Selecting a Multi-Criteria Decision-Making (MCDM) method is critical for developing robust Decision Support Systems (DSS), yet limited attention has been given to assessing their stability under structural changes in decision problems. This study proposes a simulation-based framework for evaluating the robustness of MCDM methods when the least important criteria are iteratively removed. Four selected methods, namely Additive Ratio ASsessment (ARAS), COmplex PRoportional ASsessment (COPRAS), Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS), and MultiAttributive Ideal-Real Comparative Analysis (MAIRCA) were tested across thousands of randomized scenarios, with performance assessed through mean ranking correlation, frequency of ranking alterations, and distribution of similarity values. The findings reveal consistent stability trends across methods while identifying differences in sensitivity to criteria reduction. Notably, MAIRCA and COPRAS exhibited more concise performance distributions, suggesting stronger resilience to problem changes. This work addresses a critical gap in understanding method robustness, supporting more informed selection of MCDM techniques for uncertain decision environments and enhancing the reliability of decision-making processes.
The widespread adoption of smart mobile devices (SMDs) with advanced computing capabilities presents a valuable resource for mobile crowd computing (MCC). Efficient task scheduling in MCC relies on selecting the right SMDs, which poses a complex multi-criteria decision-making challenge due to the diverse hardware specifications of the devices and the presence of non-compensatory parameters. Traditional multi-criteria decision analysis (MCDA) methods, such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), typically assume full compensability between criteria. However, this assumption may conflict with strong sustainability principles. To tackle this issue, the authors introduce the Strong Sustainability Paradigm based Technique for Order Preference by Similarity to Ideal Solution (SSP-TOPSIS) method, an extended version of TOPSIS that incorporates linear compensation reduction. This enhancement allows for a more accurate reflection of sustainability requirements in the decision-making process. The SSP-TOPSIS method demonstrates improved analytical capabilities compared to classical TOPSIS and provides a framework that supports sustainability-driven decisions.
Chatbots powered by large language models (LLMs) are increasingly prevalent in various domains. Nonetheless, they face challenges such as hallucinations and losing context during extended conversations. This study tackles these issues by proposing a multi-agent strategy for chat architecture where multiple LLMs focus on distinct tasks to enhance the quality of their output. The suggested solution involves a supervisor agent working in conjunction with a document search and review module. We assess the performance of information systems with chatbots designed to respond to sustainability questions in English and handle technical documentation for plant equipment in Polish. A comprehensive analysis of commercial and open-source models revealed that Qwen2.5 v14b’s performance is comparable to that of the Gemini family models.
This study examines the reliability of automatic evaluation metrics in assessing responses generated by large language models (LLMs) in the context of university recruitment. A total of 113 domain-specific questions were used to prompt five prominent LLMs, each in three configurations: basic, document-context, and internet-context. The generated responses were evaluated using three categories of metrics: lexical, semantic, and LLM-asa-Judge. These metric-based assessments were subsequently compared with expert evaluations conducted using a 5-point Likert scale. The findings indicate that although automatic metrics offer considerable efficiency, their consistency with expert judgments varies substantially. Moreover, the results suggest that both the model configuration and its underlying architecture significantly affect evaluation outcomes. Among the metric categories, LLM-as-a-Judge appears to yield the highest alignment with expert assessments, suggesting greater reliability in this approach.
This article presents an innovative approach to monitoring river water quality in real time by generating estimates of difficult-to-measure signals such as biochemical oxygen demand. Laboratory tests take too long for real-time monitoring. Therefore, an adaptive PDALM algorithm (Proportional Differential Algorithm with a Latch Mechanism) was developed, integrating mathematical modelling with measurement data to enable instantaneous estimation of water quality signals using a special latch mechanism. The forced eigenvalue distribution guarantees system dynamics and ensures stability and robustness to disturbances. In the proposed RTMS system, the PDALM algorithm functions as an adaptive soft sensor generating high-quality training data. This data is then used by a generative neural network for anomaly detection and forecasting of atypical scenarios in dynamic environmental systems. The system can function as an intelligent environmental monitoring module capable of learning, predicting, and responding to changing environmental conditions.
