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

Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis

Rolling bearings are omnipresent parts in industrial fields. To comprehensively reflect the status of rolling bearing and improve the classification accuracy, fusion information is widely used in various studies, which may result in high dimensionality, redundancy information of dataset, and time consumption. Thus, it is of crucial significance in extracting optimal features from high-dimensional and redundant feature space for classification. In this study, a fault diagnosis of rolling bearings model based on sparse principal subspace discriminant analysis is proposed. It extracts sparse discrimination information, meanwhile preserving the main energy of original dataset, and the sparse regularization term and sparse error term constrained by l2,1-norm are introduced to improve the performance of feature extraction and the robustness to noise and outliers. The multi-domain feature space involved a time domain, frequency domain, and time-frequency domain is first derived from the original vibration signals. Then, the intrinsic geometric features extracted by sparse principal subspace discriminant analysis are fed into a support vector machine classifier to recognize different operating conditions of bearings. The experimental results demonstrated that the feasibility and effectiveness of the proposed fault diagnosis model based on a sparse principal subspace discriminant analysis algorithm can achieve higher recognition accuracy than fisher discriminant analysis and its extensions, and it is relatively insensitive to the impact of noise and outliers owing to the sparse property