Optimal Cycle Research Articles

New uricase producing Stenotrophomonas isolate recovered from a hot well in western south Egypt: Strain identification, Statistical optimization of enzyme production and In vitro application in gout treatment

Uricase, an enzyme catalyzing the breakdown of uric acid into more soluble compounds, holds significant therapeutic potential in managing hyperuricemia and associated conditions like gout. The current study intended to bioprospect new uricase producers in one of rarely studied ecological niches (ground water well that delivers water from 1500 m depth with temperature 50 °C in Al Qasr Oasis, New Valley Governorate, Egypt). Eight isolates were screened for uricase production, and the most potent was identified using phenotypic and genotypic methods as Stenotrophomonas maltophilia with 99% similarity. This is the first record about the ability of a member in the genus Stenotrophomonas to produce uricase, and was thus targeted in subsequent optimization studies. The initial activity of 7.20 U/mL was obtained in medium No. 2 after testing enzyme production in five different media. The uricase production was then enhanced using statistical design and modeling. Plackett-Burman design (PBD) was used to screen and identify the parameters influencing uricase activity. Four of the nine investigated factors were the most significant and were further optimized using Box-Behnken design (BBD). After two optimization cycles using PBD and BBD, the yield was improved to 20.60 and 25.67 U/mL, respectively, indicating a significant 3.5-fold improvement in the enzyme yield. The uricase was partially purified by ammonium sulphate 75% with 2.58-fold increase in activity (final recovered activity of 87.5%). Under polarized light microscopy, the prepared monosodium urate crystals clumps degraded at different levels of the obtained enzyme which encourages its future application in gout treatment.

Online Bayesian Optimization of Polynomial-Multigrid Cycles for Flux Reconstruction

In this study, we present a novel strategy for dynamically optimizing polynomial multigrid cycles to accelerate convergence within the dual-time-stepping formulation of the artificial compressibility method. To accomplish this, a Gaussian process model is developed using Bayesian optimization to efficiently sample possible cycles to minimize run-time. To allow the use of conventional optimization methods, we developed fractional smoothing steps, moving the optimization from a discrete space to a continuous space. Initially, a static, offline, approach was developed, and optimal cycles were found for two flow past cylinder test cases with Re=200 and Re=500; however, when exchanging optimal cycles between the different test cases, there was significant degradation in speedup. Toward this, a dynamic, online, approach was developed where cycles are optimized during a simulation. The performance of the resulting optimal cycles gave a similar speedup to the offline approach while achieving a net reduction in run-time. Again testing the optimization strategy on the flow past a cylinder, this yielded candidates with mean speedups of ∼3.0× and ∼2.1×, respectively. Finally, testing online optimization on a turbulent flow past a cylinder at Re=3900 resulted in an overall speedup of ∼1.9×.

Optimizing the chemical vapor deposition process of 4H–SiC epitaxial layer growth with machine-learning-assisted multiphysics simulations

This work addresses a novel technique for selecting the best process parameters for the 4H–SiC epitaxial layer in a horizontal hot-wall chemical vapor reactor using a transient multi-physical (thermal-fluid-chemical) simulation model and combined with a machine-learning model. An experiment was performed to validate the feasibility of the numerical model. Secondly, a single-factor analysis was conducted to investigate the effects of process parameters, including the deposition temperature, inlet-flow volume, rotational speed of the susceptor, and cavity pressure, on the quality of the 4H–SiC epitaxial layer. Finally, a machine learning algorithm, the ant colony optimization-back propagation neural network (ACO–BPNN), was employed to develop the input/output model and optimize process parameters for obtaining a high-quality epitaxial layer and reducing the optimization cycle and costs. Notably, the optimized process was validated by real experiments, where the error between calculation and experiment is 4.03 % for deposition rate and 0.49 % for coefficient of variation, respectively. The results highlight the model as reliable and lay the foundation for the CVD growth of the 4H–SiC epitaxial layer.

Improving the energy efficiency of industrial radio frequency heat treatment by optimizing electrode sizes and reversal cycles

Radio Frequency (RF) heating technology exhibits potential advantages in the heat treatment of bulky products. The influence of electrode size on the RF heating performance by the electric field distribution, electromagnetic loss density, and temperature distribution was evaluated. An electrode reversal device was developed, facilitating the cyclic reversal of the positive and negative electrodes and the effect of electrode reversal was also investigated. Simulation results showed an optimal heating uniformity when the ratio of electrode to product width is 1.4. At that time, the influence of stray electric field between the positive electrode and the cavity on the electromagnetic energy was minimal. The variation in loss angle tangent caused by temperature change was the primary reason for the fluctuation of the electromagnetic-thermal energy conversion efficiency within the same radio-frequency field. The electrode reversal dramatically improved the heating uniformity in the direction of electromagnetic energy decay. The temperature uniformity index (TUI) can be reduced by 32.79 % to 75.41 % with the shorter reversal cycle, but the service life of the device was also curtailed due to more mechanical motions. Considering the stability of the equipment and the heating performance, the optimum reversal cycle was chosen to be 420 s. In this case, the maximum TUI didn’t exceed 0.017, demonstrating better heating uniformity as compared with the commonly used for improving heating uniformity. In conclusion, a suitable electrode width and reversal cycle can effectively avoid the thermal offset effect of larger agricultural products during RF heating, thus improving the heating performance.

Optimal treatment conditions for low-intensity pulsed ultrasound therapy for Alzheimer’s disease: applications from mice to humans

PurposeWe previously developed a novel therapy with low-intensity pulsed ultrasound (LIPUS) that ameliorates cognitive decline through upregulation of endothelial nitric oxide synthase (eNOS) in mouse models of Alzheimer’s disease (AD). In a randomized, double-blind, placebo-controlled pilot trial, we demonstrated that whole-brain LIPUS therapy is safe and tends to suppress the cognitive decline in early AD patients. We herein report the findings of our basic experiments that we performed for the pilot trial in order to apply whole-brain LIPUS therapy to humans, as well.MethodsFirst, we examined the relationship between bone density/thickness and ultrasound transmittance using human temporal bone. Next, based on the results of ultrasound transmittance, we further examined mRNA expression of VEGF, FGF2, and eNOS in response to variable ultrasound frequencies, duty cycles, and sound pressures.ResultsThere was a significant correlation between bone thickness and transmittance (1.0 MHz, P < 0.001), while there was no significant correlation between bone density and transmittance (1.0 MHz, P = 0.421). At a frequency of 0.5 MHz, the optimum duty cycle was considered to be up to 20%. When the tissue amplitude was in the range of 0.05–0.5 MPa, VEGF, FGF2, and eNOS were significantly upregulated by LIPUS. Thus, the conditions necessary for LIPUS therapy for the human brain were identified as sound pressure just below the probe 1.3 MPa (tissue amplitude 0.15 MPa), duty cycle 5%, and frequency 0.5 MHz.ConclusionWe successfully identified the optimal treatment conditions for LIPUS therapy for patients with AD.

Multi-fidelity deep learning for aerodynamic shape optimization using convolutional neural network

Aerodynamic shape design is essential for improving aircraft performance and efficiency. First, this study introduces a data-driven optimization framework utilizing a multi-fidelity convolutional neural network (MFCNN) for aerodynamic shape optimization. To achieve better optimization results with reduced computational cost, the framework dynamically incorporates new data in each optimization cycle. Specifically, it constantly involves the optimal solution from previous cycle as a new high-fidelity sample and employs a low-fidelity infilling strategy that maximizes the minimum Euclidean distance for selecting new low-fidelity samples. Moreover, a standard synthetic benchmark is used to elaborate the procedure of optimization and show the capability and effectiveness of the framework. Finally, the framework is applied to two aerodynamic shape optimization problems: maximizing the lift-to-drag ratio for the Royal Aircraft Establishment 2822 (RAE2822) airfoils and minimizing the cruise drag coefficient for the three-dimensional (3D) drooped and scarfed non-axisymmetric nacelles. The framework increases the lift-to-drag ratio by 51.21% over the baseline and achieves an 18.79% reduction in the cruise drag coefficient for nacelle optimization, outperforming traditional multi-fidelity deep neural network optimization framework. Sufficiently utilizing the implicit relations between different fidelity levels of data through defined local perceptual fields and convolution, our MFCNN-based optimization framework signifies a step forward in the efficiency and accuracy of aerodynamic shape optimization.

Hull form optimization research based on multi‐precision back‐propagation neural network approximation model

AbstractIn order to shorten the optimization cycle of ship design optimization and solve the time‐consuming problem of computational fluid dynamics (CFD) numerical calculation, this paper proposes a multi‐precision back‐propagation neural network (MP‐BP) approximation technology. Fewer high‐precision ship samples and more low‐precision ship samples were used to construct an approximate model, back‐propagation (BP) neural network was used to train multi‐precision samples. So that the approximate model is as close as possible to the real model, and achieving the effect of high‐precision approximation model. Subsequently, numerical verification and typical hull form verification are given. Based on CFD and Rankine theory, the multi‐objective design optimization framework for ship comprehensive navigation performance is constructed. The multi‐objective approximation model of KCS ship is constructed by MP‐BP approximation technology, and optimized by particle swarm optimization (PSO) algorithm. The results show that the multi‐objective optimization design framework using the MP‐BP approximation model can capture the global optimal solution and improve the efficiency of the entire hull form design optimization. It can provide a certain degree of technical support for green ship and low‐carbon shipping.

Design and implementation of duty cycle-based futuristic clustering technique in WSN

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.

Solar adsorption cooling system operating by activated–carbon–ethanol bed

One efficient way to convert small thermally energized into effective cooling is through adsorption cooling technology, which increases energy efficiency and reduces environmental pollution. This study's primary goal is to hypothetically examine the thermal coefficient of performing the solar adsorptive refrigerator machine operated with an activating carbon/Ethanol operating dual. The impact of different operating situations and design factors on the machine's performance is inspected and evaluated. The present double-bed solar energy adsorptive-cooler unit is modeled by thermodynamic methodology. Then, it was analyzed to evaluate its effectiveness work under Baghdad climate conditions. For the current study, the two-bed solar adsorption cooling unit with 0.5 kW capacity input heat 11893 that operates at 5 °C for the evaporator and 45 °C for the condenser is presented. The Engineering-Equation-Solver (EES) simulation program was created and used to solve the modeling equations that predict the optimal cycle performance and evaluate the optimum reasonable values of the operation parameters of the proposed system. The pressure range for the refrigeration cycle is 2.408 kPa for the evaporation state and 23.14 kPa for the condensation state. The findings demonstrate that an optimum coefficient of performance (COP) is 0.702 at 95 °C, a 20% performance increase, which generates 39.4 of cooling water. It produced 1 kg of chilled water for 2.463 kg of activated carbon at a temperature of 5°C. The improved solar-powered adsorption systems and refrigeration technologies are appealing substitutes that can satisfy energy demands in addition to meeting needs for cooling, ice production, air conditioning, and refrigeration preservation and safeguarding of the environment with Iraq's climate conditions.

Performance Evaluation and Cycle Time Optimization of Vapor-Compression/Adsorption Cascade Refrigeration Systems

The reliance on more sustainable refrigeration systems with less electricity consumption attracts a lot of attention as the demand for refrigeration increases due to population growth and global warming threats. This study examines the use of a cascade vapor-compression/adsorption refrigeration system in hot weather, focusing on condensing temperatures of 50, 55, and 60 °C, whereas an air-cooled condenser is in use due to practical considerations. A fully coupled transient model is developed using COMSOL Multiphysics to simulate the integrated system, considering the practical limitations of the vapor compression system (VCS) and the dynamic nature of the adsorption system (ADS). The model combines a lumped model for the ADS with the manufacturer’s data for a VCS compressor at different condensing and evaporating temperatures. It was found that the VCS is more sensitive to the change in the ADS’s condensing temperature, since when the temperature is raised from 50 °C to 60 °C, the VCS’s COP decreases by 29.5%, while the ADS’s COP decreases by 7.55%. Furthermore, the cycle time of ADS plays an important role in providing the cooling requirements for the bottoming cycle (VCS), and it can be optimized to maximize the energy conversion efficiency of the VCS. At optimum cycle time and compared to the conventional VCS, the cascade system can boost the cooling capacity of the VCS by 18.2%, lower the compressor power by 63.2%, and greatly enhance the COP by 221%. These results indicate that the application of the cascade VCS/ADS in such severe conditions is a more sustainable and energy-efficient solution to meet the growing need for refrigeration.

Integrated Hybrid Engine Cycle Design and Power Management Optimization

Abstract A novel integrated gas turbine cycle design and power management optimization methodology for parallel hybrid electric propulsion architectures is presented in this paper. The gas turbine multipoint cycle design method is extended to turboprop and turbofan architectures, and several trade studies are performed initially at the cycle level. It is shown that the maximum degree of electrification is limited by the surge margin levels of the booster in the turbofan configuration. An aircraft mission-level assessment is then performed using the integrated optimization method initially for an A320 Neo style aircraft case. The results indicate that the optimal cycle redesigned hybrid electric propulsion system (HEPS) favors takeoff and climb power on-takes while optimal retrofit HEPS favor cruise power on-takes. It is shown that for current battery energy density (250 Wh/Kg), there is no fuel burn benefit. Furthermore, even for optimistic energy density values (750 Wh/kg) the maximum fuel burn benefit for a 500 nm mission is 5.5% and 4% for redesigned and retrofit HEPS, respectively. The power management strategies for HEPS configurations also differ based on gas turbine technology with improvement in gas turbine technology showing greater scope for electrification. The method is then extended to ATR 72 style aircraft case, showing greater fuel burn benefits across the flight mission envelope. The power management strategies also change depending on the objective function, and optimum strategies are reported for direct operating cost or fuel burn. The retrofit case studies show a benefit in direct operating cost compared to redesigned case studies for ATR 72. Finally, a novel multimission approach is shown to highlight the overall fuel burn and direct operating cost benefit across the aircraft mission cluster.

A mathematical model for the integrated problem of production network design and inventory positioning

In this paper, an integrated approach is addressed to tackle the production network design and inventory positioning problem in a single-sourcing, multi-product, and multi-period context. The objective of our model is to simultaneously determine the optimal network structure, safety stock amounts, and their respective locations, considering normal demand conditions. The main goal is to minimize the overall production network cost. The proposed model merges the traditional network design formulation with that for inventory positioning using the concept of guaranteed service. The developed model is implemented and tested on a case study of office furniture manufacturing, where the bill of materials is considered. The effectiveness of the proposed model is demonstrated by comparing it to a sequential approach where the network structure and safety stock decisions are made sequentially. The results reveal that the integrated approach surpasses the sequential approach, with cost savings ranging from 1.7% to 3.7%. In addition, a higher optimal cycle service level is obtained by the integrated approach compared to that of the sequential approach. To further evaluate the model's performance, a sensitivity analysis is conducted to examine the influence of the model's parameters, i.e., committed service time and coefficient of variation of demand, on its solutions. The analysis shows that when the coefficient of the demand variation remains unchanged, longer committed service times lead to lower safety stock costs and higher optimal service levels. Moreover, it is observed that more safety stocks of components are kept in the upstream layer of the network than those of finished goods.

Three-dimensional fluid-thermal–mechanical coupling numerical modeling of elastocaloric cooler for electronic chip

The rapid progress in electronic component integration poses formidable challenges to electronic circuit thermal design. The elastocaloric cooling system is a promising solution to address the escalating need for efficient small-scale cooling and temperature control. This study aims to develop an electronic chip cooler based on the elastocaloric effect of shape memory alloys. To more accurately capture the spatial temperature distribution and the intricate fluid flow mixing process of the cooling system, we developed a novel three-dimensional elastocaloric cooling numerical model, also addressing the limitations of existing numerical models in accuracy and applicability. Three cases were designed and evaluated, varying in flow rate, cycle frequency, and strain rate. We propose a criterion for determining the optimal cycle frequency under fixed flow and strain rate: during system stabilization, the frequency that minimizes the disparity between the cooling start line and the cooling end line. The results reveal that the optimal cycle frequency is 1/2.6 Hz at a flow rate of 0.5 L/min and a strain rate of 0.45 s−1. Compared to conventional approaches, our custom-designed elastocaloric cooler results in an additional reduction of 16 K in the chip's maximum temperature. This system achieves a specific cooling power of 6.69 W/g and a coefficient of performance of 1.55.

Machine learning-based multi-objective optimization and thermal assessment of supercritical CO2 Rankine cycles for gas turbine waste heat recovery

Technologies for utilizing waste heat for power generation have attracted significant attention in recent years due to their potential to enhance energy efficiency and reduce greenhouse gas emissions. This research focuses on the comparative and optimization analysis of three supercritical carbon dioxide (sCO2) Rankine cycles (simple, cascade, and split) for gas turbine waste heat recuperation. The study begins with parametric analysis, investigating the significant effects of key variables, including turbine inlet temperature, condenser inlet temperature, and pinch point temperature, on the thermal performance of advanced sCO2 power cycles. To identify the most efficient cycle configuration, a multi-objective optimization approach is employed. This approach combines a Genetic Algorithm with machine learning regression models (Random Forest, XGBoost, Artificial Neural Network, Ridge Regression, and K-Nearest Neighbors) to predict cycle performance using a dataset extracted from cycle simulations. The decision-making process for determining the optimal cycle configuration is facilitated by the TOPSIS (technique for order of preference by similarity to the ideal solution) method. The study's major findings reveal that the split cycle outperforms the simple and cascade configurations in terms of power generation across various operating conditions. The optimized split cycle not only demonstrates superior power output but also exhibits enhanced net power output, heat recovery, system and exergy efficiency of 7.99 MW, 76.17 %, 26.86 % and 57.96 %, respectively, making it a promising choice for waste heat recovery applications. This research has the potential to contribute to the advancement and widespread adoption of waste heat recovery in energy technologies boosting system efficiency and economic feasibility. It provides a new perspective for future research, contributing to the improvement of energy generation infrastructure.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Laser powder bed fusion of pure Nb and Nb + WC powder mixture

Additive Manufacturing (AM) of high temperature refractory metals, including Nb-based alloys, can overcome the challenges associated with their processing using the conventional methods. In this study, pure Nb and a blend of Nb with 2.5 wt% WC was processed with Laser Powder Bed Fusion (LPBF) starting with a full optimisation cycle that explored the impact of the laser power, scan speed, hatch spacing, and layer thickness on the build integrity. Results show that both materials have good processability, as confirmed by the lack of cracking and other defects once the parameters are optimised. Nevertheless, the process parameters affected the materials in different ways. Increasing both the scan speed and laser power resulted in hardness increase in pure Nb. However, this was not seen in the case of the Nb + 2.5 wt% WC powder mixture. In fact, the hardness was on average ∼ 15% lower in the Nb + WC builds compared with pure Nb builds. This observation was elucidated through studying the effect of the process on the oxygen pick-up in pure Nb and the dissociation of the carbide in the Nb + WC mixture. Although the hardening effect is expected due to the full solubility of W in Nb or due to the formation of carbides, results indicate that oxygen pick-up has a greater impact on hardness.

The Cut-Cell Method for the Conjugate Heat Transfer Topology Optimization of Turbulent Flows Using the “Think Discrete–Do Continuous” Adjoint

This paper presents a topology optimization (TopO) method for conjugate heat transfer (CHT), with turbulent flows. Topological changes are controlled by an artificial material distribution field (design variables), defined at the cells of a background grid and used to distinguish a fluid from a solid material. To effectively solve the CHT problem, it is crucial to impose exact boundary conditions at the computed fluid–solid interface (FSI); this is the purpose of introducing the cut-cell method. On the grid, including also cut cells, the incompressible Navier–Stokes equations, coupled with the Spalart–Allmaras turbulence model with wall functions, and the temperature equation are solved. The continuous adjoint method computes the derivatives of the objective function(s) and constraints with respect to the material distribution field, starting from the computation of derivatives with respect to the positions of nodes on the FSI and then applying the chain rule of differentiation. In this work, the continuous adjoint PDEs are discretized using schemes that are consistent with the primal discretization, and this will be referred to as the “Think Discrete–Do Continuous” (TDDC) adjoint. The accuracy of the gradient computed by the TDDC adjoint is verified and the proposed method is assessed in the optimization of two 2D cases, both in turbulent flow conditions. The performance of the TopO designs is investigated in terms of the number of required refinement steps per optimization cycle, the Reynolds number of the flow, and the maximum allowed power dissipation. To illustrate the benefits of the proposed method, the first case is also optimized using a density-based TopO that imposes Brinkman penalization terms in solid areas, and comparisons are made.

DISCRETE OPTIMIZATION OF THE IMPLEMENTATION STAGES OF PHARMACEUTICAL DEVELOPMENT FOR A SPRAY TREATMENT FOR ORAL DISEASES

Introduction. All over the world more than 3.5 billion people suffer from oral diseases, which can lead to endogenous infectious diseases and create conditions for external infections. The growing antimicrobial resistance of bacterial strains is a global health threat. Oligoalkyleneguanidine polymers may be promising compounds to solve this problem. The spray form for the application of these substances is the most optimal. The aim of the study is to apply discrete optimization to implement the stages of pharmaceutical development of a spray based on branched oli-gohexamethyleneguanidine for the treatment of oral diseases. Material and methods. Experiments are carried out using various equipment and samples with different compositions have been developed. The implementation of the pharmaceutical development stages was carried out using a discrete optimization algorithm. Results. When implementing discrete optimization in pharmaceutical development, it is necessary to prioritize criteria and limitations using the target quality profile of the drug being developed. As a result of the optimization cycles carried out, the optimal composition was selected, which corresponds to the target quality profile, including such parameters as pH, dynamic viscosity, sterilizing filtration, adhesion of the formulations to the oral mucosa and the spray torch. A discrete optimization was carried out, taking into account the wetting edge angle and particle size distribution. The optimal com-position was sample No. 8, which has pseudoplastic properties, provides unhindered spraying and prevents the composition from draining from the mucous membrane of the oral cavity. Conclusion. During the study, the optimal ratio of components was determined and the development of a spray based on oligohexamethyleneguani-dine with the implementation of stages of pharmaceutical development for discrete optimization was proposed.

A multi-period vaccines supply chain network design with capacity expansion and different replenishment cycles under uncertain demand

The World Health Organization (WHO) proposes guidelines for vaccine distribution networks that incorporate economic order quantity and risk pooling principles, specifically through implementing different replenishment cycles between different levels of the distribution network. The substantial impact of different replenishment cycles on inventory levels is demonstrated in the existing literature. However, current optimization network design research has paid limited attention to this aspect. This article aims to address this research gap by investigating a two-stage stochastic programming for a multi-period perishable vaccine network design problem, taking into account the different replenishment cycles characteristic and capacity expansion under uncertain demand conditions. Given the inherent difficulty of solving the proposed problem, a matheuristic approach based on Variable Neighborhood Search (VNS) is developed. To illustrate the practical application and analyze the impact of uncertain demand, we apply the proposed model and its solution method using the Indonesia dataset in the COVID-19 situation. The computational results indicate that lower replenishment cycles leads to higher inventory levels, total costs, and capacity requirements for the top-level distribution center. Optimum replenishment cycles and further managerial implications and future potential research are also presented.

Multiple-failure mode division and condition-based maintenance decision making for systems with multi-indicator performance degradation

In prognostics and health management research, it is typically assumed that the health status of a system can be reflected by a single performance indicator. However, modern industrial systems are complex and often require multiple indicators to represent different aspects of system health. Furthermore, these indicators may be associated with multiple fault types, leading to multiple failure modes. This study focuses on systems with multi-indicator performance degradation. We construct a division model of multiple failure modes, and investigate the optimal condition-based maintenance decision under multiple failure modes. By constructing division models for systems with two-indicator performance degradation and systems with three-indicator performance degradation, a common multiple-failure mode division model for systems with multi-indicator performance degradation is to distinguish different fault types that may occur in such systems. Taking the systems with two-indicator performance degradation as an example, a condition-based maintenance strategy is developed that considers the differences in maintenance types and effectiveness corresponding to different faults types. A joint probability recursive model under the influence of strategies is derived, and a cost-rate model in a finite time horizon is established to determine the optimal inspection cycle and maintenance threshold for each indicator. Taking the steel rolling system as an application case, the correctness and effectiveness of the proposed strategy and model are verified. The results indicate that the division model can represent the complex relationships among multiple indicators and fault types and that the strategy considering multiple maintenance types and effects can reduce the cost rate of the system.