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Process planning and scheduling are two of the most important functions of a manufacturing system. Traditionally, these two functions are executed separately. Since they are interrelated, conducting process planning and scheduling simultaneously will provide more advantages. In this paper, according to the characteristics of the integrated process planning and scheduling (IPPS) problem, a hybrid honey bee mating optimization (HBMO) algorithm, which combines the HBMO algorithm and variable neighborhood search (VNS), is proposed to settle the problem with makespan criterion. Different with conventional HBMO, we utilize VNS with two effective and efficient neighborhood structures in the algorithm to simulate the workers' brood caring action to avoid premature convergence and to find more excellent broods. In addition, a novel individual initialization method is developed in the algorithm. The proposed algorithm is tested on typical benchmark instances taken from related literature, and the computational results are compared with those of other algorithms. Experimental results show the effectiveness and efficiency of the hybrid HBMO algorithm. New upper bounds have been captured for 16 instances, and most instances have been improved within reasonable CPU times. [ABSTRACT FROM AUTHOR]

In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching–learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method. [ABSTRACT FROM AUTHOR]

This article proposes a novel artificial ecological niche optimization algorithm for car sequencing problem considering energy consumption to satisfy the requirements of the paint shop and the assembly line. Energy consumption, which is an important issue, is often neglected during the production process. In this article, the mathematical model of car sequencing problem with energy consumption is established. A new artificial ecological niche optimization algorithm that imitates natural ecosystem is proposed to solve this problem. The car sequence is considered as an ecosystem. Ecological niche theory, niche overlap and niche differentiation principles are applied in this algorithm, which leads the ecosystem to adapt itself to a more ecological balance condition accompanying less violence appear in the car sequence. Energy consumption in the scheduling process of ring sorting buffer was considered as biogenic migration in the algorithm. The proposed algorithm is verified on a set of instances provided by the French car manufacturer Renault. The computation results indicate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Two-sided assembly lines are widely applied to produce the large-sized high-volume products, such as buses and trucks. Balancing the lines is a vital design problem for the industries, and the problem is NP-hard. Besides the fundamental constraints of the conventional line balancing problem, some specific constraints may occur in the two-sided assembly line problem, including the zoning constraints, the positional constraints, and the synchronism constraints, which make the problem more complex. In this paper, an integer programming (IP) model is constructed and solved for the two-sided assembly line balancing problem which contains the above three constraints. A novel metaheuristic named late acceptance hill-climbing (LAHC) is also proposed to solve the problem effectively. The proposed algorithm is tested on several sets of instances. The computational results of the LAHC algorithm are compared with those of IP and the lower bounds of the instances. The experiment validates the effectiveness of the LAHC algorithm. [ABSTRACT FROM AUTHOR]

In recent years, mixed model assembly lines are gaining popularity to produce a variety of models on the single-model assembly lines. Mixed model assembly lines have two types of problems which include sequencing of different models on the line and balancing of assembly line. These two problems collectively affect the performance of assembly lines, and therefore, current research is aimed to balance the workload of different models on each station, to reduce the deviation of workload of a station from the average workload of all the stations and to minimize the total flow time of models on different stations simultaneously. A multi-objective artificial bee colony (multi-ABC) algorithm for simultaneous sequencing and balancing problem with Pareto concepts and local search mechanism is presented. Two kinds of mixed model assembly line problems are analysed. For the first and second problems, each model task time data and precedence relation data are taken from standard assembly line problems, from operation research library (ORL) and from a truck manufacturing company in China, respectively. Both problems are solved using the proposed multi-ABC algorithm on two different demand scenarios of models, and the results are compared against the results obtained from a famous algorithm in the literature, i.e. non-dominated sorting genetic algorithm (NSGA) II. Computational results of the selected problems indicate that the proposed multi-ABC algorithm outperforms NSGA II and gives better Pareto solutions for the selected problems on different demand scenarios of models. [ABSTRACT FROM AUTHOR]

Permutation flow shop scheduling (PFSP) is among the most studied scheduling settings. In this paper, a hybrid Teaching–Learning-Based Optimization algorithm (HTLBO), which combines a novel teaching–learning-based optimization algorithm for solution evolution and a variable neighborhood search (VNS) for fast solution improvement, is proposed for PFSP to determine the job sequence with minimization of makespan criterion and minimization of maximum lateness criterion, respectively. To convert the individual to the job permutation, a largest order value (LOV) rule is utilized. Furthermore, a simulated annealing (SA) is adopted as the local search method of VNS after the shaking procedure. Experimental comparisons over public PFSP test instances with other competitive algorithms show the effectiveness of the proposed algorithm. For the DMU problems, 19 new upper bounds are obtained for the instances with makespan criterion and 88 new upper bounds are obtained for the instances with maximum lateness criterion. [ABSTRACT FROM AUTHOR]

Large-volume waste products, such as refrigerators and automobiles, not only consume resources but also pollute the environment easily. A two-sided disassembly line is the most effective method to deal with large-volume waste products. How to reduce disassembly costs while increasing profit has emerged as an important and challenging research topic. Existing studies ignore the diversity of waste products as well as uncertain factors such as corrosion and deformation of parts, which is inconsistent with the actual disassembly scenario. In this paper, a partial destructive mode is introduced into the mixed-model two-sided disassembly line balancing problem, and the mathematical model of the problem is established. The model seeks to comprehensively optimize the number of workstations, the smoothness index, and the profit. In order to obtain a high-quality disassembly scheme, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The proposed model and algorithm are then applied to an automobile disassembly line as an engineering illustration. The disassembly scheme analysis demonstrates that the partial destructive mode can raise the profit of a mixed-model two-sided disassembly line. This research has significant application potential in the recycling of large-volume products. [ABSTRACT FROM AUTHOR]

Levosimendan exerts positive inotropic and vasodilatory effects. Currently, its effects on right heart function remain uncertain. This systematic review and meta-analysis is intended to illustrate the impacts of levosimendan on systolic function of the right heart in patients with heart dysfunction. We systematically searched electronic databases (PubMed, the Cochrane Library, Embase and Web of Science) up to November 30, 2020, and filtered eligible studies that reported the impacts of levosimendan on right heart function. Of these, only studies whose patients suffered from heart dysfunction or pulmonary hypertension were included. Additionally, patients were divided into two groups (given levosimendan or not) in the initial research. Then, RevMan5.3 was used to conduct further analysis. A total of 8 studies comprising 390 patients were included. The results showed that after 24 h of levosimendan, patients' right ventricular fractional area change [3.17, 95% CI (2.03, 4.32), P < 0.00001], tricuspid annular plane systolic excursion [1.26, 95% CI (0.35, 2.16), P = 0.007] and tricuspid annular peak systolic velocity [0.86, 95% CI (0.41, 1.32), P = 0.0002] were significantly increased compared to the control group. And there is an increasing trend of cardiac output in levosimendan group [1.06, 95% CI (− 0.16, 2.29), P = 0.09 ].Furthermore, patients' systolic pulmonary arterial pressure [− 5.57, 95% CI (− 7.60, − 3.54), P < 0.00001] and mean pulmonary arterial pressure [− 1.01, 95% CI (− 1.64, − 0.37), P = 0.002] were both significantly decreased, whereas changes in pulmonary vascular resistance [− 55.88, 95% CI (− 206.57, 94.82), P = 0.47] were not significant. Our study shows that in patients with heart dysfunction, levosimendan improves systolic function of the right heart and decreases the pressure of the pulmonary artery. [ABSTRACT FROM AUTHOR]

• A multi-algorithm based features screening and LE features downscaling method is proposed. • For features with high dimensionality, feature filtering and feature fusion are applied to downscale the features step by step. • Fast selection of model hyperparameters based on GS. • Evaluate the prediction model through multiple experiments and multiple metrics. Accurate prediction of tool wear status plays a critical role in the digital manufacturing industry, and its health level directly affects machining quality, production costs, and overall productivity. In response to the problems of the high dimensionality of extracted features from tool wear characterization sensors, redundant information, and large individual model errors and biases, a novel method for tool wear status identification and prediction that fuses downscaling dimensionality and ensemble models is proposed. First, a multi-algorithm feature filtering based on Random Forest (RF) and extreme gradient boosting (XGBoost) is utilized, and the laplacian eigenmaps (LE) algorithm is combined to perform fusion downscaling on the filtered features. Then, the parameters of the XGBoost algorithm are optimized using grid search (GS). Finally, the performance of the proposed method is evaluated by different tool wear experiments for both regression and classification using model prediction accuracy evaluation metrics (R-squared values and f1 values) and prediction time. The experimental results show that for different tools wear experimental data, the R-squared values of the regression model are higher than 0.98, the f1 values of the classification model are above 0.96, and the prediction speed is improved by an order of magnitude compared with other models. The results are analyzed to verify the effectiveness and applicability of the proposed method, which can provide technical support for the automated machining process. [ABSTRACT FROM AUTHOR]

Vaccination has been proven effective against infection with enterovirus A71 (EV-A71) in clinical trials, but vaccine effectiveness in real-world situations remains incompletely understood. Furthermore, it is not clear whether previous vaccination will result in symptom attenuation among post-vaccinated cases. Based on long-term data extracted from the only designed referral hospital for infectious diseases, we used a test-negative case-control design and multivariate logistic regression models to analyze the effectiveness of EV-A71 vaccine against hand, foot and mouth disease (HFMD). And then, generalized linear regression models were used to evaluate the associations between prior vaccination and disease profiles. We selected 4883 inpatients for vaccine efficacy estimations and 2188 inpatients for disease profile comparisons. Vaccine effectiveness against EV-A71-induced HFMD for complete vaccination was 63.4 % and 51.7 % for partial vaccination. The vaccine effectiveness was higher among cases received the first dose within 12 months. No protection was observed against coxsackievirus (CV) A6-, CV-A10- or CV-A16-associated HFMD among children regardless of vaccination status. Completely vaccinated cases had shorter hospital stay and disease course compared to unvaccinated cases (P < 0.05). These findings reiterate the need to continue the development of a multivalent vaccine or combined vaccines, and have implications for introducing optimized vaccination strategies. [ABSTRACT FROM AUTHOR]

Recycling spent lithium-ion batteries is a significant way to achieve life-cycle management and a green circular economy, helping to achieve carbon neutrality. The form of battery pack disassembly has gradually moved away from manual manipulation to a human–robot collaborative process. However, there is no established methodology for assessing spent lithium-ion battery disassembly between the environmental benefits, technical feasibility, and economic viability of human–robot collaborative disassembly (HRCD). To ensure the efficiency and security of HRCD processes, this paper develops a resilience assessment model for HRCD using stability, redundancy, efficiency and adaptation metrics. The resilience assessment theory is used to obtain the internal relations among the resilience indicators of the system. In this work, we evaluate the resilience of HRCD for lithium-ion battery disassembly by integrating fuzzy Bayesian fusion with an analytical network process unfolding cloud. The 2017 Chevrolet Bolt driver demonstrated the feasibility and effectiveness of the proposed approach, which allows greater flexibility in complex tasks performed by humans and robots. In case study, the weights values of stability, redundancy, efficiency and adaptability are 0.340533, 0.161384, 0.203835 and 0.294248, respectively. IR2&HO1 and IR3&HO2 have an assessment value of 93 and 95, respectively, with eigenvalues of 1.28 and 1.23, which are level I. RI3&HO1 and RI4&HO3 belong to level IV, with an evaluation value of 38 and 45, respectively, and a characteristic value of 2.27 and 2.55, respectively. The integrated methodology proposed in this paper could be effectively used by disassembly management, to evaluate the HRCD disassembly scheme resilient strategies for better managing production. [ABSTRACT FROM AUTHOR]

In the real machining environment of CNC milling, there are often multi-pass (rough milling and fine milling combined) and related to each other in the process. Aiming at the existing optimization model of processing parameters which is only limited to single-step machining process, an optimization method of processing parameters of multi-pass CNC milling towards energy and carbon emission efficiency was proposed. Firstly, the process and dynamic characteristics of energy consumption and carbon emission in multi-pass CNC milling are described comprehensively and systematically. Based on this, with the goal of optimizing energy efficiency, carbon emission efficiency and cost efficiency, and with the combination of process parameters under variable process conditions as variables, the optimization model of multi-pass milling process parameters was established. An improved non-dominated sorting gravitational search algorithm was used to optimize the solution, and the analytic hierarchy process (AHP) was used to determine the optimal solution from the Pareto frontier solution set that meets the specific application scenarios. At last, an experiment case is performed to verify the effectiveness and practicality of the optimization model. [ABSTRACT FROM AUTHOR]

During the end-of-life (EOL) product recovery process, there are a series of combinatorial optimization problems (COPs) that should be efficiently solved. These COPs generally result from reverse logistics (RL) and remanufacturing, such as facility location and vehicle routing in RL, and scheduling, planning, and line balancing in remanufacturing. Each of the COPs in RL and remanufacturing has been reviewed; however, no review comprehensively discusses and summarizes the COPs in both. To fill the gap, a comprehensive review of the COPs in both RL and remanufacturing is given in this paper, in which typical COPs arising at the end of the product life cycle are discussed and analyzed for the first time. To better summarize these COPs, 160 papers published since 1992 are selected and categorized into three modules: facility location and vehicle routing in RL, scheduling in remanufacturing, and disassembly in remanufacturing. Finally, the existing research gaps are identified and some possible directions are described. [ABSTRACT FROM AUTHOR]

Disassembly is one of the most time-consuming and labor-intensive activities during the value recovery of end-of-life (EOL) products. The completion time (makespan) of disassembling EOL products is highly associated with the allocation of operators, especially in parallel disassembly. In this paper, asynchronous parallel disassembly planning (APDP), which avoids the necessity to synchronize disassembly tasks of manipulators during the parallel disassembly process, is studied to optimize the task assignment of manipulators for minimal makespan. We utilize four mixed integer linear programming (MILP) formulations to identify the optimal solutions. A set of different-sized instances are used to test and compare the performance of the proposed models, including some real-world cases. Finally, the proposed exact algorithm is further compared with the existing approach to solving APDP. Results indicate that a significant difference exists in terms of the computational efficiency of the MILP models, while three of four MILP formulations can efficiently achieve better solutions than that of the existing approach. [ABSTRACT FROM AUTHOR]

Exploiting energy-saving potentials while obtaining ideal processing results is highly sought for in fiber laser welding (FLW) applications. However, little is known about the correlation between energy consumption and bead geometry of FLW, which is largely determined by processing parameters. In this study, to find the optimal processing parameters of FLW, an ensemble of variable neighborhood search–based gene expression programming (VNS-GEP) and black-box metamodels (EGBM) is presented, combined with non-dominated sorting genetic algorithm (NSGA-II) to form the proposed optimization methodology. Firstly, the optimal weight coefficients of VNS-GEP and two black-box metamodels (Kriging and SVR) are determined considering the leave-one-out generalized mean square error. Then, the EGBM is used to establish the relationship between processing parameters (laser power, welding speed and defocus distance) and response results (energy consumption and bead geometry). Additional experiments are then performed to validate the accuracy of EGBM. Analysis of variance (ANOVA) is carried out to study the main effects of the processing parameters on response results. After that, NSGA-II is employed based on EGBM to approximate the Pareto front of processing parameters with minimal total energy consumption (TEC) and maximal depth-to-width ratio (DWR). Finally, experimental validations show that the obtained solutions can achieve ideal DWR and significant reductions in TEC. In conclusion, the proposed hybrid methodology, EGBM-NSGA-II, can facilitate obtaining optimal welding processing parameters and provide a reliable empirical basis for low-energy laser manufacturing. Note to Practitioners—This study considers a practical problem of energy-aware processing parameter optimization encountered in fiber laser welding applications. Besides, the optimization methodology can be generalized to other laser processes, optimization objectives and processing conditions. This study presents an ensemble of improved gene expression programming and black-box metamodels (EGBM). An EGBM-based optimization methodology is proposed to find the optimal laser welding parameters. The ensemble combines the strengths of different metamodels, reduces the risk of using an incorrect metamodel. Note that the engineers profit from the reduced trials and time to obtain the optimal parameters for ideal results and energy-saving. [ABSTRACT FROM AUTHOR]

Background: Ninety percent of tumour patients have negative emotions during or after anti-tumour treatment, resulting in depression. Western medicine antidepressants have many adverse reactions. Patients often discontinue antidepressants due to intolerance.Aim: This study aims to observe the clinical effect of Chaihu plus Longgu Muli decoction with five-element music therapy in treating cancer-related mild and moderate depression.Methods: A total of 120 patients with depression in the Oncology Department of Tangshan Hospital of Traditional Chinese Medicine, Hebei Province, from July 2017 to March 2019, were selected and randomly divided into the control and study groups (60 cases each) by the random number table method. The study group was treated with Chaihu plus Longgu Muli decoction with five-element music therapy for depression. The control group was treated with escitalopram tablets. After three courses of treatment, the degree of depression, clinical efficacy, quality of life, serum norepinephrine (NE) and 5-hydroxytryptamine (5-HT) levels were observed.Results: After treatment, the HAMD-24 (Hamilton Depression 24) scale scores of the study group (13.93 ± 6.32) were lower than the control group (19.04 ± 7.46), and the difference was statistically significant (x2 = 4.048, p = 0.008). The total effective rate of the study group (93.33%) was higher than the control group (73.33%), and the difference was statistically significant (x2 = 7.260, p = 0.000). After treatment, according to the QLQ-C30 (quality of life questionnaire) scale, the functional score of the study group was higher than the control group (p < 0.05), and the symptom score of the study group was lower than the control group (p < 0.05). After treatment, the serum NE and 5-HT levels in the study group were higher than the control group, and the difference was statistically significant (p < 0.05) (NE: 221.81 ± 31.14 vs 198.91 ± 29.97, t = 4.078, p = 0.000; 5-HT: 141.41 ± 20.35 vs 125.32 ± 14.58, t = 5.781, p = 0.000).Conclusion: Chaihu plus Longgu Muli decoction with five-element music therapy can effectively alleviate patients' cancer-related depression and improve their quality of life, which is worthy of promotion. [ABSTRACT FROM AUTHOR]

Industry is responsible for nearly half of the global energy consumption. Recent studies on sustainable manufacturing focused on energy saving to reduce the unit production cost and environmental impacts. Besides energy consumption, certain manufacturing activities in machine shops, such as the use of cutting fluids, disposal of worn tools, and material consumption, also cause other environmental impacts. Since all these activities lead to carbon footprint directly or indirectly, carbon footprint can be employed as a new and overall environment criterion in manufacturing. In this study, an integrated model for processing parameter optimization and flow-shop scheduling was developed. Objectives to minimize both makespan and carbon footprint were considered simultaneously, which was solved by a multi-objective teaching − learning-based optimization algorithm. Furthermore, three carbon-footprint-reduction strategies were employed to optimize the scheduling results: (i) postponing strategy, (ii) setup strategy, and (iii) processing parameter preliminary optimization strategy. In the theoretical aspect, the strategies greatly improved the performance of the optimization results through reducing machine idle time and cutting down the search space. From the perspective of practical applications, these strategies greatly help elevate production efficiency and reduce environmental impacts. [ABSTRACT FROM AUTHOR]

Characterized by the advantages of high quality, flexibility, and productivity, laser welding of aluminum alloys is extensively applied in manufacturing fields such as automobile, aerospace, and railway. However, due to the low energy conversion efficiency and high carbon emission, the impact of laser welding on the environment cannot be ignored. Therefore, the environmental impact of the laser welding process should be quantified and a comprehensive evaluation considering both carbon emission and welding performance should be conducted. In this paper, the carbon emission characteristics of each subsystem (i.e., laser device, cooling device, motion device, and gas device) are analyzed, and a laser welding carbon emission model is established. In addition, deformation modeled via finite element method and tensile strength measured through orthogonal experiments are selected as welding performance evaluation indicators. Furthermore, the impacts of process parameters (i.e., welding speed, laser power, and defocus) on carbon emission, tensile strength, and deformation are implemented by Taguchi analysis. The results demonstrate that the welding speed is the dominant parameter affecting the carbon emission, followed by defocus, and lastly by the laser power. Moreover, the welding speed is also the dominant parameter impacting tensile strength and deformation. It is once again followed by defocus and laser power. Based on the TOPSIS (technique for order preference by similarity to ideal solution) method, the best-combined group is the 9th group with 75.8% relative closeness, while the worst is the 14th group with 7.9% relative closeness. The optimal combination is identified for the laser welding of 2670 W, welding speed of 9 mm/s, and defocus of −0.3 mm. The corresponding carbon emission, tensile strength, and deformation are 52.50 gCO2-eq, 181.78 MPa, and 1.21 mm, respectively. [ABSTRACT FROM AUTHOR]

Regarding four different energy-conscious scheduling problems, namely, flexible job shop scheduling problem (FJSP), FJSP with transportation time (FJSP-T), FJSP with sequence-dependent setup time (FJSP-SDST), and FJSP with both transportation time and sequence-dependent setup time (FJSP-SDST-T), thirteen mixed integer programming (MIP) models are developed to optimally solve the problems. These models include three nonlinear models and ten linear models, and they are designed from three different modeling ideas, namely, sequence-based modeling idea, adjacent sequence-based modeling idea and machine-position based modeling idea. For each modeling idea, the MIP models are formulated by following the principle: from the simplest FJSP to the most complex FJSP-SDST-T. Regarding nonlinear MIP models, different linearization techniques are used to obtain different linear models. Comparison experiments are conducted from both size and computational complexities to evaluate the models of different modeling ideas for the same problem, the models of the same modeling ideas for the same problem, the models of the same modeling idea for different problems and the models of different modeling ideas for different problems. Experimental results indicate the effectiveness and differences of the proposed MIP models. Specifically, in terms of the average percentage deviation of obtained solution (APE), for FJSP, the machine-position based model with APE being 0.30 outperforms the sequence and adjacent sequence-based models with APE being 0.34 and 3.0 respectively. For FJSP-T, the best sequence-based model with APE being 0.22 outperforms the best machine-position based model with APE being 0.79. For FJSP-SDST, the machine-position based model with APE being 0.06 outperforms adjacent sequence-based model with APE being 1.35. [ABSTRACT FROM AUTHOR]