multiple criteria and alternatives with uncertain information; therefore, it can be considered as an uncertain multi-criteria decision-making problem. In this context, a hybrid approach is proposed based on the criteria interaction through inter-criteria correlation (CRITIC) and the complex proportional assessment (COPRAS) methods with interval-valued q-rung orthopair fuzzy set (IVq-ROFS) named as the “IVq-ROF-CRITIC-COPRAS” framework. In this approach, the significance values of the factors are computed through novel score function-based CRITIC model, whereas the CRITIC-based COPRAS model is employed to rank different domains/application areas for the adoption of BT under IVq-ROFSs environment. In this regard, a novel score function is introduced with its desirable characteristics. Further, the developed model is executed on a case study of BT adoption considering 14 factors, which confirms the efficiency and applicability of introduced approach. Based on the results, the healthcare BT alternative is most appropriate among the others. To evaluate its permanence, sensitivity analysis with regard to various ratings of decision strategic coefficient is presented under IVq-ROFS context. The strength of the developed approach is emphasized by comparing it with some of the former models under the context of IVq-ROFSs. The proposed extension of the COPRAS approach can be utilized to solve other complex group decision-making problems with uncertainties.

Intensify the hindrance to gas entrapment on the construction of Al 5356 thin-walled structure by tuning the WAAM process parameters

Abstract Compared to other metallic additive manufacturing methods, Wire Arc Additive Manufacturing (WAAM) has a number of advantages, such as less equipment capital required and more material composition flexibility. However, uneven welding and feed rates, as well as inadequate gas flow, can result in flaws such oxidation, gas entrapment, and humping. This study aims to reduce gas entrapment, maximize tensile strength, and reduced elastic modulus of the WAAM Al5356 wall by optimizing gas flow rate (13, 16 and 19 l min−1) in conjunction with welding and feed rates. The study highlighted gas flow rate as the most important component in pore formation and used the Entropy approach in conjunction with the COmplex PRoportional ASsessment (COPRAS) tool to identify ideal settings. The reduction in gas entrapment to 0.02%, as shown in the confirmation studies, resulted in a 33.9% rise in tensile strength and a 64.7% rise in elastic modulus. To verify these ideal parameters, elastic modulus mapping was done on the printed WAAM Al5356 wall. Moreover, the damage processes connected to gas entrapment and humping development were examined using fractography. Consequently, the research determined the ideal conditions to generate a multi-layer structure free of defects, improving its practicality in aerospace and automotive sectors.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

An expert system based decision-making framework for benchmarking industry in sustainable manufacturing

PurposeThe purpose of this study is to evaluate and enhance sustainable manufacturing practices across various industries, focusing on environmental, economic and social dimensions, to promote a comprehensive understanding and implementation of sustainability, thereby improving overall industry performance and fostering long-term ecological and economic health.Design/methodology/approachThe study uses multi-criteria decision-making-multivariate analysis technique to examine sustainable manufacturing practices (SMPs) in the Indian manufacturing sector. It identifies 11 SMP criteria through literature review and expert recommendations. Data are collected through questionnaires, expert committees and interviews. The study focuses on four key industries: automobile, steel, textile and plastic. Techniques like principal component analysis (PCA), technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) are used to rank and assess performance.FindingsThe Indian automobile industry has shown the most effective SMPs compared to steel, textile and plastic sectors. The automobile sector is the benchmark for sustainable measures, emphasizing the importance of green practices for environmental, social and economic performance. Recommendations extend beyond the automobile sector to cement, electronics and construction.Practical implicationsThe research emphasizes the importance of SMPs across various industries, focusing on economic, environmental and social considerations. It advocates for a holistic approach that enhances resource efficiency and minimizes ecological footprint.Originality/valueThe study uses ranking methods like PCA-integrated TOPSIS and COPRAS to evaluate performance in different industries, focusing on the benchmarked automobile sector. The research offers valuable insights and advocates for the widespread adoption of sustainable policies beyond the studied sectors.

Optimizing green hydrogen strategies in Tunisia: A combined SWOT-MCDM approach

Tunisia's rapid industrial expansion and population growth have created a pressing energy deficit, despite the country's significant, yet largely untapped, renewable energy potential. This study addressed this challenge by developing a comprehensive framework to identify and evaluate strategies for promoting green hydrogen production from renewable energy sources in Tunisia. A Strength, Weakness, Opportunity, and Threat (SWOT) analysis, incorporating social, economic, and environmental dimensions, was conducted to formulate potential solutions. The Step-wise Weight Assessment Ratio Analysis (SWARA) method facilitated the weighting of SWOT factors and sub-factors. Subsequently, a multi-criteria decision-making approach, employing the gray technique for order preference by similarity to ideal solution (TOPSIS-G) method (validated by gray additive ratio assessment (ARAS-G), gray complex proportional assessment (COPRAS-G), and gray multi-objective optimization by ratio analysis (MOORA-G), was used to rank the identified strategies.The SWOT analysis revealed "Strengths" as the most influential factor with a relative weight of 47.3 %, followed by "Weaknesses" (26.5 %), "Threats" (15.6 %), and "Opportunities" (10.6 %). Specifically, experts emphasized Tunisia's renewable energy potential (21.89 %) and robust power system (12.11 %) as primary strengths. Conversely, high investment costs (11.2 %) and political instability (7.77 %) posed substantial threat. Positive socio-economic impacts represented a key opportunity with a score of 5.2 %. As for the strategies prioritizing criteria, production cost ranked first with a score of 13.5 %, followed by environmental impact (12.8 %), renewable energy potential (12.0 %), and mitigation costs (11.3 %). The gray TOPSIS analysis identified two key strategies: leveraging Tunisia's wind and solar resources and fostering regional cooperation for project implementation. The robustness of these strategies is confirmed by the strong correlation between TOPSIS-G, ARAS-G, COPRAS-G, and MOORA-G results. Overall, the study provides a comprehensive roadmap and expert-informed decision-support tools, offering valuable insights for policymakers, investors, and stakeholders in Tunisia and other emerging economies facing similar energy challenges.

Analysis of the Selection of the Best Household Ceramics Using the Complex Proportional Assessment (COPRAS) Method

Rapid advances in communication and information technology due to globalization have had a significant impact on a number of industries, including the industrial sector. The industry is taking great advantage of the capabilities of this technology to search, store, distribute and present information. The ceramic sector in Indonesia looks increasingly promising every year. One type of building material that functions to cover the floor and beautify its appearance is ceramic. When choosing ceramics, consumers become confused because of the availability of various brands (vendors) with different themes and quality. When deciding on product quality, a decision support system can be implemented to offer a structured evaluation that assists stakeholders in the business and consumers in assessing high-quality ceramic options. DSS The complex proportional assessment method, or COPRAS, is used in system design. In improving the accuracy and efficiency of decision making, the COPRAS approach can evaluate several options and estimate them based on their utility level when attribute values ??are expressed in intervals. Based on the findings of this research, the application of the COPRAS method in the decision-making process to determine the best household ceramics can be used in selecting the best household ceramics by collecting data on ceramic criteria and the alternative used is the type of ceramic. The weights obtained for each criterion are then normalized which are then used to determine the Ui for each alternative, so that based on the results of this research the best household ceramics are obtained, namely Redhorse type ceramics with a Ui value of 100%, Fortuna type with a Ui value of 99.27%. , Prato type with a Ui value of 98.82%, Crystal type with a Ui value of 98.71%, Mulia type with a Ui value of 88.50%, Vancouver type with a Ui value of 88.24%, Murano type with a Ui value of 84.97% and the Virginia type with a Ui value of 79.77%.

Investigating cross-docking criteria for raw materials with hybrid MCDM

The cross-docking process involves unloading raw materials from incoming vehicles and immediately loading them onto outgoing vehicles. This can greatly improve supply chain operations and expedite transportation to the factory. By minimizing warehouse storage expenses, space requirements, and inventory management, the process can be streamlined effectively. In industries where production profiles need to be responsive to market demands, the location of the cross-docking facility and transportation planning are crucial. This article aims to investigate the criteria for selecting a cross-docking location, using a case study of Arta Profile Company in Ardabil. The research population includes all industrial engineers and production planners in the company, with 10 industry experts selected for this study. The analytic network process (ANP) and grey complex proportional assessment (COPRAS-G) methods were used to compare and prioritize cross-docking locations. Additionally, the fuzzy DEMATEL method was used to determine the strength of connections between different locations. The COPRAS-G approach helps decision-makers make informed decisions by evaluating different options and their importance. The industry experts identified 28 criteria for this study. The results from the network analysis method showed that the most important criterion is the vehicle routing problem (VRP), with a value of 0.46. According to the ANP technique, strategic decision-making is the best option. The fuzzy DEMATEL method revealed that distribution centers are the most important factor, followed by reduced delivery/distribution operations, and finally, the cost of late or early delivery.

ROC and COPRAS Algorithms in a Decision Support System for Employee Career Assessment

PT. Union Confectionery, a long-established company in the confectionery production industry, had encountered significant challenges in managing employee career progression, including transfers, demotions, and promotions. The primary issue faced was the difficulty in conducting objective employee performance assessments and determining the appropriate criteria for career development. These assessments involved various complex factors such as work experience, discipline, education level, age, job performance, attendance, teamwork, and work ethics. To address these challenges, this study proposed the implementation of a web-based Decision Support System (DSS) utilizing the Rank Order Centroid (ROC) method for criteria weighting and the Complex Proportional Assessment (COPRAS) method for ranking decisions. The implementation of this system was expected to assist the management of PT. Union Confectionery in making more accurate and transparent decisions, while also improving employee satisfaction and operational efficiency. This research also compared previous studies that used similar methods for performance assessment, albeit with a narrower focus, specifically at the supervisory level. The findings from this study were anticipated to provide a comprehensive and innovative solution for employee career management across all levels of PT. Union Confectionery’s organization, as well as contribute to the development of more effective human resource management practices.

A Hybrid Method for Identifying Critical Failure Modes in a Ball Mill

The reliability of a ball mill is crucial for the seamless operation of ore processing and other industrial plants, where unexpected equipment failure or downtime can result in significant financial losses and reduced production efficiency. Thus, this paper examined a hybrid method for identifying critical failure modes in a ball mill using the Failure Modes, Effects, and Criticality Analysis combined with the Complex Proportional Assessment with Grey Numbers (COPRAS-G). The COPRAS-G method was employed to assess the criticality of various failure modes by determining the weights of significant failure modes in the bearing, gearbox, motor, and shaft components of a ball mill. It was observed that gear seal failure is the most critical failure with percentage contribution (Ni) of 100%, while bearing wear is the least critical failure with percentage contribution (Ni) of 62%. The result suggests that failure modes of gear seal failure, gear pitting, motor bearing fatigue, and shaft fracture are regarded as the main contributors to failure with percentage contribution (Ni) of 100%, 97%, 87% and 83% respectively. Current ball mill maintenance includes corrective, preventive and predictive maintenance. For failure modes with high criticality, predictive maintenance was advised, while for moderate and low criticality, corrective and preventive maintenance were advised.

Selection of Solar-Based Water-Splitting Hydrogen Production and Hydrogen Storage Technologies Using Neutrosophic Analytic Hierarchy Process and Neutrosophic Complex Proportional Assessment

Abstract The production of hydrogen through water-splitting using solar energy has the potential to meet the world’s future energy demand while also addressing the issue of greenhouse gas emissions. To compare and select the best technology among solar thermochemical cycles, photoelectrochemical, and photocatalytic water-splitting in terms of solar-to-hydrogen efficiency, production cost, safety, life expectancy, maintenance cost, and detrimental impact on the environment, this study used a multi-criteria decision analysis hybrid called Neutrosophic Analytic Hierarchy Process (NAHP) and Neutrosophic Complex Proportional Assessment (NCOPRAS). The same method was used to determine the best technology for automobiles among compressed hydrogen storage, metal hydrides, metal-organic frameworks, and chemical storage in terms of gravimetric system capacity, volumetric system capacity, safety, system cost, cycle life, energy efficiency, detrimental impact on the environment, and refueling time. There were 4 experts each in hydrogen storage and hydrogen, specifically from the Philippines, Australia, India, Romania, and Italy. All experts’ judgments have a consistency ratio (CR) < 0.1. The priority criterion for hydrogen production was life expectancy (weighted value (WV) = 0.189), and gravimetric system capacity for hydrogen storage (WV = 0.137). The result showed that among all alternatives for hydrogen production, the solar thermochemical cycle was the best (relative significance value (RSV) = 0.3432) as for hydrogen storage, the best technology among the alternatives was metal hydrides (RSV = 0.2575). A sensitivity analysis was used to check the variability and robustness of the solution.

Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability

Optimizing machining parameters is crucial, enhancing machinability while maintaining high product quality standards. This study bridges a critical research gap by evaluating and comparing five Taguchi-based Multi-Criteria Decision Making (MCDM) techniques—Combined Compromised Solution (CoCoSo), Grey Relational Analysis (GRA), Multi-Objective Optimization Ratio Analysis (MOORA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS)—coupled with the Entropy method to optimize machining parameters for enhancing machinability in turning medium carbon steel. The focus is on feed rate and cutting speed under dry and Minimum Quantity Lubrication (MQL) environments considering six critical machining responses: material removal rate, surface roughness, main cutting force, cutting temperature, cutting ratio, and tool life.The results reveal that MQL consistently improves machining performance, with COPRAS, TOPSIS, MOORA, and GRA converging on an optimal setting favoring an MQL environment, a 0.14 mm/rev feed rate, and a cutting speed of 137 m/min, whereas CoCoSo suggests a different optimal parameter setting. CoCoSo and GRA demonstrate the highest reliability, evidenced by minimal discrepancies between predicted and experimental results, with absolute percentage errors of 0.647 % and 0.659 %, respectively. The COPRAS method also shows strong predictive accuracy with a 5.573 % error, outperforming MOORA and TOPSIS. Spearman's rank correlation analysis reveals a high agreement between COPRAS, MOORA, and TOPSIS, with COPRAS emerging as a potential replacement for the latter in similar decision-making scenarios. SEM and EDX analyses confirm that MQL conditions reduce tool wear, enhance surface quality, and extend tool life compared to dry machining. This research provides insights into effective parameter optimization strategies for improving machinability and underscores the benefits of adopting MQL for sustainable manufacturing processes.

An integrated multi-criteria decision making approach for selecting the starting location of railroad projects

PurposeThe successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.Design/methodology/approachBased on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.FindingsThe study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.Originality/valueThis study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

Perbandingan Efisiensi Bahan Bakar Pada Kendaraan Low MPV Dengan Metode Copras

Low multi-purpose vehicles have become a popular choice in the automotive market due to their combination of ample space and improved fuel efficiency. In the context of increasing environmental awareness and ever-increasing fuel costs, this study aims to compare the fuel efficiency of various low multi-purpose vehicles using the Complex Proportional Assessment (COPRAS) method. The Copras method, as a multi-criteria decision analysis tool, is used to evaluate vehicle alternatives based on a number of predefined criteria. In this study, the main criteria considered include engine efficiency, aerodynamics, vehicle weight, and fuel technology used. The identification of these criteria is important as fuel efficiency is influenced by various complex technical factors. Relevant data was collected for the low multi-purpose vehicles to be evaluated. Once the data was collected, a Copras analysis was conducted to determine the relative ranking of each vehicle based on its fuel efficiency. This method allowed the researcher to identify vehicles that stood out in terms of fuel efficiency. The results of this study are expected to provide valuable insights for both consumers and vehicle manufacturers. Consumers can use this information to make smarter decisions when choosing a vehicle that suits their needs, taking into account the important aspect of fuel efficiency. On the other hand, manufacturers can use these findings to develop more fuel-efficient vehicles, as well as to strengthen their position in an increasingly competitive market. In addition, this study also has significant implications in an environmental context. By choosing more fuel-efficient vehicles, consumers can contribute to the reduction of greenhouse gas emissions and reduce other negative impacts on the environment. As such, this research not only provides economic benefits but also supports efforts to maintain environmental sustainability

The Influence of Land Subsidence on Risk of Constructing 150 kV Transmission Tower for South Sumatra – Bangka Belitung Interconnector

Transmission system is one of the national essential objects and should be reliable when functioning. Moreover, disturbances in transmission system can lead to disruption of the service function. A common risk to the reliability of electric distribution is land subsidence on transmission tower. Furthermore, the 150 KV interconnector of South Sumatra-Bangka Belitung (Sumsel-Babel) is located in the coastal area of Banyuasin Dua sub-district, South Sumatra. This area is a river delta with peat and clay soil structures and is prone to land subsidence. Therefore, this research aimed to identify environmental factors that affect land subsidence and cause risk to transmission tower. In the context of this research, Copras (Complex Proportional Assessment) and MCSA (Multi Criteria Spatial Analysis) were used for parameter weighting and risk assessment of tower location, respectively. The results of an expert survey to assess risk of potential land subsidence at tower location showed five parameters, namely, land use, distance to waters or potential flooding, land slope factor, soil type, and conus value. Individual parameters were weighted quantitatively and qualitatively based on the opinions of construction experts. Specifically, the qualitative method was performed by weighting based on criteria and sub-criteria, which were analyzed using Analytic Hierarchy Process (AHP). On the other hand, spatial analysis of locations at risk of land subsidence was conducted using GIS software. Based on the assessment results obtained, land use criteria had the largest weighting influence of 0.340 and the smallest weighting on the cone value was 0.08. Finally, tower location that are most at risk of land subsidence are on peatland, near water, and clay soil.

A novel BWM-entropy-COPRAS group decision framework with spherical fuzzy information for digital supply chain partner selection

The transformation and upgrading of traditional supply chain models through digital technology receive widespread attention from the fields of circular economy, manufacturing, and sustainable development. Enterprises need to choose a digital supply chain partner (DSCP) during the process of digital transformation in uncertain and sustainable environments. Thus, the research constructs an innovative decision methodology for selecting the optimal DSCP to achieve digital transformation. The proposed methodology is propounded based upon the entropy measure, generalized Dombi operators, integrated weight-determination model, and complex proportional assessment (COPRAS) method under spherical fuzzy circumstances. Specifically, a novel entropy measure is proposed for measuring the fuzziness of spherical fuzzy (SF) sets, while generalized Dombi operators are presented for fusing SF information. The related worthwhile properties of these operators are discussed. Further, an integrated criteria weight-determination model is presented by incorporating objective weights obtained from the SF entropy-based method and subjective weights from the SF best worst method. Afterward, an improvement of the COPRAS method is proposed based on the presented generalized Dombi operators with SF information. Lastly, the practicability and validity of the proposed methodology are verified by an empirical study that selects an appropriate DSCP for a new energy vehicle enterprise to finish the goal of digital transformation. The sensitivity and comparative analysis are carried out to illustrate the stability, reliability, and superiority of the propounded methodology from multiple perspectives. The results and conclusions indicate that the propounded method affords a synthetic and systematic uncertain decision-making framework for identifying the optimal DSCP with incomplete weight information.

Bionic design of thin-walled bilinear tubes with excellent crashworthiness inspired by glass sponge structures

In order to enhance energy absorption, this study draws inspiration from the diagonal bilinear robust square lattice structure found in deep-sea glass sponges, proposing a design for thin-walled structures with superior folding capabilities and high strength-to-weight ratio. Firstly, the crashworthiness of bionic glass sponge tube (BGSTO) is compared with that of equal-wall-thickness equal-mass four-X tube through both experiments and simulations, and it is obtained that the specific energy absorption of BGSTO is increased by 78.64%. And the crashworthiness of BGSTO is also most significant compared to that of multicellular tubes with the similar number of crystalline cells. Additionally, we found that the double-line spacing of the glass sponge can be freely adjusted without changing the material amount. Therefore, based on BGSTO, we designed two other double-line structures, BGSTA and BGSTB. Then with equal wall thickness and mass as a prerequisite, this study proceeds to design and compare the energy absorption properties of three bilinear thin-walled tubes in both axial and lateral cases. The deformation modes and crashworthiness of the three types of tubes with variable bilinear spacing (βO/A/B ) are comparatively analysed. The improved complex proportional assessment (COPRAS) synthesis decision is used to obtain that BGSTO exhibits superior crashworthiness over the remaining two kinds of tubes. Finally, a surrogate model is established to perform multi-objective optimization on the optimal bilinear configuration BGSTO selected by the COPRAS method.

Prioritizing evaluation criteria of IoT-driven warehousing startups: a silver lining to the unorganized sector in food supply chain

PurposeThis research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain.Design/methodology/approachA blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups.FindingsThe critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups.Practical implicationsWarehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience.Originality/valueDespite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work.

Evaluation of carbon capture technologies in the oil and gas industry using a socio-technical systems perspective-based decision support system under interval type-2 trapezoidal fuzzy set

Concerns in relation to consequences of global warming and climate change have activated worldwide attempts for mitigating the concentration of carbon dioxide (CO2) produced by the industrial sector. Decarbonizing the oil and gas refining (OGR) industries is a challenging problem for policy-makers owing to its potential to prevent economic, environmental, and health risks. In this regard, CO2 capture, utilization, and storage (CCUS) technologies are the most encouraging options to decarbonize. The technologies related to the part of CO2 capture can play a vital role in solving the mentioned problem. Various technologies have been employed for CO2 capture, and choosing the appropriate technology is a complex multi-criteria decision-making (MCDM) issue. This work develops a novel and robust decision support system (DSS). The DSS integrates MCDM techniques of the Delphi and Entropy integration method (DAEIM) and complex proportional assessment of alternatives (COPRAS) method with the interval type-2 trapezoidal fuzzy (IT2TF) environment. The proposed DSS is used to evaluate, prioritize, and choose technologies for CO2 capture. A hybrid criteria system, which involves elements of socio-technical systems perspective has been used for evaluating the candidate technologies. For implementing the DSS of this work, five capture technologies of post-combustion (A_cc1), pre-combustion (A_cc2), oxy-fuel combustion (A_cc3), direct air capture (A_cc4), and indirect air capture (A_cc5) have been chosen for evaluation. The final value of each technology is A_cc1 (0. 2907), A_cc2 (0.2602), A_cc3 (0.1005), A_cc4 (0.2304), and A_cc5 (0.1181) and the preferences of the technologies are A_cc1> A_cc2> A_cc4> A_cc5> A_cc3. The evaluation findings reveal that post-combustion technology with the value of 0.2907 is the most suitable scenario for the capture of CO2 emissions from Iran's OGR systems. The computation results demonstrate that the suggested DSS is feasible and applicable and give reliable and robust findings for acquiring the optimal CO2 capture technology.

Cryogenic and conventional milling of AZ91 magnesium alloy

Use of magnesium is the need of the hour due to its low density as well as its high strength-to-weight and stiffness-to-weight ratio etc. This study focuses on the effectiveness of liquid nitrogen (LN2) assisted cryogenic machining on the surface integrity (SI) characteristics of AZ91 magnesium alloy. Face milling using uncoated carbide inserts have been performed under liquid nitrogen (LN2) assisted cryogenic condition and compared with conventional (dry) milling. Experiments are performed using machining parameters in terms of cutting speeds of 325, 475, 625 m/min, feed rates of 0.05, 0.1, 0.15 mm/teeth and depth of cuts of 0.5, 1, 1.5 mm respectively. Most significant surface integrity characteristics such as surface roughness, microhardness, microstructure, and residual stresses have been investigated. Behaviour of SI characteristics with respect to milling parameters have been identified using statistical technique such as ANOVA and signal-to-noise (S/N) ratio plots. Additionally, the multi criteria decision making (MCDM) techniques such as additive ratio assessment method (ARAS) and complex proportional assessment (COPRAS) have been utilized to identify the optimal conditions for milling AZ91 magnesium alloy under both dry and cryogenic conditions. Use of LN2 during machining, resulted in reduction in machining temperature by upto 29% with a temperature drop from 251.2 °C under dry condition to 178.5 °C in cryogenic condition. Results showed the advantage of performing cryogenic milling in improving the surface integrity to a significant extent. Cryogenic machining considerably minimized the roughness by upto 28% and maximised the microhardness by upto 23%, when compared to dry machining. Cutting speed has caused significant impact on surface roughness (95.33% – dry, 92.92% – cryogenic) and surface microhardness (80.33% – dry, 82.15% – cryogenic). Due to the reduction in machining temperature, cryogenic condition resulted in compressive residual stresses (maximum σ║ = -113 MPa) on the alloy surface. Results indicate no harm to alloy microstructure in both conditions, with no alterations to grain integrity and minimal reduction in the average grain sizes in the near machined area, when compared to before machined (base material) surface. The MCDM approach namely ARAS and COPRAS resulted in identical results, with the optimal condition being cutting speed of 625 m/min, a feed rate of 0.05 mm/teeth, and a depth of cut of 0.5 mm for both dry and cryogenic environments.