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Glioma is a common tumor in the brain. CircRNA hsa_circ_0030018, also termed as hsa_circPOSTN_001 (circ_POSTN), is reported to exert a promoting influence on the development of glioma. Our study intends to deeply explore its regulation mechanism of circ_POSTN. Expression of circ_POSTN, microRNA-433-3p (miR-433-3p) and Secreted protein acidic and rich in cysteine (SPARC) was detected by qRT-PCR or western blot assay. The function of circ_POSTN was analyzed by loss-of-function experiments. The targeting relationship between miR-433-3p and circ_POSTN or SPARC was predicted by bioinformatics analysis and validated by dual-luciferase reporter assay. Xenograft modeling was employed to validate the function of circ_POSTN in glioma in vivo. circ_POSTN and SPARC were upregulated while miR-433-3p was downregulated in glioma tissues and cells. Both circ_POSTN and SPARC knockdown inhibited clonogenicity, migration, and promoted apoptosis of glioma cells. Circ_POSTN sponged miR-433-3p to regulate SPARC expression. Gain of SPARC largely attenuated circ_POSTN knockdown or miR-433-3p overexpression-mediated effects on glioma cell clonogenicity, migration, and apoptosis. Furthermore, silencing of circ_POSTN decreased xenograft tumor growth in vivo. Inhibition of circ_POSTN repressed glioma development, at least in part, via regulating the miR-433-3p/SPARC axis, providing evidence for circ_POSTN as a potential therapeutic target for glioma. [ABSTRACT FROM AUTHOR]

This paper considers the large-scale bike sharing repositioning problem (BSRP) frequently encountered in modern bike sharing systems. To cope with customer demand fluctuations, BSRP aims to identify the optimal routes traveled by homogeneous vehicles to fulfill the inventory needs at each bike-sharing station in order to minimize the total cost. It is computationally intractable to obtain promising solutions, especially for large-scale instances, given its NP-hardness. This paper adapts the two-echelon structure from the vehicle routing problem (VRP) to BSRP, proposes the two-echelon BSRP model and demonstrates its competitiveness. First, a novel fuzzy clustering strategy quantitatively considering the correlation between stations is designed to construct the clusters with satellites and their corresponding customers to form the two-echelon structure. Then, a tailored fuzzy correlation based adaptive variable neighborhood search (FC-AVNS) with newly designed neighborhood structures and several feasibility and satisfaction check mechanisms is proposed to construct the routes within and between the clusters. Performance of the proposed method is compared with that of a exact model solved by CPLEX and other three state-of-the-art methods. Also, comparisons are made between the presented fuzzy clustering strategy and the other two classical clustering methods taken from the literature. Computational experiments based on medium- and large-scale instances involving 100 to 519 stations are performed and the results validate the superior performance of the proposed method with respect to solution efficiency and stability. • The two-echelon structure from VRP literature is first adapted to solve BSRP. • A fuzzy clustering strategy is designed considering both distance and inventory factors. • Objective functions with traveling cost, inventory cost and service level are considered. • A tailored fuzzy correlation based adaptive variable neighborhood search is provided. [ABSTRACT FROM AUTHOR]

Background: The duration of virus shedding is necessary for determining the infectious period. But there were few quantitative studies on the changes of viral load and the law of the viral shedding in hand foot and mouth disease (HFMD) patients has not yet been clarified.Methods: This study will prospectively recruit coxsackievirus A10 (CV-A10), coxsackievirus A16 (CV-A16) and coxsackievirus A6 (CV-A6) infected inpatients from January 2022 to December 2022. A series of samples and questionnaire information will be collected regularly to establish the dynamic function relationship between time and viral load changes and a Bayesian multilevel model will be constructed to clarify the evolvement rules which reflect the dynamic changes of viral load and the duration of viral shedding in patients with HFMD.Discussion: The results of this study is expected to further clarify the evolvement rules which reflect the dynamic changes of viral load and the duration of viral shedding in HFMD patients under the influence of related factors. It can also provide important evidence for the scientific definition of the infectious period and isolation period of HFMD in China. [ABSTRACT FROM AUTHOR]

Laser butt welding (LBW) with high quality is widely sought-after, but results in non-negligible carbon emission (CE). However, predicting bead geometry and CE of LBW is important and challenging, especially when facing new scenarios. In this study, we develop the LBW platform with a CE data acquisition system, and propose a physics-assisted transfer learning (PTL) methodology to predict bead geometry and CE in LBW by leveraging physical knowledge and data of different scenarios. Experiments are designed using the optimal Latin hypercube sampling, and conducted to acquire bead geometry of bare-plate welding and butt welding. The physics-assisted dimensionless analysis is introduced to evaluate and filter the experimental data of bead geometry. Kriging (KRG) metamodel is trained to derive the relation between processing parameters and bead geometry, and is mapped to apply the data trend of bare-plate welding to butt welding. Radial basis function (RBF) is then used as the residual compensation to construct KRG-RBF metamodel. A transfer learning metamodel is obtained by combining KRG and KRG-RBF metamodels using optimized weight coefficients. Similarly, a PTL metamodel is constructed via mapping operation and residual compensation on the analytical formula to predict welding CE for a different scenario. The carbon efficiency assessment adopted can contribute to LBW with low-carbon and high-quality. Finally, cross-validation and supplementary experiments are conducted to evaluate the prediction accuracy of constructed metamodels. The results show that the proposed PTL methodology can identify outliers caused by scenario anomalies and achieve superior prediction accuracy. • The laser welding platform equipped with a data acquisition system of carbon emissions is designed and developed. • A physics-assisted transfer learning method is proposed to use physical knowledge and multi-scenario data for prediction. • The dimensionless analysis with physical constraints is introduced to evaluate and filter data of bead geometry. • The carbon efficiency assessment considering weld bead integrity and carbon emissions is presented. [ABSTRACT FROM AUTHOR]

• formulate and study a new and practical variant of static bike-sharing re-positioning problem (BSRP) with multiple vehicle depots and comprehensive objective function of traveling cost minimization and inventory cost minimization. • propose an effective clustering strategy that enables decomposition of the original problem into classical traveling salesman problem and multi-depot vehicle routing problem. • design tailored destroy-and-repair algorithm to conduct clustering optimization, and adaptive variable neighborhood search for routing optimization. • perform extensive computational studies to validate performance of the proposed algorithm, which demonstrates the algorithm's competitiveness in solving large-scale BSRPs when compared with CPLEX and state-of-the-art algorithms. This paper studies the bike-sharing re-positioning problem (BSRP) frequently encountered in modern bike-sharing systems that are widely deployed around the world. To cope with customer demand fluctuations and improve service level, BSRP aims to identify the optimal vehicle routes to visit bike-sharing stations in order to balance their inventories, picking up excess bikes from surplus stations and adding needed bikes to insufficient stations, with the objective of minimizing total traveling cost and inventory cost. The mathematical model of the studied problem is first given, detailing the considerations of multiple depots available for re-positioning vehicles and the extra objective of inventory cost minimization. An effective clustering strategy is then proposed to put bike-sharing stations into self-sufficient groups, which is shown to be able to greatly decompose the problem complexity for large-scale instances. A destroy-and-repair algorithm is developed to improve the clusters, and an adaptive variable neighborhood search algorithm is designed to conduct intra-cluster and inter-cluster vehicle routing optimization. Performance of the hybrid algorithm is validated on three sets of benchmark instances, and compared with CPLEX as well as state-of-the-art algorithms from the literature, which demonstrates that the proposed algorithm is highly competitive in solving BSRPs. [ABSTRACT FROM AUTHOR]

Due to the rapid technology advancement and market changes, products are becoming outdated and subsequently discarded faster than ever before. Recovery, recycling, and remanufacturing of end-of-life (EOL) products are getting more attention. Disassembly is indispensable to recycle and remanufacture EOL products, and a disassembly line is an efficient way to perform it. A disassembly line balancing problem (DLBP) aims at streamlining the disassembly activities such that the total disassembly time consumed at each workstation is approximately the same and approaching the cycle time. However, the assignment of disassembly operations to workstations in a disassembly shop should ensure the recovery of valuable components and reduce undesirable impact on the environment as much as possible. In this paper, a novel heuristic technique combining multicriterion decision making (MCDM) and general variable neighborhood search (GVNS) is proposed to solve the DLBP. Based on the characteristics of the DLBP, an innovative MCDM method based on fuzzy set theory, grey relational analysis, and Choquet fuzzy integral is developed to evaluate the performance scores and determine the ranking of disassembly tasks. Subsequently, an improved GVNS algorithm is employed to further balance a disassembly line with three objectives, in which a new metric is formulated to integrate with the ranking from MCDM. The proposed method not only takes a comprehensive objective system into consideration but effectively generates a good enough tradeoff disassembly solution. Finally, the proposed approach is illustrated with an example and compared with two other heuristics to show its efficacy in solving the DLBP. [ABSTRACT FROM AUTHOR]

Abstract With energy shortage and environmental pollution becoming increasingly severe problems, energy-efficient scheduling is attracting much more attention than before. This paper addresses the flexible job shop scheduling problem (FJSP) with the objective of minimizing total energy consumption. Firstly, the total energy consumption of the workshop is discussed and modelled. Then, based on two different modeling ideas namely idle time variable and idle energy variable, six new mixed integer linear programming (MILP) models with turning Off/On strategy are proposed. The original objective function of the model based on idle time variable is nonlinear and linearization is needed. For the linearization, additional decision variables and constraints are added. The objective function of the model based on idle energy variable is originally linear and concise. Lastly, those six proposed models and the existing one are detailedly compared and evaluated under both the size and computational complexities. The correctness and effectiveness of all MILP models are verified by using CPLEX SLOVER to carry out numerical experiments. The results show that the MILP models based on different modeling ideas vary remarkably in both size and computational complexities, and all the six models proposed in this paper outperform the existing model significantly. The proposed models will help the enterprises rationalize production so as to reduce energy consumption and costs. [ABSTRACT FROM AUTHOR]

With the rapid technology advancement and market changes products are becoming outdated and subsequently discarded faster than ever before. As a result, recovery, recycling, and remanufacturing of end-of-life products are gaining more attention. Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line is an efficient way to achieve it. A disassembly line balancing problem aims at optimizing the disassembly sequences in which the disassembly times of workstations are approximately equal and approaching the cycle time. However, in addition to line balancing assigning disassembly tasks to workstations needs to consider other factors, such as how to recover valuable components as fast as possible and reduce undesirable impacts on the environment as much as possible. This work aims at addressing a disassembly line balancing problem with multiple objectives, including economic and environmental factors and disassembly line efficiency. A three-phased methodology is proposed to solve this problem. The proposed method not only enables the consideration of objectives with interdependent weights in multi-criteria decision making but effectively generates a good enough trade-off disassembly solution using a 2-optimal algorithm. The proposed approach is tested on a benchmark and compared with three other methods. Experiment results are shown to verify its performance. [ABSTRACT FROM AUTHOR]

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature. [ABSTRACT FROM PUBLISHER]

Reducing environmental impact while achieving desired processing performance is increasingly sought after. Various welding processes are widely employed in advanced manufacturing, but their carbon emission characteristics vary. However, few studies have compared the carbon emissions of different welding methods. Therefore, it is urgent to analyze the carbon emission characteristics of commonly used welding methods, and to consider the welding performance for a comprehensive comparison. In this study, we construct the carbon emission models of three welding methods (laser, arc, and laser–arc hybrid welding) by building the monitoring platform. The characteristics and proportions of carbon emissions of each system in three welding methods are analyzed. Additionally, the bead morphology, microhardness, and microstructure of aluminum alloy joints are investigated to explain the results of tensile tests. The experimental results show that the appropriate welding heat inputs (WHI) of laser, arc, and laser–arc hybrid welding lead to the ultimate tensile strength (UTS) of 229.1 ± 2.6, 186.1 ± 3.0, and 250.0 ± 0.8 MPa, with the carbon emissions of 43.12, 26.06, and 120.25 g respectively. However, the carbon efficiency of laser–arc welding is lower than laser welding, while arc welding has the lowest carbon efficiency and the highest carbon emission. Increased welding speed can help reduce carbon emissions. For each welding method, properly penetrated joints have better tensile properties than joints with insufficient or excessive WHI, and too high WHI results in low carbon efficiency due to increased carbon emissions and decreased UTS. The welding process with lower WHI help to narrow the fusion zone (FZ) and heat affected zone, and refine the grains in the FZ. This study provides an empirical understanding on the relationship of welding method–carbon emission characteristic–mechanical property. [Display omitted] • Carbon emission models are constructed by building the monitoring platform. • Carbon emission characteristics of three welding methods are analyzed. • Tensile properties are explained by bead morphology, hardness, and microstructure. • Relation of welding method–carbon emission characteristic–mechanical property is studied. [ABSTRACT FROM AUTHOR]

Process planning and scheduling are two crucial components in a manufacturing system. The integration of the two functions has an important significance on improving the performance of the manufacturing system. However, integrated process planning and scheduling is an intractable non-deterministic polynomial-time (NP)-hard problem, and the multiple objectives requirement widely exists in real-world production situations. In this paper, a multi-objective mathematical model of integrated process planning and scheduling is set up with three different objectives: the overall finishing time (makespan), the maximum machine workload (MMW), and the total workload of machines (TWM). A multi-objective memetic algorithm (MOMA) is proposed to solve this problem. In MOMA, all the possible schedules are improved by a problem-specific multi-objective local search method, which combines a variable neighborhood search (VNS) procedure and an effective objective-specific intensification search method. Moreover, we adopt the TOPSIS method to select a satisfactory schedule scheme from the optimal Pareto front. The proposed MOMA is tested on typical benchmark instances and the experimental results are compared with those obtained by the well-known NSGA-II. Computational results show that MOMA is a promising and very effective method for the multi-objective IPPS problem. [ABSTRACT FROM AUTHOR]

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]

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]

In this paper, we investigate the optimization of process planning in which various flexibilities are considered. The objective is to minimize total weighted sum of manufacturing costs. Various flexibilities, including process flexibility, sequence flexibility, machine flexibility, tool flexibility, and tool access direction (TAD) flexibility, generally exist in process planning and consideration of these flexibilities is essential for improving production efficiency and system flexibility. However, process planning is strongly NP-hard due to the existence of various flexibilities as well as complex machining precedence constraints. To tackle this problem, an imperialist competitive algorithm (ICA) is employed to find promising solutions with reasonable computational cost. ICA is a novel socio-politically motivated metaheuristic algorithm inspired by imperialist competition. It starts with an initial population and proceeds through assimilation, position exchange, imperialistic competition, and elimination. Computational experiments on three sets of process planning problem taken from literature are carried out, and comparisons with some existing algorithms developed for process planning are presented. The results show that the algorithm performs significantly better than existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS), and particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]

Since multi-objective flow shop scheduling problem (MFSP) plays a key role in practical scheduling, there has been an increasing interest in MFSP according to the literature. However, there still have been wide gaps between theories and practical applications, and the review research of multi-objective optimization algorithms in MFSP (objectives > 2) field is relatively scarce. In view of this, this paper provides a comprehensive review of both former and the state-of-the-art approaches on MFSP. Firstly, we introduce a broad description and the complexity of MFSP. Secondly, a taxonomy of multi-objective optimizations and an analysis of the publications on MFSP are presented. It is noteworthy that heuristic and meta-heuristic methods and hybrid procedures are proven much more useful than other methods in large and complex situations. Finally, future research trends and challenges in this field are proposed and analyzed. Our survey shows that algorithms developed for MFSP continues to attract significant research interest from both theoretical and practical perspectives. [ABSTRACT FROM AUTHOR]

Abstract: Traditionally, process planning and scheduling were performed sequentially, where scheduling was done after process plans had been generated. Considering the fact that these two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this paper, an agent-based approach has been developed to facilitate the integration of these two functions. In the approach, the two functions are carried out simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show the proposed approach has achieved significant improvement. [Copyright &y& Elsevier]

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]

As the future development trend of advanced manufacturing technologies, manufacturing grid (MG) is attracting more and more interests, and massive results have been achieved. However, as current existing service MG platforms are based on different standards and specifications, access to manufacturing resources of these platforms remains incompatible. To enhance the adaptability of MG with different standards, a uniform manufacturing resource service adapter for MG called uniform service adapter (UA) is suggested. Firstly, existing service access mechanisms of current MG are analyzed briefly. Then the frameworks of UA, which are composed of three parts, namely the service adapter, the data management adapter, and the binding adapter, is presented. Next, each part of UA is realized in detail. Lastly, a simple test scene of UA is provided. The UA introduced in the paper can accommodate the dynamics, complexity, and heterogeneity of the situation, providing a uniform interface for the upper applications to overcome the incompatibility of the services with different standards. [ABSTRACT FROM AUTHOR]

In order to deal with uncertainties, a robust schedule for M-machine permutation flowshop is proposed. The presented robust schedule is aimed to maximize the probability of ensuring that makespan will not exceed the expected completion time. An improved genetic algorithm (GA) with a new generation scheme is developed, which can preserve good characteristics of parents in the new generation. Experiments are performed to get robust schedules for well-known Car and Rec permutation flowshop problems, taken from OR library. The schedules obtained from the improved GA are compared with the schedules formed by well-known heuristic in literature. Computational results show that the permutation flowshop schedules obtained from improved GA are robust to produce an affirmative percentage increase in the probability of getting makespan less than expected completion time. [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]

Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners—There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators. [ABSTRACT FROM AUTHOR]

Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts’ subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify $ {\lambda }$ -fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among ${\lambda }$ -fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of ${\lambda }$ -fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Disassembly planning (DP) is critical in remanufacturing and value recovery from end-of-life products and has attracted increasing attention due to the recent resurgence of research on circular economy. DP problem is NP-hard and its complexity increases exponentially with the size of problem. Sequence-dependent cost due to varying quality of the parts to be retrieved further increases the complexity of DP problems. This paper investigates the DP considering sequence-dependent costs among disassembly operations. A mathematical model is proposed with the objective to maximize the recovery profit using an AND/OR graph (AOG) subject to sequence-dependent costs. A novel two-phase heuristic method is developed to effectively generate feasible disassembly sequence according to the AOG in reasonable computation time. In addition, an improved genetic algorithm (IGA) is proposed to solve the problem, in combination with the presented two-phase heuristic. The performance of IGA is measured on a series of test problem instances against exact methods including CPLEX and an iterative method. Results indicate that IGA successfully find the near-optimal/optimal solutions and outperforms the other methods in terms of computation time. Finally, the proposed method is applied to compute the disassembly solution of a HG5-20 triaxial five speed mechanical transmission. Compared to the existing disassembly solutions of the transmission, the obtained solutions by IGA can shorten about 11% disassembly time and increase by approximately 7% recovery profit. [ABSTRACT FROM AUTHOR]

We study the survivability of weighted-edge attacks with incomplete information on a free-scale network. We consider a situation where attackers can detect partial edges of the network, and the information regarding the detected edges may be imprecise. Random and intentional attacks are the two extreme cases of this investigation. In this article, edge betweenness is adopted to describe the weight of an edge. Based on this, α is employed as the parameter describing the scope of edges that can be detected from the network and β as the parameter depicting the accuracy of the already detected edge weight information. Attack strategies that are different from both random and targeted attacks are designed for a scale-free network with incomplete information. Numerical simulations are performed and results are obtained: (i) a larger α or β can worsen both the network connectivity and efficiency while confronting attacks; (ii) when α is small, β has a relatively small impact on the network connectivity ς , but with the increase in α , both α and β both play important roles in it; (iii) β consistently plays an important role in network efficiency, regardless of the value of α. The results of this article are helpful for the future development of effective protection strategies in scale-free networks as it is more convenient and realistic to protect network information than to adjust the network structural topology. • A larger α or β can make both ς and η worse confronting attacks. • α and β affect strongly on ς when they increase. • β always play an important role on η no matter whatever the value of α is. [ABSTRACT FROM AUTHOR]

Recent years have seen a great deal of attention in energy conservation for production and manufacturing activities, particularly for energy-intensive industries. One of the useful strategies in reducing unnecessary energy consumption is to schedule these activities by considering both energy-driven and time-oriented criteria. This scheduling model can make an interaction between the energy consumption and the production cost to realize an efficient and sustainable production process. In this regard, the customers' expectation for due date is another important factor for decision-makers to control the delay in delivery. Making these decisions is extremely difficult due to uncertain circumstances to extract the accurate information of facilities and jobs in advance. Aforementioned issues in the context of urgent need for energy-conservation as well as the advent of globalized and multi-factory manufacture motivate our attempts to address a stochastic multi-objective distributed permutation flow shop scheduling problem by considering total tardiness constraint via minimizing the makespan and the total energy consumption. Due to the uncertainty of the proposed problem, a chance-constrain approach is used to describe decision-makers' awareness for the total tardiness, and accordingly, a chance-constrained programming model is utilized to formulate this problem. As a complicated optimization problem, a new multi-objective brain storm optimization algorithm incorporating stochastic simulation approach is specifically designed to better solve problem. A comparative study based on a set of benchmark test problems as well as two classical and popular algorithms is provided. The experimental results demonstrate that the proposed algorithm shows a very competitive performance in dealing with the investigated problem. [ABSTRACT FROM AUTHOR]

• A feature reduction method based on multi-algorithm feature screening and linear discriminant analysis is proposed. • Similar working condition groups are divided for multi-conditions. • The optimal prediction model for each group of conditions is determined by the ensemble method. • A fast selection method for prediction models of milling tool wear for new working conditions is proposed. Accurate prediction of tool wear in multi-conditions is still a thorny problem, and rapid and accurate construction of prediction models for multi-conditions is an essential part of achieving intelligent manufacturing. For multi-conditions, a novel method of rapid selection of milling tool wear prediction model based on linear discriminant analysis and the ensemble method was proposed in this paper. The linear discriminant analysis is used to divide similar working conditions, and determining the optimal prediction model for different working conditions relies on ensemble methods. Compared with the traditional transfer learning algorithm, the proposed method can be used for the rapid construction of new models and prediction of new working conditions, which are validated by real experiments. The results show that the selection of prediction models based on working condition division is superior to other models. Therefore, this method provides a guarantee for rapidly constructing accurate tool wear multi-condition predictive models. [ABSTRACT FROM AUTHOR]

While the concept of remanufacturing is gaining popularity in practice, its effect on environmental protection and energy saving is significant, which has attracted widespread concerns, especially in the automotive components field. Automotive components remanufacturing (ACR), as a prominent part in automotive life-cycle development, can dramatically reduce resources-waste and mitigate energy-crisis, which has become a prominent direction to promote sustainable development of Chinese economy. Thus, influence factors analysis and enterprise operation patterns assessment which primarily contribute to ACR industry development are of paramount importance. This paper elaborates the criteria influencing the ACR industry operation and analyzes the main operation patterns for ACR in China. To overcome the uncertainty of the evaluation process, a new hybrid multi-criterion decision-making (MCDM) model that combines fuzzy AHP (analytical hierarchy process) and fuzzy G-TOPSIS (combining gray relation analysis and technique for order performance by similarity to ideal solution) is proposed to determine weights of influence criteria and evaluate ACR production operation patterns. In addition, sensitivity analysis is applied to monitor the robustness of the method. The study refers to the experience of Chinese end-of-life vehicle (ELV) laws and implementation requirements of extended producer responsibility (EPR). The obtained results provide the new guideline to revise the regulations of Chinese ACR industry. [ABSTRACT FROM AUTHOR]

Disassembly sequencing has significant effects on the performance of remanufacturing and recycling of used or discarded products. Studies on disassembly sequence optimization have largely focused on sequential disassembly. However, for large or complex products sequential disassembly takes long time to complete and is rather inefficient since it removes only one part or subassembly at a time with only one operator assigned to disassemble a product. This work studies selective cooperative disassembly sequence planning (SCDSP) problem which is essential to disassemble large or complex products in an efficient way. Similar to sequential disassembly planning, SCDSP aims at finding the optimal disassembly task sequence, but is more complicated. SCDSP is a nonlinear NP-complete combinatorial optimization problem, and evolutionary algorithms can be adopted to solve it. In this paper exclusive and cooperative relationships are introduced as additional constraints besides the common precedence relationship. A novel procedure to generate feasible cooperative disassembly sequences (GFCDS) is proposed. A mathematical programming model of SCDSP is developed based on the parallel disassembly characteristics with two optimization objectives i.e. disassembly time and profit, considered. A multi-objective evolutionary algorithm (MOEA), i.e., multi-objective discrete artificial bee colony optimization (MODABC), is adopted to solve the problem to create the Pareto frontier. This approach is applied to real-world disassembly processes of two products (a small product and a medium/large one) to verify its feasibility and effectiveness. Also, the proposed method is compared with the well-known NSGA-II. For our comparative study, the nondominated solutions of the two MOEAs are compared in both cases, and two quantitative metrics, i.e., inverted generational distance ( IGD ) and spacing ( SP ), are adopted to further measure the algorithm performance. Results indicate that the set of nondominated solutions from MODABC are better for each instance tested, and the Pareto front is overall superior to that from NSGA-II. For both cases, IGD and SP are decreased by up to 81.5% and 62.2%, respectively. [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]

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]

Two-sided assembly line balancing problems usually occur in plants producing large-sized high-volume products such as buses, trucks, or cars. Comparing to traditional simple assembly lines, the two-sided assembly line can shorten the length of line, enhance the utilization of tools and fixtures, and improve the production rate of operators, etc. Whereas in a two-sided assembly line, due to a couple of constraints such as the operation direction constraint and the sequence-dependent finish time constraint, the balancing procedure is much more complex than for a simple assembly line. In this paper, besides some fundamental constraints of the two-sided assembly lines, we put forward additionally the restriction of operator number at each workstation, and take into account concurrently more constraints such as the positional constraints, zoning constraints, and the synchronous task constraints. A novel hybrid imperialist competitive algorithm (ICA) combined with the late acceptance hill-climbing (LAHC) algorithm is employed to solve the multiconstraint problem. For the evaluation of the proposed algorithm, the performance of the hybrid ICA is examined over benchmarks from the literature and compared with the LAHC algorithm and the lower bounds of the instances. Furthermore, a practical two-sided assembly line balancing problem from an engineering machinery company is solved with the proposed approach. The results confirm that the hybrid ICA can effectively shorten the line length and reduce the amount of operators for the existing two-sided assembly line. [ABSTRACT FROM AUTHOR]

Flexible job-shop problem has been widely addressed in literature. Due to its complexity, it is still under consideration for research. This paper addresses flexible job-shop scheduling problem (FJSP) with three objectives to be minimized simultaneously: makespan, maximal machine workload, and total workload. Due to the discrete nature of the FJSP problem, conventional particle swarm optimization (PSO) fails to address this problem and therefore, a variant of PSO for discrete problems is presented. A hybrid discrete particle swarm optimization (DPSO) and simulated annealing (SA) algorithm is proposed to identify an approximation of the Pareto front for FJSP. In the proposed hybrid algorithm, DPSO is significant for global search and SA is used for local search. Furthermore, Pareto ranking and crowding distance method are incorporated to identify the fitness of particles in the proposed algorithm. The displacement of particles is redefined and a new strategy is presented to retain all non-dominated solutions during iterations. In the presented algorithm, pbest of particles are used to store the fixed number of non-dominated solutions instead of using an external archive. Experiments are performed to identify the performance of the proposed algorithm compared to some famous algorithms in literature. Two benchmark sets are presented to study the efficiency of the proposed algorithm. Computational results indicate that the proposed algorithm is significant in terms of the number and quality of non-dominated solutions compared to other algorithms in the literature. [ABSTRACT FROM AUTHOR]

• The depot inventory is determined together with vehicle routes in static bike-sharing rebalancing problems. • Two models are formulated to minimize the travel cost and the depot inventory cost. • An improved general variable neighbourhood search is developed to solve our problem. Smart shared mobility is an emerging transportation strategy that promotes sustainable and intelligent transportation. As one mode of smart shared mobility bike sharing is gaining popularity in recent years. A daily rebalancing operation is commonly carried out to keep high level service of bike-sharing systems (BSSs). The static bike-sharing rebalancing problems (SBRPs) studied in existing papers focus on determining the vehicle routes with minimal traveling cost. However, the depot inventory is rarely considered during the relocation. Thus, this paper researches the integration of the depot inventory and vehicle routing problems, with the aim of minimizing the daily operational cost including the depot inventory cost (DIC) and the traveling cost. First, two mixed integer programming (MIP) formulations are proposed to find the daily optimal decision on the vehicle routes and the numbers of bikes and vehicles employed from the depot. Based on the models, an improved general variable neighborhood search (IGVNS) algorithm is developed with a variety of neighborhood structures and a hybrid strategy. Finally, we apply a set of benchmark instances to test our proposed model and approach, and the computational results demonstrate that IGVNS can efficiently compute the SBRP and achieve lower operational cost than the existing solutions. [ABSTRACT FROM AUTHOR]

Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance. [ABSTRACT FROM AUTHOR]