The rapid development of e-commerce has made last-mile delivery a critical bottleneck in logistics management, with its efficiency directly impacting operational costs, service quality, and environmental sustainability. To address the multi-criteria decision-making (MCDM) problem of parcel locker location selection, this study constructs an intelligent decision-support framework that integrates the Improved Fuzzy Step-wise Weight Assessment Ratio Analysis (IMF SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) methods. Based on real-world data from the Brčko Distribution Center of a regional logistics company (X Express), the research first employs the IMF SWARA method to determine fuzzy weights for six key criteria, including availability, frequency of user requests, and accessibility. The WASPAS method is then applied to comprehensively rank twelve candidate locations. Results indicate that location A2 is the optimal choice, followed by A4 and A3. The robustness of the model is verified through sensitivity analysis, including comparisons with other MCDM methods such as ARAS, EDAS, and MARCOS, as well as systematic variation tests of the $\lambda$ parameter in WASPAS. This framework provides logistics managers with a structured and quantifiable decision-making tool, facilitating data-driven optimization of last-mile delivery networks in complex urban environments and enhancing the sustainability and operational efficiency of logistics systems.
Assessing the performance of decision-making units (DMUs) under intuitionistic fuzzy conditions has emerged as an essential area of investigation in today’s performance evaluation studies. The framework demonstrated in Intuitionistic Fuzzy Data Envelopment Analysis (IFDEA) is a way to assess the relative performance of DMUs when the observed data are notably expressed as ambiguity or uncertainty in the inputs and outputs represented by intuitionistic fuzzy numbers (IFN). When the situations define the conditions to use models with traditional input-output distinctions, traditional models are not less applicable when the parameters are vague, thus prompting the need for a set of more flexible tools. In this work, a ranking procedure is utilized that uses the centroid of triangular intuitionistic fuzzy numbers (TIFNs) to address the IFDEA model that defined input and output variable through TIFNs, it allows to calculate the efficiency status of each unit and to differentiate the DMUs between efficient and inefficient groups. An intuitionistic super-efficiency (IFSE) model is provided to obtain a complete ranking of DMUs that identified as efficient. To help decision makers, a reference-set-oriented benchmarking strategy is created to identify relevant peer units of the DMUs identified as inefficient to assist in improving their performance. To demonstrate the strength and practical applicability of the proposed framework, two examples of application are presented, as well as discussed, the technical differences of comparing the outcomes of analysis with the ranking proposals existing in the literature.
Selecting an optimal logistics service provider is a complex multi-criteria decision-making problem that directly affects a company’s competitiveness. This paper proposes a hybrid MCDM model that integrates the Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods. FUCOM was used to determine the weight coefficients of seven criteria, while MARCOS was applied to rank ten potential logistics providers in the market of Bosnia and Herzegovina. The case study was conducted for the company Hygiene Pro Team from Banja Luka. The results showed that provider P9 represents the most favorable solution, which was confirmed by an extensive sensitivity analysis that verified the stability of the model. The proposed FUCOM–MARCOS model provides a robust framework for strategic decision-making in logistics.
It could be argued that the competitive resources possessed by organisations today are similar. One of the most important factors that differentiates businesses, provides a competitive advantage, and enables them to stay one step ahead of their competitors is human capital. Organisations' ability to act in line with their mission, vision, and goals depends on the effective and efficient management of this capital. Selecting the right personnel is one of the most important stages in managing human resources effectively and efficiently. If the selected personnel do not perform as expected, it can indeed harm the organisation. The purpose of this study is therefore to identify the selection criteria prioritised by human resources managers in a call centre, a hospital, a bank, a public economic enterprise, and two companies operating in an organized industrial zone in personnel selection. The criteria prioritised in personnel selection were first collected during initial interviews with relevant managers to create a pool of criteria. Ten of these criteria were then presented to the managers in a second interview, and they were asked to rank them in order of importance. Data obtained from each manager was analysed using the IMF-SWARA method. According to the results, the most important criterion for managers was “Position and competency alignment (PCA)”, while the least important criterion was “solving problems promptly and effectively (SPP)”. These findings demonstrate that managers prioritise compatibility between the qualities of the job and those of the personnel. It is believed that these results can guide managers in organisations operating in the relevant sector, as well as individuals considering working in this sector.
The optimization of project schedules in the presence of uncertainty remains a critical challenge in project management. This study proposes a hybrid methodology that combines Monte Carlo Simulation (MCS) with Integer Linear Programming (ILP) to optimize project crashing strategies under conditions of schedule risk. The approach was applied to a real-world telecommunications infrastructure project, which involved the construction of 50 towers within a stringent contractual deadline. MCS was employed to model the uncertainty in activity durations and assess the likelihood of on-time project completion, while ILP was used to determine the most cost-effective crashing strategy. The findings indicate that, without any mitigation measures, the probability of completing the project within the planned 68-day schedule was a mere 3%. However, upon implementing risk response measures, this probability increased to 21%. A comparative analysis demonstrated that delay penalties increase at a much higher rate than crashing costs, highlighting the significant financial benefits of early intervention. This study illustrates that the integration of probabilistic risk analysis with optimization techniques not only enhances schedule reliability but also minimizes cost overruns, providing a robust decision-making framework for complex projects. By leveraging the combination of MCS and ILP, the proposed methodology supports the development of more resilient and economically efficient project plans, particularly in projects characterized by high uncertainty and time-sensitive constraints.
The accelerating growth of urban populations, rapid city expansion, and inadequacies in transportation infrastructure have exacerbated traffic congestion and environmental burdens in metropolitan areas. These challenges have intensified the demand for sustainable mobility strategies, with electric vehicles emerging as a central component of urban decarbonization and efficiency initiatives. In this study, a structured multi-criteria decision-making framework was established to determine the most suitable electric vehicle for courier services. The framework was developed using the analytic hierarchy process (AHP), which enables the systematic evaluation of both criteria and sub-criteria and provides a robust mechanism for prioritizing alternatives. To enhance reliability, the model was implemented and validated using Expert Choice software, allowing for consistency testing and sensitivity analysis. Three categories of electric vehicles—electric cars, electric scooters, and electric bicycles—were assessed against a comprehensive set of decision factors encompassing economic, operational, environmental, and infrastructural dimensions. The resulting preference weights indicated that electric cars (0.387) represent the most suitable option for courier services under the evaluated conditions, followed closely by electric scooters (0.316) and electric bicycles (0.297). The ranking highlights the relative advantages of electric cars in balancing load capacity, operational flexibility, and environmental impact, while also reflecting the growing feasibility of scooters and bicycles for last-mile delivery. By offering a transparent and replicable approach to alternative vehicle selection, this research contributes to the optimization of courier logistics and the promotion of environmentally responsible transportation systems in congested urban environments. The methodological framework developed in this study may be adapted for broader applications in sustainable transport planning and fleet management, supporting policy-makers and practitioners in achieving urban sustainability objectives.
The rapid proliferation of mobile banking has transformed the delivery of financial services, necessitating a comprehensive understanding of service quality and its impact on customer satisfaction. In this study, bibliometric and content analyses were employed to examine the evolution of research on mobile banking service quality and customer satisfaction, with emphasis placed on research trends, influential contributors, thematic structures, and emerging gaps. Data retrieved from the Scopus database spanning 2003–2025 were analyzed using VOSviewer and Biblioshiny software to conduct co-word analysis, citation analysis, co-authorship mapping, and bibliographic coupling. Findings indicate a marked acceleration of research activity after 2015, with significant contributions originating from India, Indonesia, and Saudi Arabia, while University Tun Hussein Onn Malaysia emerged as one of the most active institutions. The International Journal of Bank Marketing was identified as the leading publication outlet, and scholars such as Lee and Chung were recognized as influential authors. Network analysis revealed that customer satisfaction, trust, security, service quality, and usability constitute the dominant themes in this research domain. Co-authorship analysis demonstrated robust collaborations among Saudi Arabia, China, the United Kingdom, and the United States, whereas bibliographic coupling confirmed that trust and service quality are central drivers of mobile banking adoption. The originality of this study lies in the provision of a structured synthesis of the intellectual landscape of mobile banking research and in the articulation of critical knowledge gaps. Limitations include reliance on Scopus-indexed studies and the exclusion of non-English publications, which may restrict global comprehensiveness. Future research should prioritize the integration of artificial intelligence in mobile banking, the role of mobile financial services in advancing financial inclusion, and the implications of evolving regulatory frameworks for customer trust and satisfaction. By consolidating existing evidence and highlighting strategic research directions, this study offers a foundation for advancing theoretical, methodological, and practical understanding of mobile banking services.
In an era defined by digital transformation and systemic volatility, conventional approaches to strategic risk management have been increasingly challenged by the complexity and unpredictability of modern operational environments. To address these limitations, a novel artificial intelligence (AI)–driven framework has been developed to enhance organizational resilience and optimize strategic decision-making. Constructed through a systematic review conducted in accordance with PRISMA 2020 guidelines, this study synthesizes current academic literature and industry publications to identify critical enablers, practical gaps, and methodological advancements in AI-enabled risk governance. The proposed framework integrates real-time analytics, predictive modelling, and adaptive governance mechanisms, aligning them with enterprise-wide strategic objectives to support decision-making under volatile, uncertain, complex, and ambiguous (VUCA) conditions. Anchored in dynamic capabilities theory and decision support systems (DSS) literature, the framework is designed to facilitate proactive risk anticipation, reduce cognitive and algorithmic biases in decision-making, and foster strategic alignment in rapidly evolving contexts. Its adaptability to small and medium-sized enterprises (SMEs), as well as its cross-sectoral relevance, underscores its scalability and practical utility. Nonetheless, the effectiveness of the framework is contingent upon the availability of high-quality data, the level of digital maturity within organizations, and the implementation of responsible AI principles. By bridging the gap between theoretical innovation and real-world applicability, this study contributes a robust foundation for future empirical validation and sector-specific customization. The framework is expected to inform governance and technology leaders aiming to institutionalize AI-based resilience capabilities, thereby supporting sustainable strategic outcomes in both developed and emerging markets.
Green supplier selection (GSS) as a critical strategic element is placed in the limelight in contemporary supply chain management (SCM), owing to the growing emphasis on environmental responsibility and sustainability. This study presents a fuzzy multi-criteria decision-making (FMCDM) framework, employing Fuzzy Logarithmic Percentage Change-Driven Objective Weighting (FLOPCOW) method to determine the relative importance of sustainability criteria under uncertainty. A panel of five academic and industry experts was selected to identify 21 criteria, which were categorized into three main dimensions including environmental performance (C1), resource efficiency (C2), and corporate sustainability policies (C3). Triangular fuzzy numbers (TFNs) were adopted to model linguistic ambiguities in expert judgments whereas fuzzy normalization was applied to ascertain the weights of criteria. Key findings indicated that corporate sustainability policies (C3) were prioritized as the most influential dimension, followed by environmental performance (C1) and resource efficiency (C2). This suggested the centrality of institutional governance in advancing long-term sustainability objectives. Sub-criteria analysis further revealed ecological training programs, air emissions control, and sustainability reporting as the most critical indicators in the interplay of operational practices and transparent governance. FLOPCOW has effectively processed expert opinions with the use of fuzzy normalization, hence advocating a clear and repeatable approach for the evaluation of green suppliers. Furthermore, it highlighted the importance of policy-based criteria in supplier assessment and organizations could then align their purchasing decisions with sustainability goals by considering more on governance-related factors like compliance and stakeholder engagement.
It is crucial to ensure system reliability in changing situations where systems are required to operate in uncertainty or against disturbances. Human-in-the-Loop (HITL) simulations have in recent times emerged as an important key in ensuring the robustness and resilience of the system via evaluation and testing. The method introduces human decision-making and adaptability as well as providing insight into zones of possible weak points of the system and failure modes which are not even captured by computer-based systems. By incorporating HITL simulations into the system, designers and engineers could simulate operational challenges in real life, identify unforeseen defects, and implement mitigative strategies to enhance both robustness-ensuring consistent performance in the nominal operation and resilience-maintaining functionality in and after disruptions. This article looks at the effectiveness of HITL simulations in various domains, and particularly at the role that these contribute to system robustness and resilience. Among the most significant issues will be the nature of building the simulation environments, the test for the range of scenarios, and the roles for humans to be simulated within the loop. We investigate the behavior of humans during stress and uncertainty, then provide valuable feedback to the system to help it learn. By revealing the vulnerabilities of the system design and acknowledging human effects on recovery and decision-making operations, HITL simulations finalize the development of more adaptable, stable systems that could recover rapidly from interruptions. To conclude, HITL simulations are a critical tool for improving the reliability of systems, hence providing a comprehensive framework to address either expected or unexpected challenges in complex operating environments.
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