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This study addresses a practical problem within a multi-level supply chain where a wide range of customers can be served through different strategies such as make-to-stock, make-to-order, or vendor-managed inventory. The customer demand is stochastic, and sensitive to pricing associated with different production-inventory strategies. We propose a two-stage stochastic mixed-integer non-linear programming model. In the first stage, decisions are made regarding the selection of production-inventory strategies and pricing to maximise the expected profit. The second stage involves decisions related to production, inventory, and distribution, which are used to evaluate the first-stage decisions under various scenarios with different levels of accuracy. To solve the model, a metaheuristic approach based on the Simulated Annealing algorithm is developed. To showcase the practical applicability of our model and solution approach, we use a real case study in a Canadian pulp and paper supply chain. The results revealed that both the production-inventory strategy assigned to customers and the sales price underwent changes across scenarios. Furthermore, we demonstrated that by implementing the SA algorithm, we could improve the initial profit by up to 1.43% through slight adjustments in the sales price and assigned strategies for customers. [ABSTRACT FROM AUTHOR]

Transporting ore from mines to ports is of significant interest in mining supply chains. These operations are commonly associated with growing costs and a lack of resources. Large mining companies are interested in optimally allocating resources to reduce operational costs. This problem has been previously investigated as resource constrained job scheduling (RCJS). While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention. RCJS with uncertainty is a hard combinatorial optimisation problem that is challenging for existing optimisation methods. We propose an adaptive population-based simulated annealing algorithm that can overcome existing limitations of methods for RCJS with uncertainty, including pre-mature convergence, excessive number of hyper-parameters, and the inefficiency in coping with different uncertainty levels. This new algorithm effectively balances exploration and exploitation, by using a population, modifying the cooling schedule in the Metropolis-Hastings algorithm, and using an adaptive mechanism to select perturbation operators. The results show that the proposed algorithm outperforms existing methods on a benchmark RCJS dataset considering different uncertainty levels. Moreover, new best known solutions are discovered for all but one problem instance across all uncertainty levels. [ABSTRACT FROM AUTHOR]

We examine an algorithm for the creation of proton-disordered ice cells based on a simulated-annealing (SA) scheme for molecular orientations. Application to defect-free ice Ih, a clathrate-hydrate structure, and a random polycrystalline ice Ih sample demonstrates the SA technique to be effective, attaining maximum HB connectivity using relatively short cooling simulations, thus serving as an alternative method for those cases in which the application of topology-based methods is inhibited. [ABSTRACT FROM AUTHOR]

This paper presents a novel solution approach for a variant of the job shop scheduling problem with machine unavailability due to both condition-based preventive maintenance and corrective maintenance following random breakdowns. We first provide an exact mathematical formulation of the problem under simplifying assumptions, namely that the number of breakdowns for each job position on each machine is known, the degradation rates are fixed, and the preventive and corrective maintenance durations are deterministic parameters. Moreover, to handle the more realistic case of stochastic machine degradation, random breakdowns, and uncertain maintenance durations, a simulation-optimisation algorithm is proposed. The real makespan function is first approximated using multiple surrogate measures, which are optimised through independent genetic algorithms. Then, the fittest solutions obtained from these surrogate measures are simulated, and the best among them is added to an elite list, which is included in the genetic algorithms' populations for the next iteration. Schedule robustness is ensured by using an objective function that consists of the weighted average of the expected makespan and its 90th percentile. Furthermore, to reduce the likelihood of falling into a local optimum, a stopping criterion based on simulated annealing is implemented. Numerical experimentation on extended benchmark instances confirmed the validity of the mathematical formulation and the favourable performance of the proposed simulation-optimisation algorithm in terms of computational time and solution quality. [ABSTRACT FROM AUTHOR]

The rapid growth of pharmaceutical refrigerated logistics poses sustainability challenges, including elevated costs, energy consumption, and resource inefficiency. Collaborating multiple depots can enhance logistics efficiency when standalone distribution centres have limited transport resources, i.e. refrigerated vehicles. However, the sustainable benefits and performance across different strategies remain unexplored. This study fills this research gap by addressing a refrigerated pharmaceutical routing problem. While many collaborative strategies prioritise economic and environmental benefits, our approach highlights a vital social indicator: maintaining vehicle flow equilibrium at each depot during collaboration. This ensures the stability of transport resources for all stakeholders, promoting sustainable collaborative logistics. The problem is formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Three collaborative strategies using Clustering VRP (CLUVRP) and improved Open VRP (OVRP) are proposed and compared. We develop two approaches to address traditional OVRP limitations in ensuring vehicle flow equilibrium at each depot. Our models consider perishable pharmaceuticals and time-dependent travel speeds. Three hybrid heuristics based on Simulated Annealing and Variable Neighborhood Search (SAVNS) are proposed and evaluated for efficacy. Computational experiments and a case study demonstrate distinct sustainable benefits across various strategies, offering valuable insights for decision-makers in the refrigerated logistics market. [ABSTRACT FROM AUTHOR]

Most existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment. [ABSTRACT FROM AUTHOR]

Thermal transport plays a pivotal role across diverse disciplines, yet the intricate relationship between amorphous network structures and thermal conductance properties remains elusive due to the absence of a reliable and comprehensive network's dataset to be investigated. In this study, we have created a dataset comprising multiple amorphous network structures of varying sizes, generated through a combination of the node disturbance method and Delaunay triangulation, to fine-tune an initially random network toward both increased and decreased thermal conductance C. The tuning process is guided by the simulated annealing algorithm. Our findings unveil that C is inversely dependent on the normalized average shortest distance L n o r m connecting heat source nodes and sink nodes, which is determined by the network topological structure. Intuitively, the amorphous network with increased C is associated with an increased number of bonds oriented along the thermal transport direction, which shortens the heat transfer distance from the source to sink node. Conversely, thermal transport encounters impedance with an augmented number of bonds oriented perpendicular to the thermal transport direction, which is demonstrated by the increased L n o r m . This relationship can be described by a power law C = L n o r m α , applicable to the diverse-sized amorphous networks we have investigated. [ABSTRACT FROM AUTHOR]

E-commerce is a continuously growing sector significantly affected by sustainability issues during the last few years. To deal with economic, environmental and social sustainability aspects, e-commerce platforms consolidate orders to pick-up several requests from the same location, defining the so-called Few-to-Many Pick-up and Delivery Vehicle Routing Problem (F-M VRPPD). The proposed contribution addresses the optimisation of this problem by developing a multi-objective simulated annealing algorithm distinguished by four tailored Local Search (LS) operators specifically developed to increase the probability to identify feasible solutions and decrease the computational time. This algorithm is validated with several instances of a case study e-commerce platform based in an European mountain region. Firstly, the original LS operators are compared to benchmark literature ones to solve identical problems, reporting better performance in 84% of these instances. Furthermore, for the most relevant scenarios significant results are presented and discussed concerning the economic, environmental and social performance of the defined solutions according to the characteristics of the instances, as the routes height profile and the drivers' metabolic energy consumption. The tri-dimensional Pareto frontiers suggest how through a slight worsening in the economic objective function it is possible to improve the social one by up to 18.3% on average. [ABSTRACT FROM AUTHOR]

This work describes optimizations of N-electron system wave functions by means of the simulated annealing technique within the doubly occupied configuration interaction framework. Using that technique, we minimize the energy variance of a Hamiltonian, providing determinations of wave functions corresponding to ground or excited states in an identical manner. The procedure that allows us to determine electronic spectra can be performed using treatments of restricted or unrestricted types. The results found in selected systems, described in terms of energy, spin, and wave function, are analyzed, showing the performance of each method. We also compare these results with those arising from more traditional approaches that minimize the energy, in both restricted and unrestricted versions, and with those obtained from the full configuration interaction treatment. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

We introduce a generalization of the σ-SCF method to approximate noncollinear spin ground and excited single-reference electronic states by minimizing the Hamiltonian variance. The new method is based on the σ-SCF method, originally proposed by Ye et al. [J. Chem. Phys. 147, 214104 (2017)], and provides a prescription to determine ground and excited noncollinear spin states on an equal footing. Our implementation was carried out utilizing an initial simulated annealing stage followed by a mean-field iterative self-consistent approach to simplify the cumbersome search introduced by generalizing the spin degrees of freedom. The simulated annealing stage ensures a broad exploration of the Hilbert space spanned by the generalized spin single-reference states with random complex element-wise rotations of the generalized density matrix elements in the simulated annealing stage. The mean-field iterative self-consistent stage employs an effective Fockian derived from the variance, which is utilized to converge tightly to the solutions. This process helps us to easily find complex spin structures, avoiding manipulating the initial guess. As proof-of-concept tests, we present results for Hn (n = 3–7) planar rings and polyhedral clusters with geometrical spin frustration. We show that most of these systems have noncollinear spin excited states that can be interpreted in terms of geometric spin frustration. These states are not directly targeted by energy minimization methods, which are meant to converge to the ground state. This stresses the capability of the σ-SCF methodology to find approximate noncollinear spin structures as mean-field excited states. [ABSTRACT FROM AUTHOR]

Platform-based product development is a cost-efficient approach to satisfy wide range of customer preferences. One important problem is the product platform configuration, in which two types of platform configuration are widely used, either module selection or module integration. The platform configuration based on module selection provides a broader solution space of platform selection, while module integration facilitates product platform commonality to gain economic benefits. Up to date, most research focused on the platform configuration based on module selection with a given module set. In this paper, we propose a new model to determine the optimal platform configuration for an external product family, considering both module selection and integration. The demand for the external product family is uncertain and assumed to follow normal distribution. A hybrid methodology combining simulated annealing and variable neighbourhood search is proposed. A numerical examination is carried out for the purpose of evaluation. The results show that the proposed model can provide a practically good solution. Main contributions of this research are two folds, one is the balance point between module selection and module integration in the platform configuration and the other is that we take into account the operation cost of module acquisition to the platform configuration problem. [ABSTRACT FROM AUTHOR]

In the degradation evaluation process of the crane girder structure under moving loads, the complex vibration response and low accuracy of health index standards are the main factors limiting the performance of the evaluation method. A method for identifying and evaluating damage to crane girder structures under moving loads has been proposed. In this method, optimized empirical wavelet transform (EWT) is used for decomposing the vibration signal of the crane girder structure to obtain sensitive components. A health indicator based on power spectrum maximum sequence is constructed by combining structural vibration simulation and response characteristics analysis under moving loads. A degradation index acquisition scheme based on an optimized sparse autoencoder is proposed, which utilizes a simulated annealing algorithm to optimize the model parameters of the sparse autoencoder. The effectiveness of the proposed method is verified through girder structure crack tests under moving loads. [ABSTRACT FROM AUTHOR]

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments. [ABSTRACT FROM AUTHOR]

Aiming at the multi-objective vehicle path planning problem with time windows (VRPTW), a Spark-based parallel Adaptive Large Neighborhood Search algorithm (Spark-ALNS) is proposed to solve it. The main design of the 4-point strategy: (1) Design a new simulated annealing algorithm cooling strategy to achieve a better jump out of the local optimal solution. (2) Adopt CW initialization to accelerate the convergence speed. (3) Use three destruction operators and three repair operators to implement local path optimization. (4) A new parallel strategy is proposed to improve the algorithm's accuracy and reduce the running time. To illustrate the algorithm's effectiveness, the arithmetic example in Solomon is used as an example. The experimental results show that the proposed Spark-ALNS can find better solutions, get the known optimal solutions for 41 out of 56 instances, and find new optimal solutions for 31 algorithms, which outperforms other evolutionary algorithms. The runtime is 3–5 times better than other parallel algorithms and is able to solve VRPTW effectively. [ABSTRACT FROM AUTHOR]

AbstractAccurate estimation of frictional pressure loss in an annulus is crucial for selecting appropriate pump systems, determining horsepower requirements, and controlling hole cleaning. Generally, when calculating the friction factor, the temperature of the fluid and the rotation of the inner pipe are not considered, which can lead to errors in estimating the pressure difference in the annulus. In this study, to ensure accurate pressure drop calculations, the effects of temperature and inner pipe rotation, along with other parameters, were included. An extensive experimental study was conducted at Izmir Katip Celebi University’s Flow Loop using two Carboxymethyl cellulose (CMC) solutions, across various fluid velocities, fluid temperatures, diameter ratios, and inner pipe rotation speeds. A dimensional analysis was performed to identify the parameters that affect friction factors in the annulus. Finally, friction factor equations were developed based on the experimental data using optimization algorithms such as interior point (fmincon), genetic, minimax, and simulated annealing. The findings showed that the predictions from all the algorithms tested in the study closely matched the experimental results; however, the interior point algorithm (fmincon) provided the best performance, with an average absolute percentage error of 9.4%. [ABSTRACT FROM AUTHOR]

ABSTRACT The limit equilibrium method has been widely used in the study of searching the slip surface of slopes. However, the method ignores the deformation characteristics of the rock mass and assumes that the shape of the slip surface is circular, which is quite different from the actual situation of the slope. For this reason, this paper proposes a fast search method for noncircular slip surface considering the deformation characteristics of the rock mass. The method is able to calculate the compression and shear deformation energies stored in the slip surface, as well as the virtual displacement generated by the slide mass when the slope is in a critical equilibrium state. The direction of motion of the slide mass is further calculated from the magnitude of the virtual displacement. In addition, this paper improves the generation of new solutions in the simulated annealing (SA) algorithm for the structural characteristics of the slip surface of the slope, thus achieving a fast search of the slip surface. Finally, the method of this paper is compared with the test question of ACADS and the simulation results of the finite difference method (FDM) to verify the effectiveness of the method of this paper. [ABSTRACT FROM AUTHOR]

Generating uniform design tables (UDTs) is the first step to experimenting efficiently and effectively, and is also one of the most critical steps. Thus, the construction of uniform design tables has received much attention over the past decades. This paper presents a new algorithm for constructing uniform design tables: restart discrete dynamical evolutionary algorithm (RDDE). This algorithm is based on a well-designed dynamical evolutionary algorithm and utilizes discrete rounding technology to convert continuous variables into discrete variables. Considering the optimization of UDT is a multi-objective optimization problem, RDDE uses Friedman rank to select the optimal solution with better comprehensive comparison ranking. RDDE also utilizes a simulated annealing-based restart technology to select control parameters, thereby increasing the algorithm's ability to jump out of local optima. Comparisons with state-of-the-art UDTs and two practical engineering examples are presented to verify the uniformity of the design table constructed by RDDE. Numerical results indicate that RDDE can indeed construct UDTs with excellent uniformity at different levels, factors, and runs. Especially, RDDE can flexibly construct UDTs with unequal intervals of factors that cannot be directly processed by other designs of experiment. [ABSTRACT FROM AUTHOR]

Phononic crystals are periodically artificial composite structures that have garnered widespread attention for their bandgap properties and potential applications in wave propagation control, offering novel solutions for engineering vibration and noise reduction. The design of these materials is a current research focus. This study takes two-dimensional layered phonon crystal structure as an example, presents an innovative method for designing two-dimensional layered phononic crystal structures, utilizing LightGBM and an improved genetic algorithm. Initially, the LightGBM algorithm was employed for bandgap prediction of the phononic crystals, with the structural arrangement vectors subjected to categorical feature processing, and hyperparameter tuning conducted through the simulated annealing algorithm, achieving a prediction accuracy with an overall error of no more than 2% within 1/3134 of the traditional finite element method computation time. Subsequently, a design method integrating a genetic algorithm based on an elite preservation strategy was introduced. Using the environmental vibration issue caused by subway operation as an example, a fitness function optimized for bandgap width was established, resulting in a population with structural bandgaps covering the 30 ~40 Hz frequency range. Finally, finite element analysis of one structure verified its bandgap characteristics align closely with expectations. This research provides a new solution for the efficient optimization design of multiple phononic crystal structure schemes. [ABSTRACT FROM AUTHOR]

Infrared hyperspectral instrument is a complex optical instrument. The requirement for the application of the infrared hyperspectral interferometer is fine calibration accuracy. The accuracy of the spectral frequency of the infrared hyperspectral interferometer is a key influencing factor of the radiometric calibration accuracy. In order to meet the quantitative application, the spectral calibration accuracy is generally within 10ppm. For infrared hyperspectral instruments in geostationary orbit, the focal plane has the characteristics of large array and small detector size. When the detector size of an infrared hyperspectral interferometer is in the geostationary orbit whether the detector as a point detector will affect the spectral calibration accuracy. In this paper, we simulate and analyze the influence of the shape of the detector (square and point detector) on the spectral calibration accuracy. This paper first builds an infrared hyperspectral observation and calibration simulation model. The brightness temperature deviation between the calibration spectrum and the input spectrum is close to 0 K, which shows the rationality of building the simulation model. Then, in the process of spectral calibration, two methods, square detectors and point detectors, are adopted, respectively. The spectral calibration of the square detector needs to calculate the line shape function of the instrument according to the detector position. Regarding point detector spectral calibration, this paper proposes a spectral calibration parameter optimization algorithm based on the optimization algorithm, which does not depend on the physical parameters of the instrument, but only uses the observed simulated spectrum and the reference spectrum. The results show that the spectral calibration accuracy of the point detector based on the optimization method can be close to 0ppm, which is consistent with the spectral calibration accuracy of the square detector. [ABSTRACT FROM AUTHOR]

This study addresses the set orienteering problem with mandatory visits (SOPMV), a variant of the team orienteering problem (SOP). In SOPMV, certain critical sets must be visited. The study began by formulating the mathematical model for SOPMV. To tackle the challenge of obtaining a feasible route within time constraints using the original MILP approach, a two-stage mixed-integer linear programming (MILP) model is proposed. Subsequently, a simulated annealing (SA) algorithm and a dynamic programming method were employed to identify the optimal route. The proposed SA algorithm was used to solve the SOP and was compared to other algorithms, demonstrating its effectiveness. The SA was then applied to solve the SOPMV problem. The results indicate that the solutions obtained using SA are superior and more efficient compared to those derived from the original MILP and the two-stage MILP. Additionally, the results reveal that the solution quality deteriorates as the ratio of the set of mandatory visits increases or the maximum allowable travel time decreases. This study represents the first attempt to integrate mandatory visits into SOP, thereby establishing a new research direction in this area. The potential impact of this research is significant, as it introduces new possibilities for addressing complex combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

Airport transshipment centers play a pivotal role in global logistics networks, enabling the swift and efficient transfer of cargo, which is essential for maintaining supply-chain continuity and reducing delivery times. The handling of irregularly shaped air cargo containers presents new constraints for automated guided vehicles (AGVs), as these shapes can complicate loading and unloading processes, directly impacting overall operational efficiency, turnaround times, and the reliability of cargo handling. This study focuses on optimizing the scheduling of AGVs to enhance cargo-handling efficiency at these hubs, particularly for managing irregular air cargo containers. A mixed-integer linear programming (MILP) model is developed, validated for feasibility with the Gurobi solver, and designed to handle large-scale operations. It incorporates a novel approach by integrating a simulated annealing optimized genetic algorithm (GA). The experimental results demonstrate that the designed algorithm can solve models of considerable size within 8 s, offering superior time efficiency compared to the solver, and an average solution quality improvement of 12.62% over the genetic algorithm, significantly enhancing both the model's efficiency and scalability. The enhanced AGV scheduling not only boosts operational efficiency but also ensures better integration within the global logistics framework. This research provides a robust foundation for future advancements in logistics technology, offering both theoretical and practical insights into optimizing complex transportation networks. [ABSTRACT FROM AUTHOR]

In order to improve the detection range and imaging resolution of dipole remote detection logging, an optimal nonlinear frequency modulation (ONLFM) excitation method is proposed in this paper. In this method, the optimal waveform model of the sound source is designed with objective functions of SNR and resolution to obtain the highest resolution under the condition of the required SNR. This optimal model is a multi-constraint optimization problem, and the simulated annealing method has been used to solve it. Solving the optimal model can obtain the optimal spectrum, and the ONLFM waveform can be designed by using the stationary phase principle. Compared with the electric pulse sound source used in traditional technology, this ONLFM sound source can improve the energy and extend the frequency band range of the reflected wave, which can provide the higher resolution and SNR. The simulation results show that the ONLFM sound source can effectively improve the SNR and resolution of the reflected wave, and the detection range and imaging resolution of dipole shear wave remote detection will be improved. [ABSTRACT FROM AUTHOR]

As the societal demand for precision in industrial robot operations increases, calibration can enhance the end-effector positioning accuracy of robots. Sampling data optimization plays an important role in improving the calibration effect. In this study, a robot calibration sampling point optimization method based on improved robot observability metrics and a Binary Simulated Annealing Algorithm is proposed. Initially, a robot kinematic model based on the Product of Exponentials (POE) model and a generalized error model is established. By utilizing the least squares method, the ideal pose transformation relationship between the robot's base coordinate system and the laser tracker measurement coordinate system is derived, resulting in an error calibration model based on spatial single points. An improved robot observability metric combined with the Binary Simulated Annealing Algorithm (BSAA) is introduced to optimize the selection of calibration sampling data. Finally, the robot's parameter errors are obtained using a nonlinear least squares method. Experimental results demonstrate that the average end-effector positioning error of the robot after calibration can be reduced from 0.356 mm to 0.254 mm using this method. [ABSTRACT FROM AUTHOR]

Performing reliable calculations of power system dynamics requires accurate models of generating units. To be able to determine the parameters of the models with the required precision, a well-defined testing procedure is used to record various unit transient signals. Unfortunately, the recorded signals usually contain discontinuities, which complicates the removal of the existing harmonic interferences and noise. A set of four transient signals recorded during typical disturbance tests of a 200 MW power-generating unit was used as both training and research material for the signal denoising/interference removal methods compared in the paper. A systematic analysis of the measured transient signals was conducted, leading to the creation of a coherent mathematical model of the signals. Next, a method for denoising power-generating unit transient signals is proposed. The method is based on Sparsity-Assisted Signal Smoothing (SASS) combined with optimization algorithms (simulated annealing and Nelder-Mead simplex) and is called an optimal SASS method. The proposed optimal SASS method is compared to its direct Linear Time-Invariant (LTI) competitors, such as low-pass and notch filters. The LTI methods are based on the same filter types (Butterworth filters) and zero-phase filtering principle as the SASS method. A set of specially generated test signals (based on a developed mathematical model of the signals) is used for the performance evaluation of all presented filtering methods. Finally, it is concluded that—for the considered class of signals—the optimal SASS method might be a valuable noise removal technique. [ABSTRACT FROM AUTHOR]

Due to the low signal-to-noise ratio of raw data, the reliability of data and the accuracy of acoustic source localization are severely affected by fiber optic acoustic sensing technology. To address this issue, this study optimizes the wavelet thresholding method. Firstly, a novel thresholding function is proposed, which achieves denoising while preserving key information through shape adjustment factors. It combines the advantages of both hard and soft threshold functions and has high flexibility and controllability. Secondly, an adaptive threshold calculation method is introduced, utilizing an improved simulated annealing algorithm to optimize threshold selection, reducing the algorithm’s dependence on threshold parameter selection. Through simulation experiments, it has been verified that this research method effectively suppresses noise in the signal and improves data availability. Compared to the original methods, this approach significantly improves the signal-to-noise ratio and demonstrates robustness in simulated tests of real signals. [ABSTRACT FROM AUTHOR]

DS theory is widely used in the fields of decision-level information fusion. To suppress the counter-intuitive results such as "one vote rejection" and "trust bias" encountered when utilizing DS theory of combination, as well as the conflict issue between elements within the evidence, a novel method for effectively handling with conflicting evidence is proposed. The method first introduces the representations of self-conflict and mutual-conflict to characterize conflicts in evidence combination. And then according to Pignistic probability distance, an algorithm for correcting self-conflict based on the simulated annealing adaptive algorithm is designed. A correction method for mutual-conflict is next presented on the basis of Pignistic similarity. Finally, the combination result can be obtained utilizing the DST combination rule. Some numerical examples provided show the efficiency and rationality of the proposed method. [ABSTRACT FROM AUTHOR]

The excessive travel demand in the metro system during peak hours may cause platform congestion and reduce the comfort of the passengers. Thus, this article proposes an approach for integrally optimizing the train timetable and passenger control on an oversaturated metro line, to improve the comfort of passengers on the platform. Specifically, taking the short-term impact of the boarding/alighting passengers into account, a novel quantitative assessment method of the perceived waiting time, considering the impact of the platform passengers' density and passenger control strategy, named the generalized waiting time, is proposed. A mixed-integer nonlinear programming model is formulated to minimize the generalized waiting time, and a heuristic algorithm based on the adaptive simulated annealing genetic algorithm and the adaptive large neighbourhood search (ASAGA-ALNS) is designed to find high-quality solutions. The results of numerical experiments show that the method can alleviate platform congestion, while improving the comfort of waiting passengers. [ABSTRACT FROM AUTHOR]

Background and aim: China has used traditional Chinese medicine (TCM) to treat diseases for more than 2000 years. Traditionally, TCMs in medicine cabinets are arranged alphabetically or on the basis of experience, but this arrangement greatly affects dispensing efficiency. However, owing to the unique properties and qualities of TCM, very few automatic approaches or systems have specifically addressed TCM dispensing problems. Therefore, it is necessary to establish a method of optimizing the traditional Chinese medicine placement scheme (TCMPS) via computer algorithms to improve the work efficiency of pharmacists. Methods: A prescription dataset from a hospital in 2022 was obtained, and the association rule algorithm (ARA) was used to calculate the frequency of use for each type of TCM and the associations between different types of TCMs. On the basis of these association and frequency data, the optimal TCMPS was calculated using the simulated annealing algorithm (SAA) and then verified using the prescription dataset from 2023. Results: A total of 10,601 prescriptions were collected in 2022, involving 360 different TCMs, and each prescription contained an average of 9.485 TCMs, with Danggui (3628) being the most frequently used. When the threshold of support was set to 0.05 and the confidence was set to 0.8, 78 couplet medicines used in orthopedics clinics were found through ARA. When the threshold value of support was set to 0, the confidence was set to 0, and the rule length was 2, a total of 129,240 rules were obtained, indicating support between all pairwise TCMs. The TCMPS, calculated using SAA, had a correlation sum of 14.183 and a distance sum of 3.292. The TCMPS was verified using a prescription dataset from 2023 and theoretically improved the dispensing efficiency of pharmacists by approximately 50%. Conclusions: In this study, the ARA and SAA were successfully applied to pharmacies for the first time, and the optimal TCMPS was calculated. This approach not only significantly improves the dispensing efficiency of pharmacists and reduces patient waiting time but also enhances the quality of medical services and patient satisfaction, and provides a valuable reference for the development of smart medicine. [ABSTRACT FROM AUTHOR]

AbstractThe goal of terrain viewshed point selection is to identify multiple viewpoints within a specific area that offer optimal visibility. However, as the resolution of terrain data becomes finer, the number of data points to be processed grows significantly, leading to a sharp rise in computational demands for viewshed point selection. This paper introduces the Efficient Precise Viewshed Point Selection (EPVPS) algorithm, which provides an important reference for multi-viewpoint planning. First, the Empty Circles based K-means (ECKM) algorithm is utilized to determine the initial centers for candidate viewpoint clustering. Second, a new viewpoint evaluation metric, Weighted Coverage Overlap Metric (WCOM), is proposed. This metric divides the viewshed of candidate points into coverage contribution points and overlap contribution points, computes WCOM based on this division, and stores the values in a min-heap. Finally, viewpoints are added and deleted from clusters by adjusting the sets of coverage contribution points and overlap contribution points. The EPVPS algorithm is compared with the Candidate Viewpoints Filtering (CVF) algorithm and the Simulated Annealing (SA) algorithm. Experimental results demonstrate that the EPVPS algorithm outperforms the others in computational efficiency, coverage rate, and overlap rate. [ABSTRACT FROM AUTHOR]

We studied a variant of the vehicle routing problem (VRP) arising in last-mile distribution, called the multi-depot two-echelon vehicle routing problem with delivery options (MDTEVRP-DO). The MDTEVRP-DO involves two decision levels: (i) designing routes for a fleet of vehicles located in multiple depots to transport customer demands to a set of satellites and (ii) routing a fleet of vehicles from the satellites to serve the final customers. Nowadays, a relevant feature of the problem characterizing delivery services is that customers can collect their packages at pickup stations near their homes or workplaces. We designed an effectively simulated annealing (SA) heuristic for the problem. The new algorithm was extensively tested on benchmark instances from the literature, and its results were compared with those of start-of-the-art algorithms. The results show that the proposed SA obtains 30 out of the 36 best solutions for the MDTEVRP-DO benchmark instances. Moreover, its computation performance is superior to state-of-the-art algorithms for the MDTEVRP-DO. [ABSTRACT FROM AUTHOR]

A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced. [ABSTRACT FROM AUTHOR]

Data mining and machine learning require feature selection because features can dramatically improve model performance. In contrast, there are no polynomial solutions for selecting a subset feature. It is possible to achieve this by using meta-heuristic algorithms, specifically population-based algorithms that are able to provide a subset of features that is optimal and not exact. Meta-heuristic algorithms face challenges such as staying in local minima, easily falling into local optimum, weakly global searchability, premature convergence, and slow convergence speeds. However, recent research has limitations such as high complexity and weak initialization. In order to overcome these limitations, a three-stage model is proposed. In the first stage, the correlation of features and the correlation of features with class are considered during feature selection and used to create the initial population in the pathfinder optimization algorithm (PFA). PFA is a population-based algorithm and has some drawbacks, in the last iterations, the fluctuation rate (A) and vibration vector (ε) parameters converge to 0, and finding a new solution is impossible. As a second stage, a fuzzy inference system is designed to adjust these parameters adaptively and is called fuzzy-pathfinder optimization (FPO). In the third stage, FPO is used to select relevant features based on classification error, proportion of selected features, and redundancy. Finally, different algorithms such as simulated annealing (SA), differential evolutionary (DE), genetic algorithm (GA), particle swarm optimization (PSO), PFA, estimation of distribution algorithm (EDA), and symmetrical uncertainty criterion (SUC-PSO) are used for comparison. Based on the results, the proposed model is able to reach an average accuracy of 96% on average. Based on a comparison of the proposed algorithm with SA, DE, GA, PSO, PFA, EDA, and SUC-PSO, the objective function is improved by 17.3%, 5.6%, 3.0%, 4.5%, 5.0%, 0.5%, and 1.2%, respectively. The use of comprehensive objective functions, the adaptive adjustment of parameters, and the creation of a targeted initial population are key strengths of FPO. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) path planning algorithm often suffers from low population diversity and rapid early convergence, which can lead to the algorithm easily falling into local optima and affecting its stability. This paper proposes an improved PSO algorithm with two key enhancements: (1) the introduction of a particle reactivation module during the iteration process, and (2) the incorporation of the Simulated Annealing (SA) concept during the global optimal solution update phase. These improvements enhance the diversity of the particle population, slow down the early convergence rate of the algorithm, reduce the probability of the algorithm getting trapped in local optima, and increase its overall stability. Experiments were conducted to compare the proposed algorithm with the standard PSO algorithm, genetic algorithms, and other improved PSO path planning algorithms. The results indicate that the improved algorithm shows superior performance in both average path length and algorithm stability. [ABSTRACT FROM AUTHOR]

Accurate analysis of creep effects in finite element analysis depends on whether the exponential form of the creep coefficient can accurately reproduce the creep coefficient curve. This paper aims to develop a high-precision fitting method for arbitrary creep coefficient curves to achieve accurate finite element creep analysis. The core issue was examined, and an optimization algorithm was proposed. First, the simulated annealing algorithm was used to search for the global optimal solution, followed by the application of the Levenberg-Marquardt algorithm for precise calculations. Two examples were employed to verify the method. The results showed that: (1) The proposed creep coefficient curve fitting method could accurately fit the creep coefficient curve. (2) The case studies showed that the algorithm achieved a correlation coefficient ( R) above 0. 999 9 when fitting the creep coefficient curves according to highway and railway standards, indicating high fitting accuracy and strong general applicability. (3) The proposed algorithm featured a simple structure and clear hierarchy, allowing for easy implementation using common programming languages. This method provides valuable insights for the development of finite element program creep analysis modules or related scientific research on concrete creep behavior. [ABSTRACT FROM AUTHOR]

The non-waiting constraint is a crucial factor in industries with continuous production flows. Recognizing the significance of this constraint in the manufacturing process, we propose several approaches to minimize the maximum tardiness in the no-wait manufacturing flow shop scheduling problem. This research presents two main approaches: an exact method that utilizes mixed integer linear programming and an approximate method based on constructive heuristics and hybrid meta-heuristics. To tackle the problem, we introduce multiple heuristics and hybrid improved meta-heuristics based Nawaz Enscore Ham, greedy randomized adaptive search procedure and genetic algorithm. Furthermore, we propose three hybrid meta-heuristics based on the simulated annealing approach. To validate the effectiveness and robustness of our methods, we conducted experiments on multiple instances of the flow shop problem proposed by Taillard. The results demonstrate that the hybrid algorithm, which combines a greedy randomized adaptive search procedure with the insertion procedure and simulated annealing, exhibits strong performance. In fact, this algorithm achieved a success rate of 72 % across 200 test instances. It outperformed the other two meta-heuristics, with a minimum average relative percentage deviation of only 0.0039 % . [ABSTRACT FROM AUTHOR]

High-value payment systems (HVPSs) are typically liquidity intensive because payments are settled on a gross basis. State-of-the-art solutions to this problem include algorithms that seek netting sets and allow for ad hoc reordering of submitted payments. This paper introduces a new algorithm that explores the entire space of payments reordering to improve the liquidity efficiency of these systems without significantly increasing payment delays. Finding the optimal payment order among the entire space of reorderings is, however, an NP-hard combinatorial optimization problem. We solve this problem using a hybrid quantum annealing algorithm. Despite the limitations in size and speed of today's quantum computers, our algorithm provides quantifiable liquidity savings when applied to the Canadian HVPS using a 30-day sample of transaction data. By reordering batches of 70 payments, we achieve an average of Canadian (C) $240 million in daily liquidity savings, with a settlement delay of approximately 90 seconds. For a few days in the sample, the liquidity savings exceed C$1 billion. Compared with classical computing and with current algorithms in HVPS, our quantum algorithm offers larger liquidity savings, and it offers more reliable and consistent solutions, particularly under time constraints. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00314. [ABSTRACT FROM AUTHOR]

Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of "last-mile" delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes. [ABSTRACT FROM AUTHOR]

This paper proposes an AI-based module for a loading pattern (L/P) optimization algorithm applied to the i-SMR, designed for flexible operation. The AI module can be used as a surrogate model in the simulated annealing (SA) screening process, which allows for more efficient optimization. The convolution neural network (CNN) model was trained using reactor core L/Ps and corresponding core parameter values derived from a realistic core simulation code. For load-following operations, we selected core parameters such as control rod insertion depth, radial peaking factor, axial shape index, and effective multiplication factor. To calculate the objective function of an L/P during the SA process using core design codes, it takes approximately 3 s, while the AI-based module can predict the objective function within about 0.1 ms. During the prediction of selected parameters, we discovered two factors affecting prediction accuracy. First, the model exhibited a significant increase in error when trained on dataset containing negative values. Second, utilizing batch normalization (BN) layer and squeeze and excitation (SE) module, intended to improve accuracy, resulted in a decrease in performance of the model. Our study demonstrated that the CNN-based model achieves excellent prediction accuracy and has an ability to accelerate optimization algorithms by taking advantage of artificial intelligence's inherent computational speed. [ABSTRACT FROM AUTHOR]

A new method for the generation of axial power distributions using artificial neural network (ANN) technique and real-time measured planar radial peaking factor, Fxy (hereinafter 'Live Fxy') method for digital core protection system of pressurized water reactors is presented. ANN with Simulated Annealing (SA) technique to find global optimum solution was used to calculate core average axial power distribution. In Live Fxy method, axial node-wise Fxys were calculated by multiplying pin/box factors to the measured assembly powers inferred from in-core detector signals without calculating detailed 3D power distributions. The hot pin power distribution for DNBR and LPD was then obtained by multiplying the core average axial power distribution and Fxys at axial nodes. The validation of the method was performed for various core conditions (e.g. core power levels, control rod positions, etc.) of Korean OPR1000 power plant. The result showed a decrease in RMS errors by 2.2% ~ 7.0% (3.6% on average), and the minimum thermal margins for DNBR and LPD were increased by 6.4% and 15.6%, respectively. The application of the method would improve the accuracy of axial power distribution and thermal margin, contributing to the operability and safety of digital core protection system. [ABSTRACT FROM AUTHOR]

This paper addresses a no-wait flowshop scheduling problem with total tardiness minimization. We present a mixed-integer linear programming formulation with positional decision variables and a new constraint programming model to solve the problem as exact approaches. As an alternative solution procedure, we develop an innovative Reactive Iterated Greedy (RIG) algorithm that presents a dynamic mechanism to adjust the number of jobs to be removed from the current permutation in the destruction phase. We carried out computational experiments on a set of 800 test instances available in the literature to evaluate the performance of the proposed RIG algorithm in comparison with five solution approaches (Mixed-Integer Linear Programming, Constraint Programming, Block Simulated Annealing, Variable Neighborhood Descent, and Iterated Greedy). The used performance criterion is the Relative Deviation Index (RDI). The RIG algorithm returned an average RDI of 5.0%, representing the best results among the solution procedures under comparison. Computational results pointed out that our proposal outperformed the mathematical model and other metaheuristics available in the literature with statistical significance. [ABSTRACT FROM AUTHOR]

We describe a new quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP) protocol for the prediction and refinement of crystal structures, including its design, benchmarking, and application to seven organic HCl salts. Five HCl salts with a limited number of low-energy conformations were chosen as model systems for benchmarking: betaine HCl, glycine HCl, D -alanine HCl, guanidine HCl, and aminoguanidine HCl; two were chosen for blind tests: N,N′-dimethylglycine HCl and metformin HCl. The QNMRX-CSP protocol uses experimental 35Cl solid-state NMR (SSNMR) spectra and X-ray diffraction (XRD) data in tandem with Monte Carlo simulated annealing and dispersion-corrected plane-wave density functional theory (DFT-D2*) calculations. The protocol comprises three modules: (i) the assignment of motion groups, (ii) a Monte Carlo simulated annealing algorithm for generating tens of thousands of candidate structures, and (iii) DFT-D2* geometry optimizations of structural models and concomitant computation of 35Cl EFG tensors. Key benchmarked metrics are used for retaining the best candidate structures, including unit cell parameters, static lattice energies, and EFG distances (ΓEFG). The protocol is shown to generate structural models that are excellent matches with experimental crystal structures that have been DFT-D2* geometry optimized, as validated by crystallographic R-factors (R) and root-mean squared distances (RMSDs) of atomic positions that are well below 10% and 0.2 Å, respectively. Finally, consideration is given to the use of the QNMRX-CSP protocol as a standalone technique or in concert with other NMRX methods and Rietveld refinements, and possible applications employing other quadrupolar nuclei (i.e., 14N and 17O). [ABSTRACT FROM AUTHOR]

Objective To use neural networks and optimization algorithms, establish an obesity level prediction model to assess obesity risk. Methods Perform correlation analysis on 2 111 recorded data collected from participants aged between 14 and 61 years old in Mexico, Peru, and Colombia, and establish a BP neural network obesity level prediction model. At the same time, optimize the number of hidden nodes and transfer function of the model through pruning to find the optimal network structure. In addition, the genetic algorithm and the simulated annealing algorithm were used to optimize the weights and thresholds of the model, ultimately establishing a high-precision and practical GASA-BP neural network obesity level prediction model. Results The R2 of the prediction model was 0.975 1, and the MAE was 0.352, indicating high prediction accuracy and strong practicality. In the process of predicting obesity levels in the model, weight index was the most important, with a correlation of 0.913 with obesity levels. The correlation between overweight members in the family was also relatively strong, with a correlation of 0.505. Conclusion The GASA-BP neural network prediction model performs better than other models in predicting obesity levels, and can make the most accurate prediction of obesity levels, providing guidance and reference for personalized obesity assessments and subsequent prevention and control measures. [ABSTRACT FROM AUTHOR]

This paper explores a multi-objective, multi-period integrated routing and scheduling problem under uncertain conditions for distributing relief to disaster areas. The goals are to minimize costs and maximize satisfaction levels. To achieve this, the proposed mathematical model aims to speed up the delivery of relief supplies to the most affected areas. Additionally, the demands and transportation times are represented using fuzzy numbers to more accurately reflect real-world conditions. The problem was formulated using a fuzzy multi-objective integer programming model. To solve it, a hybrid algorithm combining a multi-objective ant colony system and simulated annealing algorithm was proposed. This algorithm adopts two ant colonies to obtain a set of nondominated solutions (the Pareto set). Numerical analyses have been conducted to determine the optimal parameter values for the proposed algorithm and to evaluate the performance of both the model and the algorithm. Furthermore, the algorithm's performance was compared with that of the multi-objective cat swarm optimization algorithm and multi-objective fitness-dependent optimizer algorithm. The numerical results demonstrate the computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang's calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms. [ABSTRACT FROM AUTHOR]

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time. [ABSTRACT FROM AUTHOR]

In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm's tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system's logistics during critical situations. [ABSTRACT FROM AUTHOR]

Background/Objective: Nutritionists play a crucial role in guiding individuals toward healthier lifestyles through personalized meal planning; however, this task involves navigating a complex web of factors, including health conditions, dietary restrictions, cultural preferences, and socioeconomic constraints. The Analytic Hierarchy Process (AHP) offers a valuable framework for structuring these multi-faceted decisions but inconsistencies can hinder its effectiveness in pairwise comparisons. Methods: This paper proposes a novel hybrid Particle Swarm Optimization–Simulated Annealing (PSO-SA) algorithm to refine inconsistent AHP weight matrices, ensuring a consistent and accurate representation of the nutritionist's expertise and client preferences. Our approach merges PSO's global search capabilities with SA's local search precision, striking an optimal balance between exploration and exploitation. Results: We demonstrate the practical utility of our algorithm through real-world use cases involving personalized meal planning for individuals with specific dietary needs and preferences. Results showcase the algorithm's efficiency in achieving consistency and surpassing standard PSO accuracy. Conclusion: By integrating the PSO-SA algorithm into a mobile app, we empower nutritionists with an advanced decision-making tool for creating tailored meal plans that promote healthier dietary choices and improved client outcomes. This research represents a significant advancement in multi-criteria decision-making for nutrition, offering a robust solution to the inconsistency challenge in AHP and paving the way for more effective and personalized dietary interventions. [ABSTRACT FROM AUTHOR]

One of the most striking examples of the close connections between global optimization processes and statistical physics is the simulated annealing method, inspired by the famous Monte Carlo algorithm devised by Metropolis et al. in the middle of last century. In this paper, we show how the tools of linear kinetic theory allow the description of this gradient-free algorithm from the perspective of statistical physics and how convergence to the global minimum can be related to classical entropy inequalities. This analysis highlights the strong link between linear Boltzmann equations and stochastic optimization methods governed by Markov processes. Thanks to this formalism, we can establish the connections between the simulated annealing process and the corresponding mean-field Langevin dynamics characterized by a stochastic gradient descent approach. Generalizations to other selection strategies in simulated annealing that avoid the acceptance–rejection dynamic are also provided. [ABSTRACT FROM AUTHOR]