The university course timetable problem (UCTP) is known to be NP-hard, with solution complexity growing exponentially with the problem size. This paper introduces an algorithm that effectively tackles UCTPs by employing a combination of exploration and exploitation strategies. The algorithm comprises two main components. Firstly, it utilizes a genetic algorithm (GA) to explore the search space and discover a solution within the global optimum region. Secondly, it enhances the solution by exploiting the region using an iterated local search (ILS) algorithm. The algorithm is tested on two common variants of UCTP: the post-enrollment-based course timetable problem (PE-CTP) and the curriculum-based course timetable problem (CB-CTP). The computational results demonstrate that the proposed algorithm yields competitive outcomes when compared empirically against other existing algorithms. Furthermore, a t-test comparison with state-of-the-art algorithms is conducted. The experimental findings also highlight that the hybrid approach effectively overcomes the limitation of local optima, which is encountered when solely employing GA in conjunction with local search.

In dynamic and harsh environments, ensuring the accuracy and reliability of sensor data is essential for robust real-time decision-making. Conventional trust evaluation approaches often overlook the compounded uncertainties arising from sensor degradation, environmental fluctuations, and measurement noise, relying instead on binary or probabilistic models that inadequately capture partial truth and indeterminacy. This study introduces a neutrosophic-based framework for evaluating sensor trust under uncertainty and degradation. The proposed model characterizes truth, indeterminacy, and falsity through neutrosophic membership functions derived from sensor attributes such as coverage, battery level, noise, drift, and failure risk. Based on thresholded neutrosophic values, the sensing field is segmented into three zones: interior (trustworthy), boundary (ambiguous), and exterior (unreliable) to delineate sensor reliability. Trust scores are then computed by aggregating normalized truth values over effective coverage areas, while temporal degradation is captured through a spatiotemporal modeling process. Extensive Monte Carlo simulations with 50 runs are employed to assess resilience and variability, with performance evaluated using per-sensor metrics including mean trust, standard deviation, and top-3 occurrence frequency. Results show that the framework robustly tracks sensor trust dynamics, captures spatial and temporal degradation patterns, and enables informed, real-time sensor selection in uncertain environments.

This paper presents an integrated decision-support framework for selection of healthcare treatment based on the use of Neutrosophic Logic, Dempster-Shafer Theory (DST), and Interval-Valued Fuzzy Sets (IVFS) in an extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) structure. Healthcare decision-making often involves incomplete clinical data, conflicting expert opinions, and context-dependent variability, which are not adequately addressed by conventional MCDM approaches. Existing methods exhibit key limitations: classical models assume precise inputs, fuzzy models capture vagueness but not indeterminacy, and existing neutrosophic approaches lack a mechanism for resolving inter-expert conflict prior to ranking. The proposed framework addresses these gaps through a sequential uncertainty-handling process in which neutrosophic logic models truth, indeterminacy, and falsity, IVFS captures variability via interval-valued representations, and DST performs evidence-theoretic fusion to reconcile conflicting expert inputs before ranking. To overcome these problems, the proposed framework is used to transform neutrosophic evaluations into fuzzy representations by using interval-valued representations, which liberalizes the treatment of uncertainty. The DST systematically integrates expert judgment to support structured evidence fusion while avoiding premature consensus. The extended TOPSIS method is subsequently applied to generate treatment rankings using belief-weighted neutrosophic scores. The framework is evaluated using sensitivity analysis and Monte Carlo simulation, where variations in criterion weights and stochastic perturbations representing expert variability are introduced to assess ranking stability under uncertainty. The framework is applied to an illustrative numerical evaluation in a healthcare setting, where treatment options are assessed in terms of efficacy, adverse effects, cost, recovery period, and patient satisfaction. Sensitivity analysis and Monte Carlo simulations are employed to validate the approach, demonstrating its robustness and stability under varying weighting schemes and expert opinion perturbations. Results from a synthetic illustrative scenario indicate stable and interpretable rankings under weight perturbations and stochastic noise, suggesting robustness while requiring further validation with real clinical data. The proposed approach provides an uncertainty-aware decision-support tool for clinicians, administrators, and policymakers, offering interpretable treatment prioritization while remaining scalable for complex healthcare environments.