This research article contributes to the advancement of the medical and wellness tourism supply chain through the development of a robust mathematical model aimed at optimizing profitability. Rooted ...in the principles of open innovation, our study delves into innovative avenues to enhance the economic sustainability of the sector. By strategically integrating diverse medical services including health assessments, dental care, beauty treatments, and spa services into the tourism framework, our model captures the intricate interplay among various stakeholders. Our findings underscore the current adequacy of supply chain capacities. However, a substantial challenge emerges in light of an anticipated 30% upswing in tourist volume, which necessitates a corresponding augmentation of medical facilities without commensurate increases in hotel and tourist route capacities. Rectifying this imbalance becomes pivotal in accommodating forthcoming demands effectively. The study identifies several pivotal factors—tourist numbers, medical center capacities, and resultant profits for supply chain participants—as key drivers of overall profitability. Embracing the tenets of open innovation empowers stakeholders to cultivate collaboration, shared knowledge, and collaborative solutions that optimize these factors, thereby fostering the economic viability of the broader ecosystem. This research offers insightful strategies for resource allocation optimization, operational efficiency enhancement, and sustainable economic growth within the realm of medical and wellness tourism. This contribution is intended to resonate with practitioners, policymakers, and industry stakeholders.
This study introduces a modified differential evolution approach (MoDE) for evaluating the optimal objective and parameter values of the friction stir welding (FSW) process of dissimilar materials: ...AA5083 and AA6061. The aim of this study is to investigate the ultimate (UTS), maximum hardness (MH), and minimum heat input (HI) of the weld zone. The controlled welding parameters were shoulder diameter, rotation speed, welding speed, tilt angle, pin type, reinforcement particle type, and tool pin movement direction. The D-optimal experimental design method was used to create the experiment and obtain the mathematical model for optimizing the targeted objectives. The optimal rotational speed, welding speed, shoulder diameter, tilt angle, pin-type, additive type, and tool pin movement are 1162.81 rpm, 52.73 mm/min, 21.17 mm, 2.37 degrees, straight cylindrical, silicon carbide, and straight movement direction, respectively. The optimal values for UTS, MH, and HI are 264.68 MPa, 105.56 HV, and 415.26 °C, respectively. The MoDE outcome exceeded particle swarm optimization (PSO), the original differential evolution algorithm (DE), and the D-optimal design (experiment) results. The MoDE provides better UTS, MH, and HI than other approaches by an average of 8.04%, 4.44%, and 2.44%, respectively. In particular, when comparing results produced by using various approaches, we discovered that the MoDE results are 7.45%, 4.45%, and 3.50% better than PSO, DE, and the experimental results, respectively. All methods were evaluated for their reliability by comparing the results of actual experiments to those predicted by theory, and we discovered that the MoDE yielded the smallest percentage difference between the two, at 1.49%, while PSO and DE yielded differences of 5.19% and 3.71%, respectively.
This research aimed to design a transportation model for the international trade of agricultural products between Thailand, Laos, and Cambodia, known as the Great Mekong Subregion (GMS). Agricultural ...products are transported from a farmers’ cooperative to a foreign end market. The mathematical models GA, DE, and VaNSAS were developed to determine the optimal mode of transportation. The objectives of the proposed methods are to (1) maximize the total profit for the entire agricultural chain and (2) minimize the makespan or the arrival time of the containers to the end market in order to maintain the freshness of the agricultural product. The computational result shows that VaNSAS produces a 100 % optimal solution for small-size problem instances, while DE and GA produce a 63.63 % and 72.72 % optimal solution, respectively. For large-size problem instances, VaNSAS shows a profit that is higher than that of DE and GA by 10.53 % and 8.96 %, respectively, while it shows a makespan that is lower by 9.57 % and 7.20 %, respectively.
This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive ...samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.
In recent years, agriculture products have contributed to 28.75% of Thailand’s GDP. China, Vietnam, Myanmar, Cambodia, Laos and Vietnam are the main markets for agricultural products. The annual ...export volume exceeds 119,222 million THB. The majority of them are shipped over Thailand’s land borders to its neighbors. Small and medium-sized farmers make up more than 85% of those who produce agricultural items. Numerous scholars have studied the transportation methods used by the Greater Mekong Subregion (GMS) nations along the economic corridor, but the majority of them have concentrated on import–export operations involving sizable firms, which are not applicable to the transportation of agricultural products, particularly when attention is paid to small and medium-sized farmers. In this study, mixed-integer programming (MIP) is presented to design an agricultural product logistics network. In order to prolong the lifespan of the container used, the MIP’s primary goal is to maximize the total chain profit while maintaining the lowest container usage possible. The approach was developed to increase small and medium-sized farmers’ ability to compete. Small and medium-sized farmers bring their products to an agricultural product collecting center, also known as a container loading facility. After that, skilled logistics companies distribute the goods. In order to convey the goods to the final clients in neighboring nations, the proper locations of the containing loading centers, the correct transportation option and the borders must be decided. The issue was identified as multi-echelon location–allocation sizing (MELLS), an NP-hard problem that cannot be handled in an efficient manner. To solve a real-world problem, however, efficient techniques must be supplied. AMIS, an artificial multiple intelligence system, was created to address the suggested issue. AMIS was developed with the goal of leveraging a variety of methods for local search and development. There are several well-known heuristics techniques employed in the literature, including the genetic algorithm (GA) and the differential evolution algorithm (DE). With respect to the improved solutions obtained, the computational results show that AMIS exceeds the present heuristics, outperforming DE and GA by 9.34% and 10.95%, respectively. Additionally, the system’s farmers made a total of 15,236,832 THB in profit, with an average profit per container of 317,434 THB and an average profit per farmer of 92,344.44 THB per crop. The container loading center uses 48 containers, with a 5.33 container average per container loading center (CLC). The farmers’ annual revenues were previously less than 88,402 THB per family per year, so we can predict that the new network may increase customers’ annual income by 4.459% for each crop.
Agricultural sectors all over the world are facing water deficiencies as a result of various factors. Countries in the Greater Mekong Subregion (GMS) in particular depend on the production of ...agricultural products; thus, drought has become a critical problem in such countries. The average water level in the lower part of the Mekong River has been decreasing dramatically, resulting in the wider agricultural area of the Mekong watershed facing a lack of water for production. The construction of community reservoirs and associated water supply networks represents a strategy that can be used to address drought problems in the GMS. This study aims to solve the agricultural community reservoir establishment and water supply network design (CR–WSND) problem in Khong Chiam, Ubon Ratchathani, Thailand—a city located in the Mekong Basin. The CR–WSND model is formulated using mixed-integer programming (MIP) in order to minimize the cost of reservoir construction and water irrigation. An adjusted variable neighborhood strategy adaptive search (A-VaNSAS) is applied to a real-world scenario involving 218 nodes, and its performance is compared with that of the original variable neighborhood strategy adaptive search (VaNSAS), differential evolution (DE), and genetic algorithm (GA) approaches. An improved box selection formula and newly designed improvement black boxes are added to enhance the quality beyond the original VaNSAS. The results reveal that the quality of the solution from A-VaNSAS is significantly better than those of GA, DE, and VaNSAS (by 6.27%, 9.70%, and 9.65%, respectively); thus, A-VaNSAS can be used to design a community reservoir and water supply network effectively.
•The study introduces a novel machine learning approach to predict Ultimate Tensile Strength (UTS), Maximum Hardness (MH), and Heat Input (HI) of AA-7075 Friction Stir Welding material, utilizing ...four control parameters.•A Heterogenous Ensemble Machine Learning model (He-EM) is developed, amalgamating diverse Long Short-Term Memory (LSTM) models, and optimizing them using the Artificial Multiple Intelligence System (AMIS).•The approach's efficacy was tested using three datasets from actual experiments, each employing a different experimental design method: d-optimal design, Taguchi method, and Box–Behnken design.•The AMIS He-EM model outperformed existing techniques like GPR, SVM, HE-UWE, and HE-WEDE, improving accuracy by up to 35.12%.•Importantly, the proposed model showed robustness to changes in experimental design methods, maintaining consistent prediction accuracy across different designs.
This study addresses the research gap in materials science by developing an integrated predictive model for Ultimate Tensile Strength (UTS), Maximum Hardness (MH), and Heat Input (HI) in AA-7075 Friction Stir Welding (FSW). The aim is to enhance welding procedures, particularly in high-precision industries like aerospace and automotive. By incorporating four control parameters (Tilt Angle, Rotation Speed, Welding Speed, and Shoulder Diameter) and utilizing a Long Short-Term Memory (LSTM) machine learning model, a Heterogeneous Ensemble Machine Learning (He-EM) approach is developed. The Artificial Multiple Intelligence System (AMIS) optimizes the decision fusion strategy of each technique, ensuring the model's effectiveness. Validated using diverse experimental design methods and three datasets, the proposed AMIS He-EM model outperforms existing techniques (GPR, SVM, HE-UWE, and HE-WEDE) by significant margins (35.12%, 24.92%, 22.31%, and 15.48% respectively). The model's robustness is demonstrated, as its performance remains consistent across different experimental design methods. The key finding of this research is the remarkable improvement in accuracy for predicting UTS, MH, and HI in AA7075 FSW. This study highlights the importance of incorporating four control parameters and utilizing the D-optimal design for efficient exploration of the input parameter space. The implications of this research are profound, offering opportunities to optimize welding procedures, improve product performance, and streamline manufacturing processes in industries relying on AA7075 FSW.
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Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ...ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.
•Ensemble Machine Learning predicts Asiaticoside in Centella Asiatica.•Utilization of LSTM, GRU, Conv-LSTM, A-LSTM models.•Effective combination of machine learning results using the DE ...algorithm.•Factors impacting Asiaticoside in CAU: CO2, light, exposure time, cultivar, spectra.
This study proposes a novel heterogeneous ensemble machine learning methodology to predict the concentration of asiaticoside in Centella asiatica (CA-CA) in the context of the lack of an effective prediction method capable of accurately estimating its quantity based on various growing environmental factors. The accurate prediction of the asi-aticoside concentration in CA-CA holds great significance in optimizing cultivation practices and improving the efficacy of the derived medicinal products. The presented approach aims to address this crucial need by employing a diverse ensemble of machine learning techniques. The proposed model integrates several machine learning tech-niques, including the standard long short-term memory (LSTM), gated recurrent unit (GRU), convolutional long short-term memory (ConvLSTM), and attention-based LSTM, by utilizing a differential evolution algorithm to optimize the ensemble model's weights. The developed model is called the heterogeneous ensemble machine learning model (He-ML). Experimental results demonstrate that the He-ML achieves an im-pressive root-mean-square error (RMSE) value of 4.76, which is up to 12.48 % lower than the RMSE. The findings highlight the advantages of employing an ensemble model over a single model, as the ensemble model achieves an RMSE value that is 14.67 % lower than that of the individual machine learning model. The utilization of differential evolution as the decision fusion strategy provides a notable improvement over the unweighted average approach. As a result, the RMSE value achieved is 8.46 % lower than that obtained with the unweighted average (UWA) technique.
This research presents a novel algorithm for finding the most promising parameters of friction stir welding to maximize the ultimate tensile strength (UTS) and maximum bending strength (MBS) of a ...butt joint made of the semi-solid material (SSM) ADC12 aluminum. The relevant welding parameters are rotational speed, welding speed, tool tilt, tool pin profile, and rotation. We used the multi-objective variable neighborhood strategy adaptive search (MOVaNSAS) to find the optimal parameters. We employed the D-optimal to find the regression model to predict for both objectives subjected to the given range of parameters. Afterward, we used MOVaNSAS to find the Pareto front of the objective functions, and TOPSIS to find the most promising set of parameters. The computational results show that the UTS and MBS of MOVaNSAS generate a 2.13% to 10.27% better solution than those of the genetic algorithm (GA), differential evolution algorithm (DE), and D-optimal solution. The optimal parameters obtained from MOVaNSAS were a rotation speed of 1469.44 rpm, a welding speed of 80.35 mm/min, a tool tilt of 1.01°, a cylindrical tool pin profile, and a clockwise rotational direction.
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