Multi-criteria Decision-making Framework Research Articles

Assessment of Metaverse wearable technologies for smart livestock farming through a neuro quantum spherical fuzzy decision-making model

Livestock wearable technologies are innovations designed to ensure livestock health management. However, the user aspect of these devices from farmers’ perspective is still questionable. Additionally, livestock wearables are still in progress compared to the other wearables. Thus, this research aims to identify key design features regarding wearable smart collars (WSCs) and rank the alternative WSC prototypes within Metaverse, allowing farmers to select the best wearable device. To this end, an integrated neuro quantum spherical fuzzy multi-criteria decision-making (MCDM) framework is introduced via facial expressions to obtain the priority weights of WSC criteria with the improved decision-making trial and evaluation laboratory (DEMATEL) approach and to rank the WSC alternatives in Metaverse through the improved multi-objective optimization based on ratio analysis (MOORA) model. The novelties of this research are: (1) to build and introduce a novel decision support tool based on facial expressions, expert recommendations, and the quantum spherical fuzzy sets, (2) to guide industrial designers about the essential features of WSCs, whereas they are designing these devices, and (3) to help smallholder farmers to decide on the best WSC to enhance animal welfare and efficiency of animal production. Concerning the findings, “sound and stress analyzer” is the most significant feature, followed by “disease detection” and “price.” Moreover, Prototype 3 is the best WSC for farmers to adopt for livestock health management. Some essential implications are further presented.

Promoting inclusivity in education amid the post-COVID-19 challenges: An interval-valued fuzzy model for pedagogy method selection

The advent of the COVID-19 pandemic has brought about unprecedented disruptions in the field of education, necessitating a reevaluation of pedagogical approaches in the post-COVID era. This research paper introduces a novel interval-valued fuzzy simple additive weighting (SAW), weighted product model (WPM), and weighted aggregates sum product assessment (WASPAS) multi-criteria decision-making (MCDM) framework to address the challenge of promoting inclusivity in education amidst the post-COVID era. Leveraging the uncertainty inherent in the post-pandemic educational landscape, the proposed method offers a comprehensive pedagogy selection approach incorporating interval-valued fuzzy sets to account for imprecise and ambiguous data. By integrating the principles of inclusivity and diversity, the method evaluates various pedagogical approaches and their effectiveness in fostering an inclusive learning environment for diverse student populations. The study showcases the application of pedagogy selection in real-world educational scenarios, demonstrating its potential to inform policy decisions and enable educational institutions to adapt and cater to the evolving needs of learners in the aftermath of the COVID-19 pandemic.

Ship collision risk assessment: A multi-criteria decision-making framework based on Dempster–Shafer evidence theory

Ship collision risk modelling is dominated by the utilization of the experts’ experiential insights and knowledge. To construct an effective assessment model for evaluating potential ship collision risks grounded in expert judgments, this paper introduces a novel multi-criteria decision-making framework based on Dempster–Shafer evidence theory (DSET). Firstly, a novel index-based assessment system is provided to generate the potential collision risk value of ships based on automatic identification system (AIS) data, which encompasses ship condition parameters, deviation coefficient, and frequency coefficient. Further, to generate the weights of these indicators, a framework to extract expert opinions is provided, which includes two parts: a) converting experts’ opinions into D-S framework through hesitant fuzzy linguistic terms and b) generating experts’ opinions using improved DSET. Regarding part a, a methodology is proposed to evaluate the trustworthiness of experts by considering both the level of uncertainty in their opinions and the degree of similarity between their viewpoints. Concerning part b, strategies are integrated to ascertain the most suitable weight for the basic probability assignment, thereby mitigating conflicts within the evidence. Finally, based on the collected AIS data, the proposed framework has been applied to six of the world's busiest waterways: (1) the Strait of Malacca, (2) the English Channel, (3) the Panama Canal, (4) the Suez Canal, (5) the Danish Straits, and (6) the Strait of Hormuz. Subsequently, comprehensive analytical results and recommendations are furnished, such as wide and smooth channels showing elevated risk.

Sustainable Evaluation of E-Commerce Companies in Vietnam: A Multi-Criteria Decision-Making Framework Based on MCDM

This study aims to effectively support decision-makers in evaluating leading e-commerce companies in Vietnam from a sustainability perspective. In addition, this study evaluates and analyzes criteria that affect the performance of e-commerce companies in Vietnam to find the best practices for e-commerce implementation of managers. In this way, companies can save costs and increase marketing and promotion efficiency, helping to reach more customers. In doing so, an integrated framework based on MCDM is proposed for the first time in e-commerce industry assessment. In the first stage, the fuzzy decision making trial and evaluation Laboratory method is applied to determine the weights of 9 criteria based on collected data. This study lists prominent quantitative evaluation criteria, taking into account the sustainability aspect. In the second phase, ranking the top 12 e-commerce companies in Vietnam according to their performance based on these nine criteria was done by applying the neutrosophic fuzzy axiomatic design method. According to an analysis of the data, the external financial assistance coefficient (12.7%) is the most crucial factor determining how competitive international e-commerce businesses are. The results of this study can help underperforming companies make strategic decisions to improve their performance. Integrating these two techniques provides a new method for evaluating global trading companies that have hitherto remained unaddressed in the trading industry and, therefore, leave a gap that needs to be investigated.

Understanding the Impact of Seismic Hazard and Climate Conditions on Multi Criteria–Based Retrofitting of Existing Buildings

A large share of the reinforced concrete (RC) building stock in Mediterranean countries faces a dual challenge of seismic vulnerability and energy inefficiency, calling for urgent renovation efforts. While energy upgrades have been the focus of previous renovation policies, recent research highlights the critical need for integrated retrofitting solutions that address both structural integrity and energy performance. Multi-criteria decision-making (MCDM) approaches are a promising tool for optimizing the combined choice of these integrated interventions, considering various decision variables (DVs) of economic, social, environmental, and technical nature. To understand the impact of climate and seismic hazard conditions on multi-criteria-based retrofitting assessment, a case-study RC school building is selected and assumed to be located in three distinct climate conditions, cold, mild, and warm, and three seismic hazard levels, low, medium and high. Moreover, given the complexity and challenges of quantifying seismic performance metrics for practitioners, an available simplified (practice-oriented) approach is compared herein with a more thorough research-based one for quantifying the seismic performance of RC buildings within the MCDM framework. Both approaches are applied to the case-study building, considering twelve possible combinations of energy and seismic interventions. The accuracy of the practice-oriented approach and its impact on the retrofitting rankings is evaluated, emphasizing the importance of accessible and efficient evaluation methods in facilitating informed decision-making for building renovation.

Enhancing Robustness in Precast Modular Frame Optimization: Integrating NSGA-II, NSGA-III, and RVEA for Sustainable Infrastructure

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework’s capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development.

A multi-criteria decision-making model to determine the optimal share of variable renewable energy sources in a wholesale electricity market

In recent years, the development of renewable energy technologies has gotten increasing attention due to the realization of cleaner production and high energy efficiency. Therefore, the proportion of these resources in electricity production has experienced a dramatic increase. However, with the features of intermittent nature and merit order effects, it is challenging to effectively optimize the integration of renewable energy sources (RES) into the electricity markets. To deal with the complex problem of selecting the proportionate share of non-dispatchable RES for participation in the market, this paper establishes a multi-criteria decision-making (MCDM) framework, which combines the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Five criteria are defined and the corresponding mathematical model is solved by the proposed AHP-SAW method. In this way, the optimal share of renewable resources for participation in the wholesale market can be determined based on the desired criteria. Also, to compare the results obtained by the AHP-SAW method, the criteria weights are determined by Shannon entropy, and the problem is solved by the TOPSIS method. To assert the effectiveness of the proposed framework, two case studies are done. According to the results, by increasing the RES penetration level, the average price of the market decreases mildly while price volatility increases significantly . Also, the amount of forward contracts is decreased when the RES share increases. Moreover, the results show that a high amount of RES is not necessarily desirable and depends on the type of renewable resources and policymakers' priorities. The results for systems 1 and 2 indicate that the maximum allowable amount of RES are 85.359 and 38.24 MW, respectively.

Evaluation of Ergonomic Risks for Construction Workers based on Multicriteria Decision Framework with the Integration of Spherical Fuzzy Set and Alternative Queuing Method

Ergonomic risks critically impact workers’ occupational health, safety, and productivity, and thereby the sustainability of a workforce. In the construction industry, the physical demands and dynamic environment exposes workers to various ergonomic hazards. While previous research has mainly focused on postural risks, there is a need to broaden the scope to include more relevant factors and assess them systematically. This study introduces a multi-criteria decision framework integrating the Spherical Fuzzy Sets (SFSs) and Alternative Queuing Method (AQM) to evaluate and prioritize ergonomic hazards. First, SFSs are employed to quantify the linguistic expressions of experts, addressing the inherent vagueness and uncertainty. Then, an entropy-based objective weighting method is adopted to determine the criteria weights. Finally, AQM is utilized to generate the risk priority. The proposed method has been implemented in a real-life construction project, where “overexertion due to unreasonable task organization”, “hypertension and heart diseases”, and “existing WMSD record” are identified as the top three ergonomic hazards. Then, a thorough discussion of intervention strategies regarding different risk categories is presented to facilitate ergonomic interventions. This proposed decision support system can promote effective ergonomic risk management, benefiting workers’ health and well-being and contributing to the sustainable workforce development of the construction industry.

Assessing indoor positioning system: A q-spherical fuzzy rough TOPSIS analysis

This study investigates advanced data collection methodologies and their implications for understanding employee and customer behavior within specific locations. Employing a comprehensive multi-criteria decision-making framework, we evaluate various technologies based on four distinct criteria and four technological alternatives. To identify the most effective technological solution, we employ the q-spherical fuzzy rough TOPSIS method, integrating three key parameters: lower set approximation, upper set approximation, and parameter q (where q ≥ 1). Our novel approach combines the TOPSIS method with q-spherical fuzzy rough set theory, providing deeper insights into data-driven decision-making processes in corporate environments. By comparing our proposed framework with existing multi-criteria decision-making methodologies, we demonstrate its strength and competitiveness. This research contributes to enhancing decision-making capabilities in corporate settings and beyond.

Assessment of Third-Party Reverse Logistics Provider Through the SWARA-WASPAS Integrated Framework

The selection of the most suitable third-party provider for reverse logistics (RL) activities is a key factor in initiating effective RL processes. However, the process of identifying the optimal third-party provider involves navigating through various conflicting objectives and criteria. This task is intricate and time-consuming, necessitating the application of multi-criteria decision-making (MCDM) approaches. This study addresses the evaluation and selection of the best 3PRLP by introducing a comprehensive MCDM framework. Therefore, this study aims to assist a manufacturer specializing in milk and dairy products who has opted to transfer its reverse logistics operations to a logistics service provider. The objective is to aid them in selecting the most suitable 3PRLP. The proposed framework incorporates the SWARA (Step-wise Weight Assessment Ratio Analysis) technique for determining weights and the WASPAS (Weighted Aggregated Sum Product Assessment) method for thoroughly evaluating alternatives. In this study, a panel of four experts made a joint decision after identifying six criteria and five alternatives. The research concluded that the reverse logistics cost (C1) emerges as the most pivotal factor, with the service quality of reverse logistics (C3) being recognized as the least significant criterion. As a result, A1 stands out as the top choice among the 3PRLP.

Spatial modeling of land resources and constraints to guide urban development in Saudi Arabia’s NEOM region using geomatics techniques

This research presents a GIS-based approach to evaluate land suitability for the urbanization of Saudi Arabia’s NEOM region, an ambitious initiative for sustainable development within a desert setting. By employing a multi-criteria decision-making framework, we have synthesized data on land resources, stability, accessibility, construction costs, and proximity to conservation areas using the Analytical Hierarchy Process (AHP) to prioritize these factors based on their importance for sustainable growth. Our analysis reveals that areas designated with grid values of 9 and 8, totaling 6312.33 square kilometers or 28.23% of the area studied, are most favorable for urban development. These sectors span the northern, central, and southern parts of NEOM, which are notable for their strategic location near future infrastructure and natural assets conducive to sustainable practices. This finding supports NEOM’s key initiatives such as “The Line,” “Oxagon,” and “TROJENA,” showcasing the model’s utility in directing urban development strategies. The study discusses the broader socio-economic and environmental ramifications of pinpointing areas suited and unsuited for development, underlining the model’s role in facilitating decision-making that aligns urban expansion with environmental stewardship. Furthermore, it advocates for leveraging development-restricted zones in conservation efforts and renewable energy ventures. Nevertheless, the study concedes certain limitations, such as the dependence on static datasets and inherent challenges related to the AHP method, including the presumption of criteria independence. Future research avenues are proposed, emphasizing the integration of dynamic data sources, comprehensive socio-cultural impact evaluations, and adopting flexible urban planning methodologies to refine the model’s precision and practicality. Overall, this detailed examination showcases NEOM’s blueprint as an exemplar for future urban environments, illustrating a systematic strategy for developing desert regions that integrate innovation, ecological sustainability, and the preservation of cultural heritage.

A multi-criteria decision framework to evaluate sustainable alternatives for repurposing of abandoned or closed surface coal mines

Surface coal mines, when abandoned or closed, pose significant environmental and socioeconomic challenges. Repurposing these sites is crucial for sustainable land use and responsible resource management. This study presents a comprehensive decision framework tailored to the Indian mining context, utilizing a hybrid approach combining the analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) methodology. The proposed framework assesses and ranks alternative repurposing options by considering a multi-criteria evaluation, including ecological, economic, social, and regulatory factors. AHP is employed to determine the relative importance of these criteria, reflecting the unique priorities and perspectives of stakeholders involved in the repurposing process. TOPSIS then identifies the optimal alternatives based on their overall performance against the established criteria. This hybrid methodology contributes to informed decision-making in the sustainable repurposing of abandoned surface coal mines in India. It aids in identifying the most viable and environmentally responsible alternatives, promoting efficient land use and resource conservation while addressing the challenges associated with abandoned mine sites. The methodology’s applicability extends globally to industries facing similar repurposing challenges, facilitating the transition toward a more sustainable and responsible land reclamation and resource management approach. The methodology is implemented using real mine data and demonstrates the analysis for evaluation among multiple alternatives such as solar parks, fish farming, eco-resorts, forestry, and museums. In our study, eco-resorts show more promise based on the significant potential for local economic development, provision of local employment, long-term revenue generation, potential for upskilling local youth in management, gardening, construction, and animal husbandry, and serving as a site for exhibitions of various arts and crafts.

Exploring critical determinants influencing businesses’ continuous usage of mobile payment in post-pandemic era: Based on the UTAUT2 perspective

Consumers across demographics have diverse digital lifestyles and rely heavily on e-commerce to purchase necessities including food and beverages. The COVID-19 pandemic has habituated consumers to making mobile payments at an accelerated pace. Despite rising consumer demand for mobile payments, comparable trends in the supply side for businesses remains crucial for the future sustainability of mobile payments. However, businesses must remain willing to accept mobile payments and engage in continued marketing to promote mobile payment. Few studies thus far have applied multi-criteria decision-making analyses to evaluate the critical determinants of intention and motivation to use mobile payment; quantitative analysis methods, such as factor, regression, and variance analyses, are more commonly applied. This study examined the critical evaluation constructs and criteria that affect retailers and service providers in outlying islands using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as our theoretical basis. It then established a multi-criteria decision-making framework for understanding the factors influencing businesses' mobile payment use. This study uses the multi-criteria evaluation of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and DEMATEL-based Analytic Network Process (DANP) to create network relation maps of the constructs and criteria for the willingness of outlying island retailers and service providers to continue accepting mobile payments. Penghu County is located on the outlying islands of Taiwan and has witnessed a substantial shift in transaction practices among its small and medium-sized enterprises following the outbreak of the pandemic. It experienced a marked decrease in tourist influx during the COVID-19 pandemic, significantly impacting local businesses. This study aimed to better understand the opinions and motivations of businesses in Penghu County regarding mobile payment services. Insights from this perspective should help improve local businesses’ adaptability and usage habits. The findings offer significant strategies for the government to provide specific training programs and subsidies or incentives for businesses adopting mobile payment technologies, ultimately expanding mobile payment services across small businesses that target tourists.

“Multi-Criteria Group Decision Making Approach for Scheduling Algorithms Selection by Short Term Scheduler using Fuzzy TOPSIS”.

The fundamental objective of the CPU scheduler is to equitably and effectively allocate CPU time among competing processes. Within the realm of schedulers, the short-term scheduler specifically addresses this objective by selecting processes from the ready queue for execution on the CPU, guided by various scheduling algorithms. The critical challenge faced by the short-term scheduler lies in the prudent selection of the most suitable algorithm, as an erroneous choice can detrimentally impact system performance, leading to increased waiting and response times for processes. To surmount this challenge, we employ the Fuzzy TOPSIS method within the framework of Multi-Criteria Decision Making (MCDM) to rank scheduling algorithms, taking into account both quantitative and qualitative factors. The proposed approach involves two steps: firstly, defining criteria for algorithm selection, and secondly, obtaining linguistic ratings from experts for potential alternatives based on the specified criteria. The principal aim of this investigation is to utilize the Fuzzy TOPSIS method, integrating fuzzy sets, to produce comprehensive scores that assist in selecting the optimal alternative.

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

The aim of this research is to address the challenge of transforming car-oriented industrial parks into pedestrian- and bicycle-friendly environments. Through the implementation of a multi-criteria decision-making (MCDM) approach, the study aims to evaluate alternative pathway connections and assess their potential impact on bicycle and pedestrian traffic volumes. By enhancing the connectivity of the cycling pathway network, the research seeks to demonstrate the potential for substantial increases in cycling and walking within industrial zones. This research leverages a multi-criteria decision-making framework, specifically the ARAS-G method, and integrates geographic information system analysis alongside Python scripting to project future bicycle usage and assess alternative pathway connections. The study underscores the potential for substantial increases in cycling and walking by augmenting the connectivity of the cycling pathway network. The findings hold practical significance for urban planners and industrial zone developers, advocating a holistic approach to sustainable transportation. The research contributes a comprehensive set of criteria encompassing connectivity, safety, accessibility, efficiency, integration within the urban fabric, and cost-effectiveness to evaluate sustainability and prioritize actions and measures for reestablishing industrial zones as bicycle-friendly spaces.

Multi-criteria decision framework for catalyst selection: Production of formic acid as a circular liquid organic hydrogen carrier in the hydrogen economy

This study aims to use the multi-criteria decision analysis (MCDA) for the selection of suitable catalysts for the production of formic acid (FA), which serves as a circular liquid H2 carrier for the H2-carbon cycle. TOPSIS, VIKOR, ARAS, COPRAS, and PROMETHEE analytical decision-making methods were used to establish priority rankings for each method. With the exception of the top two ranks, the rankings differed across the methods. Therefore, we proposed an alternative ranking for the catalyst selection in the FA production, where all the methods’ results were given equal priority. By aggregating the outcomes from the decision methods and sorting them in ascending order, a final prioritized ranking was established. Our results showed that the proposed MCDA method was effective in identifying the most suitable catalyst for the FA production as a circular LOHC in the H2-carbon cycle. The model determined that Pd nanoparticle-based activated carbon-supported catalyst exhibited high activity, selectivity, and stability, while also being cost-effective. The findings of this study might contribute to the development of more efficient H2 storage systems based on FA.

A multi-criteria decision-making framework for sustainable road transport systems: Integrating stakeholder-cost-environment-energy for a highway case study in United Arab Emirates

Existing lifecycle assessment models narrowly concentrate on limited parameters, disregarding system ecology and multi-stage stakeholder integration with sequestered analyses. Assimilation of new transport technologies with traditional design-bid-build decision-making paradigm should acknowledge sustainability triple-bottom-line social, cost, and environmental impacts, across all lifecycle stages for a system-wide optimisation. This study presents a multilevel framework to evaluate road transport system projects utilising 1) stakeholder engagement for flexibility in establishing key performance indicators, 2) passenger surveys to identify what encourages mode choice towards public transport, 3) microsimulation models, lifecycle cost and lifecycle analysis for quantitative assessment, 4) analytic hierarchy for qualitative assessment, and finally 5) multi-criteria modelling with stakeholder expert group preferences. Framework is applied to a 5-lane dual carriageway project in Abu Dhabi, analysing eight virgin and recycled material-based and public transport service alternatives. Study revealed the significance of saved user time, reduced fuel consumption during use phase and vehicle operational costs as the highest lifecycle impact generators. The total sustainability performance across the lifecycle for all alternatives was affected by traffic gridlock formation due to traffic growth in later years and lack of traffic load reduction strategies. Results showed that an autonomous vehicle-based bus rapid transit service coupled with recycled material to construct the road significantly reduced lifecycle cost (51.1%), energy (55.6%) and pollutant (54.5%-CO2, 49.7%-NOx, 24.1%-PM) burdens. It exhibited effectiveness of system choice using lifecycle assessment over the typically fragmented project planning and evaluation approach. Overall, framework exhibited robustness against unit cost and LCI uncertainties, fuel distribution assumptions, discount rate, and indicator weights variations based on priorities of different stakeholder groups.

Green hydrogen hubs in India: A first order analytical hierarchy process for site selection across states

Green hydrogen can be the transitional solution required to reduce anthropogenic emissions and enhance energy independency from fossil fuel. To improve green hydrogen adoption, a cluster-based infrastructure, called a hydrogen hub, is devised taking into account the demand-supply interplay coupled with transport ease. In this paper, we have identified sites across states that are conducive for hydrogen hubs. It involves a multicriteria decision-making (MCDM) framework deployed to assign priorities within competing set of criteria. A comprehensive analytical hierarchy process (AHP) is used to rank criteria that are connected to establish a functioning hydrogen hub. These criteria include, but not limited to, proximity to refineries, fertilizer plants, substations, chlor-alkali units, steel manufacturing plants, water availability, access to gas pipelines, railway line access, and finally distance to highways. Our analyses indicate that proximity to refineries and fertilizers scored higher (scores directly proportional to importance) while railway line access scored the least. These prioritised scores are used to spatially locate sites using geographical information system. In a total of thirteen states identified, Gujarat had the largest parcel of most suitable land for setting up hydrogen hub followed by Maharashtra and Andhra Pradesh. For India to set up a functioning hydrogen economy and achieve green hydrogen goals, it is important to identify states that can drive the agenda forward. This paper illustrates a way (amongst many available ways) to identify states based on their strength and capability to fulfil the national goal of 5 million tons of green hydrogen by 2030.

A risk-based multi-criteria decision-making framework for offshore green hydrogen system developments: Pathways for utilizing existing and new infrastructure

Unlocking the potential of offshore renewables for green hydrogen (GH2) production can be a game-changer, empowering economies with their visionary clean energy policies, amplifying energy security, and promoting economic growth. However, their novelty entails uncertainty and risk, necessitating a robust framework for facility deployment and infrastructure planning. To optimize offshore GH2 infrastructure placement, this work proposes a novel and robust GIS-based multi-criteria decision-making (MCDM) framework. Encompassing thirty-two techno-socio-economic-safety factors and ocean environmental impact analysis this methodology facilitates informed decision-making for sustainable and safe GH2 development. Utilizing the synergies between offshore wind and solar resources, this study investigates the potential of hybrid ocean technologies to enhance space utilization and optimize efficiency. To illustrate the practical application of the proposed framework, a case study examining a GH2 system in Australia's marine region and its potential nexus with nearby offshore industries has been conducted. The performed life cycle assessment (LCA) explored various configurations of GH2 production, storage, and transportation technologies. A Bayesian objective weight integrating technique has been introduced and contrasted statistically with the hybrid CRITIC, Entropy, MEREC and MARCOS-based MCDM approaches. Various locations are ranked based on the net present value of life cycle cost, GH2 production capacity, risk, availability, and environment sustainability factors, illustrating their compatibility. A sensitivity analysis is conducted to confirm that a Bayesian approach improves the decision-making outcomes through identifying optimal criteria weights and alternative ranks more effectively. Empowering strategic GH2 decisions globally, the proposed approach optimizes system performances, cost, sustainability, and safety, excelling in harsh environments.

Optimizing Supplier Selection in Indonesia Through Analytical Hierarchy Process (AHP)

This study introduces a structured multi-criteria decision-making framework using the Analytical Hierarchy Process (AHP), applied to evaluate suppliers based on quality, price, quantity of supply, and plantation area. Effective supplier selection is crucial for maintaining supply chain efficiency. By employing the Expert Choice 11 software, the AHP method quantitatively assessed each criterion, resulting in a definitive ranking of suppliers. The analysis highlighted the paramount importance of quality in supplier selection, assigning it the highest weight (0.565). The outcomes reveal that a meticulous application of AHP significantly enhances decision-making in supplier selection, suggesting its broader applicability in global supply chain management contexts to improve operational efficacy and resilience. Highlights: Structured Evaluation: The AHP method provides a systematic approach to supplier assessment. Emphasis on Quality: Quality is the dominant criterion in supplier selection with the highest weight. Operational Improvement: Implementing AHP enhances supply chain efficiency and decision accuracy. Keywords: Supplier Selection, Analytical Hierarchy Process, Expert Choice