Search Results (4,841)

Permutation flow shop scheduling is an important production planning problem handled in different contexts. Just-in-time measures have been significant in the optimization of real problems and one is specifically addressed here: the total earliness and tardiness of jobs. The most used approach in the literature to mathematically express this measure is to sum them up using unit weights thus obtainning a mono-objective function. In this paper it is shown that this is a simplification of a problem that is inherently multi-objective, highlighting how a more comprehensive approach can better support decision-making. A bi-objective mathematical optimization model and tools capable of analyzing the mono-objective solution within the multi-objective perspective are proposed. A computational study to analyze the benefits and difficulties of the solution using the bi-objective approach is presented. The results show that for large-scale instances in which the tardiness factor is small, the conflict between the objectives of minimizing the total earliness and minimizing the total tardiness of jobs increases significantly. Specifically, the mono-objective solution is unbalanced in 50.00% of the analyzed instance structures. However, in 48.12% of the instances, alternative Pareto-optimal trade-offs can be achieved with zero increase to the mono-objective optimal value. Therefore, the multi-objective approach has a greater potential to support decision-makers. Furthermore, we show that the choice of the solution method must be carefully considered, since the Pareto frontier associated with most instances has many non-supported points, representing up to 66.71% of the non-dominated set. Full article

This paper examines the role of Multimodal Journey Planners (MJPs) as a link between user-oriented personalization and the broader societal goals of sustainable urban mobility. In smart cities, MJPs may serve as digital decision-support tools that connect individual mobility choices with broader sustainability objectives. Although contemporary journey planners increasingly display multiple criteria, such as travel time, cost, CO₂ emissions, and number of transfers, they still generally rely on predefined and non-personalized criterion weights and rarely infer travellers’ actual preferences from observed choices. The paper therefore proposes a transparent methodological proof-of-concept that combines multicriteria decision-making and inverse optimization to discover individual preference weights and enable personalized, preference-aware planning of multimodal routes. The Weighted Sum Method (WSM) is adopted as the basic ranking framework, and the proposed approach is evaluated within a controlled methodological testbed based on multimodal journey scenarios in Vienna. The results indicate that, within the available methodological testbed, the preference-discovery-based model achieved closer in-sample agreement with user-provided route evaluations than the model based on explicitly rated criteria. This was observed in the ranking-agreement analysis, where a more favourable penalty-point ratio was obtained in 19/21 cases (90.5%) and in the numerical error comparison, where lower in-sample reconstruction errors were obtained for 18/21 users (85.71%) across all scenarios. The paper further considers the tension between individual and system-level goals, as well as a conceptual extension toward system-aware re-ranking of alternatives. Within the broader framework of smart mobility, the importance of interoperability and open data is also recognized, with National Access Points (NAPs) for multimodal travel information potentially representing an important precondition for the development of advanced and transparent MJP solutions. Full article

Optimal sizing of hybrid renewable energy systems for desert camps is a multi-objective problem that must account for cost, reliability, component degradation, and uncertainty. This paper introduces an improved multi-objective neural architecture search (IMONAS) framework for hybrid nanogrid sizing in the desert environment of Hafr Al-Batin, Saudi Arabia. The framework combines neural optimization, stochastic uncertainty modeling, and explicit battery degradation modeling, a combination not addressed in the reviewed studies for this application. Six test cases are examined by varying uncertainty assumptions, battery degradation, and the annual duration of uncertain operation. For each case, IMONAS provides Pareto-front solutions that specify the photovoltaic, diesel generator, battery autonomy, and inverter choices while minimizing the cost of energy (COE) and the loss of power supply probability (LPSP). IMONAS is compared with the original MONAS and five other multi-objective optimization methods. In addition to visual Pareto-front comparisons, the assessment uses Pareto-dominance indicators, namely the C-metric and an aggregated score derived from pairwise C-metric comparisons across the algorithms and cases. The results provide a validated sizing framework for remote arid-region nanogrids under uncertainty and battery degradation. Full article

The choice of brass as the substrate due to its widespread use in soft non-ferrous industrial components such as bearings and electrical connectors creates the primary basis of novelty in this study. While prior tribological studies on titanium aluminum nitride (TiAlN) coatings is primarily focused on hard substrates such as steel and WC–Co, this work addresses the research gap by presenting a systematic investigation of the combined influence of sputtering power and working pressure on TiAlN coatings deposited on brass. Application of TiAlN coatings on brass surfaces was accomplished using magnetron sputtering. Within the scope of this study, the influence of sputtering power and working pressure on the tribological and structural attributes of TiAlN films is evaluated. The analysis of surface morphology is carried out using scanning electron microscopy (SEM), while structural characteristics revealed a progressive increment in the intensity of the (103) and (107) peaks with variation in deposition parameters. An analysis was conducted to evaluate the tribological properties of the TiAlN coating using a pin-on-disk tribometer. The study involved varying the speeds, loads, and sliding lengths. The optimized condition achieved wear reduction as high as 22% compared to uncoated brass at a sliding distance of 785 m, which highlights the strong dependence of wear performance on deposition parameters. The wear rates of TiAlN-coated brass ranged between 1.03 × 10⁻³ and 5.87 × 10⁻⁴ mm³/Nm depending on parameters like load, sliding distance and speed. Conversely, TiAlN-coated brass pins prepared at varying power showed wear rates ranging from 1.83 × 10⁻⁴ to 5.87 × 10⁻⁴ mm³/Nm. These findings demonstrate that optimization of TiAlN coating parameters on brass can significantly enhance wear resistance, which ultimately improves the durability and performance of engineering components in tribological applications. Full article

Mohs micrographic surgery (MMS) is a highly specialized skin cancer procedure that combines complete microscopic margin assessment with maximal preservation of uninvolved tissue. The technique is based on staged excision of the tumor with systematic horizontal sectioning and real-time examination of the entire peripheral and deep surgical margins, allowing further tissue removal only in areas where residual tumor is identified. Its unique strength lies in the ability to detect subclinical tumor extensions that may be missed by conventional excision and standard vertical sectioning, thereby improving local control while minimizing unnecessary tissue sacrifice. Since its introduction in the 1930s by Frederic E. Mohs, the technique has evolved into a cornerstone of modern dermato-oncology, particularly for tumors arising in anatomically critical areas, recurrent neoplasms, and histologically aggressive malignancies. MMS is now widely regarded as the treatment of choice for high-risk basal cell carcinoma and cutaneous squamous cell carcinoma because of its superior cure rates and tissue-sparing approach. Beyond its oncologic advantages, MMS allows precise clinicopathologic correlation and immediate reconstruction tailored to the final defect, contributing to favorable functional and cosmetic outcomes. As experience with the technique has expanded, so too has interest in adjunctive tools for preoperative tumor delineation and margin control, further refining patient selection and surgical accuracy. Overall, MMS represents an essential advance over conventional excision for selected cutaneous malignancies, offering an optimal balance between radical tumor clearance and preservation of normal tissue. Full article

Background: Bone and joint infections (BJIs) are a significant clinical challenge due to their tendency to recur, increased healthcare expenses, reduced quality of life, and mortality. Patients with BJIs are a heterogeneous group due to their different clinical presentations as well as patient-related risk factors. Empiric antibiotic regimens are commonly based on deductions from in vitro microbiologic findings, despite the fact that their relative efficacy and optimal antibiotic choices are underexplored. Methods: This retrospective cohort study included 521 patients surgically treated for BJIs at a specialized orthopedic infection unit between 2016 and 2023. Treatment strategies were guided by the Oral Versus Intravenous Antibiotics for Bone and Joint Infection (OVIVA) trial. All patients received a narrow-spectrum Gram-positive–targeted empiric systemic antibiotic regimen determined according to regional recommendations in collaboration with infectious disease specialists. The primary outcome was clinical failure within one year, with a minimum follow-up of 12 months. For the analyses, the patients were divided into three groups based on microbiological susceptibility: susceptible (SusEmp), non-susceptible (NonSus) and culture-negative (CulNeg) patients. Results: The three groups were found to differ significantly in seven patient-related factors: sex, age at primary operation (OP age), BMI, ASA group, diabetes status, peripheral arterial disease status (PAD), and endocrinopathy status (other than diabetes). In performing multivariate analyses, OP age was found to be independently associated with the overall failure rate (p = 0.04) and ASA group (p = 0.047), and PAD (p = 0.043) was independently associated with the secondary outcome of proximal amputation. Patients with non-susceptible pathogens (NonSus) had more than twice the odds of clinical failure (OR: 2.10; 95% CI: 1.12–3.95) and nearly fivefold higher odds of secondary proximal amputation (OR: 4.95; 95% CI: 1.41–23.2) compared with patients with susceptible pathogens (SusEmp). Conclusions: The current study demonstrates that a large group of patients can presumably be treated safely with a more restrictive narrow approach. More studies are needed to identify subgroups suited for the safe use of a narrow-spectrum empiric regimen, hereby reserving the broad-spectrum antibiotics for patients with the right indications and for whom it would have a positive effect on the clinical outcome. Such an approach would justify a more restrictive stewardship of broad-spectrum antibiotic use without negatively impacting patient outcomes. Full article

The job shop scheduling problem (JSSP) is a paradigmatic and strongly $\mathcal{NP}$-hard combinatorial optimisation problem that underpins production planning in modern manufacturing systems, and constraint programming (CP) has become one of the leading methodologies for tackling it. However, comparative studies of CP solvers for the JSSP have so far been restricted to a single benchmark family, a single instance-size range, or a single hardware setting, which limits the practical guidance they offer to both researchers and practitioners. This paper presents a controlled empirical evaluation of four state-of-the-art CP solvers—IBM ILOG CP Optimizer, Google OR-Tools (CP-SAT), Hexaly, and OptalCP—on the makespan-minimisation JSSP. The four engines are run with default parameters and a uniform 600 s wall-clock time budget on 332 instances drawn from nine canonical benchmark families (Fisher–Thompson, Lawrence, Adams–Balas–Zawack, Applegate–Cook, Yamada–Nakano, Storer–Wu–Vaccari, Taillard, Demirkol–Mehta–Uzsoy, and Da Col–Teppan), spanning sizes from $6\times 6$ to $1000\times 1000$ operations. OptalCP emerges as the most robust engine overall, certifying optimality on 191 of the 332 instances ($57.5\%$) with the smallest average optimality gap ($3.55\%$), followed by CP Optimizer (166 optima), OR-Tools (144), and Hexaly (116), while Hexaly dominates on industrial-scale problems and produces the bulk of the 22 new best-known upper bounds and one new best-known lower bound reported here. A Friedman test followed by Nemenyi post hoc comparisons confirms that OptalCP attains significantly smaller optimality gaps than the three other engines ($p<0.001$). Solver competitiveness depends sharply on instance size and the $n/m$ ratio, with square instances confirmed as the hardest case. In practical terms, these findings support an instance-aware approach to CP solver selection: OptalCP is the default choice for small to large instances of moderate aspect ratio, whereas Hexaly is preferable for industrial-scale problems with tens of thousands of operations or extreme $n/m$ ratios, where it is the only engine that reliably returns high-quality feasible schedules within the time budget. Full article

This study investigates the use of interpretable machine learning techniques to identify behavioral and demographic patterns associated with diabetes, based on structured population survey data from the Canadian Community Health Survey (CCHS). A decision tree classifier was applied to a dataset comprising $16,824$ respondents and 38 preprocessed features covering lifestyle, well-being, and sociodemographic factors. The model was optimized through grid search with five-fold stratified cross-validation, achieving a test accuracy of $61.3$% (mean $62.6$% ±$0.6$% across a $10\times 5$ repeated stratified cross-validation). Feature importance analysis revealed that age, alcohol consumption patterns, daily energy expenditure, and physical activity were the most influential factors associated with diabetes status, with the top three features exhibiting stable importance across all cross-validation folds. The model produced a set of 32 human-readable decision rules; a sensitivity analysis confirmed that these rules are stable across encoding choices and cross-validation folds. Several model variants were evaluated: a class-weighted decision tree, a logistic regression baseline, an age-only decision tree, and an age and sex logistic regression. The class-weighted model improved minority-class recall (from $0.25$ to $0.53$) at the cost of overall accuracy. A one-hot encoding sensitivity analysis showed that replacing ordinal label encoding of nominal variables with one-hot encoding produces virtually identical results (accuracy: $61.4$% vs. $61.3$%), confirming that the main rules are not artifacts of the encoding choice. Although the classification accuracy is moderate and not significantly better than a majority-class baseline (McNemar’s test, $p=0.455$), the extracted rules confirmed several known associations and revealed interactions between social and lifestyle variables. These rules are intended as hypothesis-generating population-level descriptors rather than validated clinical decision tools, and no causal inference is claimed. This approach demonstrates the value of rule-based models for exploratory public health research. Full article

This paper studies an optimal consumption–investment problem in a multi-asset financial market where risky assets returns incorporate returns history. Preferences are modelled using Epstein–Zin recursive utility, allowing a separation between risk aversion and intertemporal substitution. Using the well-known martingale optimality principle and forward–backward stochastic differential equations (FBSDEs), we obtain explicit closed-form solutions for the optimal strategy and value function. A sensitivity analysis illustrates the dependence of optimal policies and value function on key parameters, including risk aversion, elasticity of intertemporal substitution (EIS), memory horizon, learning intensity, and wealth-history parameters. The findings provide new insights into the interaction between behavioural features and dynamic portfolio choice in a multi-asset setting. Full article

Urban healthcare systems are fundamentally constrained by the mismatch between static resource configurations and dynamically evolving patient demand. Under the tiered healthcare system, traditional static planning methods struggle to capture the complexity and randomness of patient flows. While recent reinforcement learning (RL) approaches enable adaptive decision-making, they suffer from dimensionality explosion and unstable convergence due to massive action spaces and delayed spatiotemporal credit assignment in city-scale environments. To address this gap, we propose Twin–Game: a digital twin-driven hierarchical reinforcement learning (HRL) framework that formulates adaptive healthcare resource optimization as a “Twin Game” between a simulation-based game environment (Strategic Sandbox) and a hierarchical decision policy. First, we construct the “first twin”—an offline digital twin that serves as the Strategic Sandbox parameterized with Wuhan’s observed facility, population, and transportation data, while patient arrivals and disease profiles are generated synthetically under documented assumptions because individual-level clinical flow data are not publicly available. This environment integrates a dynamic gravity model with a two-way referral mechanism to represent the nonlinear coupling between hospital attractiveness, crowding levels, and patient choice behaviors. Second, we build the “second twin”—an Option-based HRL policy. The Manager (Macro-level Strategic Layer) uses a Deep Q-Network (DQN) for discrete spatial attention allocation; the Worker (Micro-level Execution Layer) uses Proximal Policy Optimization (PPO) for continuous, fine-grained controls such as bed expansion ratios and personnel scheduling. The two twins interact in a closed-loop game, performing strategy search and game evolution under complex constraints to optimize allocation. Experimental results from the Wuhan case indicate that the Twin–Game framework outperforms static baselines and single-layer RL in reducing average travel times, enhancing resource utilization, and improving tiered diagnosis and treatment within the simulation setting. The results should be interpreted as simulation-based decision-support evidence rather than direct clinical validation. This study provides a data-driven, game-theoretic decision support tool for building resilient urban healthcare systems. Full article

Meeting the physiological and psychological needs of shelter cats through appropriate care is critical to reducing stress and disease risk, as well as enabling positive homing outcomes. Shelter organisations across the British Isles provide care for many cats; however, little is known about the types of housing and husbandry approaches applied. This study, therefore, aimed to quantify current approaches to cat housing, husbandry, and general care practices, in addition to providing information relevant to local site capacity, considering reported practices against sector minimum standards where applicable. Nine hundred and sixty-one shelter organisations and/or sites caring for cats were identified and invited to complete an online survey including predominantly multiple-choice questions. A total of 393 unique responses were collected from employees and volunteers, and quantitative data were summarised descriptively. In most cases, the results provided evidence of majority alignment with sector standards, although substantial variations in reported practices were also consistently captured. While most responses described approaches supportive of meeting cats’ basic physiological needs (e.g., access to veterinary care and basic resources), psychological needs were addressed less consistently (e.g., general housing and husbandry approaches), potentially leading to poor welfare outcomes. Identified opportunities to better meet cats’ needs include more cat-friendly, low-stress approaches to pen cleaning and cat handling; greater and more consistent provisioning of within-pen resources; and improved approaches to multi-cat housing and associated decision-making. Additional opportunities to enhance both cat and human wellbeing include more structured intake and assessment processes and capacity management to support optimal cat-to-staff ratios, staff working hours, cat lengths of stay and more consistent access to isolation and emergency intake facilities. Full article

Accurate forecasting of groundwater level (GWL) fluctuations in drought-prone and data-limited regions remains a major challenge for sustainable groundwater management. The complexity of nonlinear and dynamic groundwater systems, influenced by spatiotemporal variability and limited observational data, further complicates the development of reliable predictive models. Groundwater is a critical resource for irrigation and domestic use in drought-prone northwestern Bangladesh, requiring accurate forecasting of GWL dynamics for sustainable management. To address this challenge, the present study evaluates seven deep learning (DL) approaches: GRU, LSTM, hybrid LSTM–GRU, and their Genetic Algorithm (GA)- and Particle Swarm Optimization (PSO)-variants, using time-series data from nine observation wells. The developed models were benchmarked against the widely used univariate time-series forecasting model, ARIMA. Model performance varied spatially. The GA-LSTM model performed best at Bagha–Arani (R = 0.879, IOA = 0.906, NRMSE = 0.149), while the standalone LSTM achieved superior results at Bagmara–Auchpara (R = 0.940, IOA = 0.958, NRMSE = 0.155). All DL models outperformed the benchmark ARIMA model across all locations. Overall, the best models achieved R = 0.724–0.940, IOA = 0.707–0.958, NRMSE = 0.149–0.285, and MAD = 0.369–1.369 m, indicating strong predictive skill. Optimization (GA, PSO) improved accuracy, particularly for GRU-based models, though LSTM remained competitive in several sites. Hybrid and optimized models required higher computational cost due to iterative tuning but often yielded improved accuracy. A CRITIC–EDAS multi-criteria decision-making framework, based on six statistical metrics, identified no universally superior model; instead, optimal choices varied by location. Selected models successfully forecasted future GWL trends, capturing temporal variability. The integrated modelling–ranking framework provides a robust, scalable approach for groundwater management in data-limited, drought-affected regions. Full article

Kidney transplantation is the treatment of choice for end-stage renal disease, although delayed graft function remains a frequent early complication with important clinical implications. Because early graft recovery depends on adequate perfusion, careful perioperative volume assessment and hemodynamic optimization are essential. Conventional markers such as interdialytic weight gain and estimated dry weight provide only indirect information on intravascular volume and may lead to pre-transplant misclassification of volume status. Complementary tools, including bioimpedance, natriuretic peptides, and congestion-focused ultrasound, may improve characterization of fluid distribution and hemodynamic stress, but none reliably define effective graft perfusion. Pressure-based parameters remain central to perioperative management; however, mean arterial pressure reflects systemic perfusion pressure and may be preserved despite reduced renal blood flow. Central venous pressure is an imprecise surrogate of intravascular volume and fluid responsiveness, with inconsistent associations with clinical outcomes across studies. In this context, flow-guided strategies based on dynamic indices of fluid responsiveness provide a more direct assessment of circulatory adequacy and have been associated, in selected studies, with improved early graft outcomes. Overall, the evidence supports a multimodal approach integrating volume assessment tools with pressure- and flow-oriented monitoring to optimize graft perfusion and early transplant outcomes. Full article

Walking tourism is an important form of thematic and sustainable tourism, especially in rural and naturally attractive destinations. It contributes to diversifying the tourist environments and improving destination management. This paper analyses the role of walking tourism in destination management and uses an integrated machine-learning model to predict tourist preferences. A key focus of this study is identifying the key factors influencing walking tourism preferences, including demographic, socioeconomic, behavioural, and activity-related variables. The methodology of this study is based on an integrated Machine Learning (ML) approach. CatBoostClassifier was used as the primary predictive model, and hyperparameter optimization was performed using Particle Swarm Optimization (PSO). Model interpretability was ensured through SHapley Additive exPlanations (SHAP) analysis, supported by CatBoost feature importance evaluation. This combination enables both high prediction accuracy and transparent explanation of variable influence. The research is based on 467 responses collected through an anonymous online survey. Results confirm that walking tourism is predominantly linked to natural and mountain experiences, which have strong implications for destination planning and tourism product development. The proposed model provides reliable predictions of tourist preferences under class imbalance conditions, achieving a macro-F1 score of 0.5114. Additionally, key factors influencing the choice of walking tours were identified, supporting destination managers in tourist segmentation, tourism product development, and sustainable allocation of destination resources. Full article

The Cuckoo Catfish Optimizer (CCO) is a recent swarm method with four built-in movement strategies. Its weakness is not the moves themselves but the way it chooses among them: a fixed chain of random-versus-threshold ($\mathrm{rand}>C$) tests that ignores how each agent is actually doing and keeps no memory of which move has been paying off. On harder, higher-dimensional problems, this rigidity drains diversity and the search stalls. We propose CCO-RL, which hands the choice of move to a small tabular Q-learning controller. For every agent at every iteration, the controller reads a 48-state summary of the agent’s crowding, its recent stagnation and how far the run has progressed, then picks one of the four moves. A bounded reward and a decaying $\epsilon$-greedy rule let it learn a policy online with no extra function evaluations. We test CCO-RL against the original CCO and eight popular metaheuristics on CEC2017 ($D=30,50$) and CEC2022 ($D=20$): 70 instances, 30 runs each. CCO-RL earns the best overall Friedman rank ($1.69$) and significantly beats every external competitor according to the Nemenyi test. It also finds the best mean design in three engineering problems. Full article