A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

The relationships of preventive behaviors and psychological resilience with depression, anxiety, and stress among university students during the COVID-19 pandemic: A two-wave longitudinal study in Shandong Province, China

IntroductionStudies have shown that the psychological impact of the COVID-19 pandemic may lead to long-term health problems; therefore, more attention should be paid to the mental health of university students. This study aimed to explore the longitudinal effects of preventive behaviors and psychological resilience on the mental health of Chinese college students during COVID-19.MethodsWe recruited 2,948 university students from five universities in Shandong Province. We used a generalized estimating equation (GEE) model to estimate the impact of preventive behaviors and psychological resilience on mental health.ResultsIn the follow-up survey, the prevalence of anxiety (44.8% at T1 vs 41.2% at T2) and stress (23.0% at T1 vs 19.6% at T2) decreased over time, whereas the prevalence of depression (35.2% at T1 vs 36.9% at T2) increased significantly (P < 0.001). Senior students were more likely to report depression (OR = 1.710, P < 0.001), anxiety (OR = 0.815, P = 0.019), and stress (OR = 1.385, P = 0.011). Among all majors, medical students were most likely to report depression (OR = 1.373, P = 0.021), anxiety (OR = 1.310, P = 0.040), and stress (OR = 1.775, P < 0.001). Students who wore a mask outside were less likely to report depression (OR = 0.761, P = 0.027) and anxiety (OR = 0.686, P = 0.002) compared to those who did not wear masks. Students who complied with the standard hand-washing technique were less likely to report depression (OR = 0.628, P < 0.001), anxiety (OR = 0.701, P < 0.001), and stress (OR = 0.638, P < 0.001). Students who maintained a distance of one meter in queues were less likely to report depression (OR = 0.668, P < 0.001), anxiety (OR = 0.634, P < 0.001), and stress (OR = 0.638, P < 0.001). Psychological resilience was a protective factor against depression (OR = 0.973, P < 0.001), anxiety (OR = 0.980, P < 0.001), and stress (OR = 0.976, P < 0.001).DiscussionThe prevalence of depression among university students increased at follow-up, while the prevalence of anxiety and stress decreased. Senior students and medical students are vulnerable groups. University students should continue to follow relevant preventive behaviors to protect their mental health. Improving psychological resilience may help maintain and promote university students' mental health

Detecting influenza and emerging avian influenza virus by influenza and pneumonia surveillance systems in a large city in China, 2005 to 2016.

BACKGROUND(#br)Detecting avian influenza virus has become an important public health strategy for controlling the emerging infectious disease.(#br)METHODS(#br)The HIS (hospital information system) modified influenza surveillance system (ISS) and a newly built pneumonia surveillance system (PSS) were used to monitor the influenza viruses in Changsha City, China. The ISS was used to monitor outpatients in two sentinel hospitals and to detect mild influenza and avian influenza cases, and PSS was used to monitor inpatients in 49 hospitals and to detect severe and death influenza cases.(#br)RESULTS(#br)From 2005 to 2016, there were 3,551,917 outpatients monitored by the ISS system, among whom 126,076 were influenza-like illness (ILI) cases, with the ILI proportion (ILI%) of 3.55%. After the HIS was used, the reported incident cases of ILI and ILI% were increased significantly. From March, 2009 to September, 2016, there were 5,491,560 inpatient cases monitored by the PSS system, among which 362,743 were pneumonia cases, with a proportion of 6.61%. Among pneumonia cases, about 10.55% (38,260/362,743) of cases were severe or death cases. The pneumonia incidence increased each year in the city. Among 15 avian influenza cases reported from January, 2005 to September, 2016, there were 26.7% (4/15) mild cases detected by the HIS-modified ISS system, while 60.0% (9/15) were severe or death cases detected by the PSS system. Two H5N1 severe cases were missed by the ISS system in January, 2009 when the PSS system was not available.(#br)CONCLUSIONS(#br)The HIS was able to improve the efficiency of the ISS for monitoring ILI and emerging avian influenza virus. However, the efficiency of the system needs to be verified in a wider area for a longer time span in China

Approach of Solving Dual Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problem Based on MOEA/D

With considering the scheduling objectives such as makespan, machine workload and product cost, a dual resource constrained flexible job shop scheduling problem is described. To solve this problem, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) was proposed to simplify the solving process, and an improved differential evolution algorithm was introduced for evolving operation. A special encoding scheme was designed for the problem characteristics, the initial population was generated by the combination of random generation and strategy selection, and an improved crossover operator was applied to achieve differential evolution operations. At last, actual test instances of flexible job shop scheduling problem were tested to verify the efficiency of the proposed algorithm, and the results show that it is very effective.</p

Approach of Solving Dual Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problem Based on MOEA/D

With considering the scheduling objectives such as makespan, machine workload and product cost, a dual resource constrained flexible job shop scheduling problem is described. To solve this problem, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) was proposed to simplify the solving process, and an improved differential evolution algorithm was introduced for evolving operation. A special encoding scheme was designed for the problem characteristics, the initial population was generated by the combination of random generation and strategy selection, and an improved crossover operator was applied to achieve differential evolution operations. At last, actual test instances of flexible job shop scheduling problem were tested to verify the efficiency of the proposed algorithm, and the results show that it is very effective

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis method based on product gene theory and knowledge base was developed. First, a product gene model and a sectional encoding method for the quality control of the manufacturing process of large equipment were proposed. In that model, the processing surface was the minimum information granularity to meet the production characteristics of large equipment and to improve the flexibility of the product gene model. Then, a similarity evaluation rule and an optimization method of the weights of elements based on particle swarm optimization (PSO) were addressed to filter the available knowledge of product gene from the product gene knowledge base. Aiming at the characteristic of many-to-many between quality defects and quality influence factors in some cases, a fuzzy comprehensive evaluation (FCE) method was developed for the further localization of diagnosis knowledge. Finally, an experiment of bearing spacer was applied to illustrate the proposed quality diagnosis approach. In the experiment, the data from the target gene and knowledge genes were described reasonably. On this basis, available knowledge genes could be accurately filtered with the proposed similarity rule and the method of filtration, where the PSO was proved to be effective. The diagnosis results of the experiment show that multiple factors lead to the defects that were verified. Therefore, the proposed quality defects diagnosis method is an effective way for quality control

A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset

Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process. However, it is a great challenge to achieve a highly accurate rolling bearing fault diagnosis because of the severe imbalance and distribution differences in fault data due to weak early fault features and interference from environmental noise. An intelligent fault diagnosis strategy for rolling bearings based on grayscale image transformation, a generative adversative network, and a convolutional neural network was proposed to solve this problem. First, the original vibration signal is converted into a grayscale image. Then more training samples are generated using GANs to solve severe imbalance and distribution differences in fault data. Finally, the rolling bearing condition detection and fault identification are carried out by using SECNN. The availability of the method is substantiated by experiments on datasets with different data imbalance ratios. In addition, the superiority of this diagnosis strategy is verified by comparing it with other mainstream intelligent diagnosis techniques. The experimental result demonstrates that this strategy can reach more than 99.6% recognition accuracy even under substantial environmental noise interference or changing working conditions and has good stability in the presence of a severe imbalance in fault data

Variations in the Characteristics of Water and Sediment in Response to Extreme Weather Conditions in the Qinjiang River of the Beibu Gulf

Small and medium-sized mountain rivers have characteristics of short processes, rapid flow rates, and quick responses to extreme weather, thereby playing an important role in the global marine geochemical cycle. Understanding the changes in water and sediment characteristics of small and medium-sized rivers in response to extreme weather can provide scientific support for disaster prevention and mitigation and channel management of river basins. This study analyzed data from the past 60 years to understand the variations in the characteristics of water and sediment in the Qinjiang River of the Beibu Gulf under extreme weather conditions by estimating their variation coefficients and contribution rates. The results of the analyses show the following: (1) The Qinjiang River has an average discharge of 32 and 290 m3/s in normal and tropical cyclone weather conditions, respectively. During a tropical cyclone year, the average flow rate is 9.06 times that during normal weather. The average sediment transport rates during normal weather and tropical cyclones are 0.05 × 104 and 1.15 × 104 t, respectively, wherein the latter is 23 times higher. (2) The average flow rates during normal weather and flood periods are 375 and 2,725 m3/s, respectively; the latter is 7.27 times higher. The average sediment transport rates in normal weather and flood period are 0.07 × 104 and 1.14 × 104 t, respectively, wherein the latter is 16.28 times higher. (3) The average annual contribution rates of tropical cyclones and floods to the runoff and sediment discharge of the Qinjiang River are 10.75% and 20.95% and 16.75% and 30.07%, respectively. Extreme weather contributes significantly to the variations in the water and sediment characteristics of the Qinjiang River. During extreme weather, the Qinjiang River exhibits a surge of water and sediment with inter-annual variations. The peaks of runoff and sediment transport during the transit of tropical cyclones tend to decrease gradually over the years, probably because of the reduction in the impact of their activities. The Qinjiang River is located in the South China Sea. The under construction western land-sea new passage grand project, namely the Pinglu Canal, will provide access to the Beibu Gulf along the Qinjiang River. The results of this study will provide scientific support for the disaster prevention and mitigation of the Pinglu Canal and the construction of the port