Balancing Problem Research Articles

Human-robot collaboration in assembly line balancing problems: Review and research gaps

Researchers have been working on assembly line optimization problems, especially assembly line balancing, for decades. Integration of Industry 4.0 technologies into assembly lines creates new challenges in optimal resource selection, task allocation, line balance, and scheduling decisions, to name a few. Over the last decade, researchers have developed novel frameworks for conventional assembly line optimization problems that comply with the recent industrial revolution and leading technologies. State-of-the-art Balancing problems in assembly lines with collaborative robots are the core of this study as a key decision in Assembly 4.0. The collaboration of humans and robots in a fenceless environment requires a more comprehensive approach than the traditional task-station assignment in a balancing model. Resource selection/allocation and scheduling are the most common decisions that researchers combine with balancing models. Thus, the studies on all these optimization challenges in the last ten years through the Web of Science and Engineering Village are present in the current review. We analyze the articles’ statistics (e.g., year and journals) and contents to find the research trends and gaps for future research direction in collaborative robotic assembly line optimization which incorporates occupational health and safety.

Integrated optimization and engineering application for disassembly line balancing problem with preventive maintenance

The current research on the disassembly line balancing (DLB) problem has not yet considered the preventive maintenance of workstation equipment. The lack of preventive maintenance can easily lead to sudden failure of the disassembly line equipment, which will affect the disassembly efficiency. Preventive maintenance is planned maintenance, which can effectively reduce the probability of equipment failure in actual disassembly line engineering applications. This study integrates preventive maintenance into the DLB. The objectives focus on optimizing both cycle times under the regular DLB scenario and the preventive maintenance DLB scenario, and the number of task adjustments between the two scenarios. A mixed integer linear programming model integrating preventive maintenance is established. Then an improved genetic simulated annealing (IGSA) algorithm based on the disassembly characteristics is designed. The exact solver and the proposed IGSA are used to solve a small-scale case simultaneously to verify the correctness of the model and algorithm. The performance of the proposed IGSA is verified by comparing the solution results of 21 benchmarks. Finally, the proposed model and algorithm are applied to a microwave disassembly line as the engineering application case. The results show that the integrated optimization can ensure the disassembly efficiency of the regular disassembly scenario, and can fully utilize workstations of unperformed maintenance during the preventive maintenance scenario. This can also effectively improve workstation utilization and reduce time costs.

An adaptive load balancing strategy for stateful join operator in skewed data stream environments

As one of the most computationally intensive operations in stream processing applications, join operation can cause severe load imbalance problem when dealing with skewed data. Most of the popular solutions focused on monitoring-based dynamic balancing strategies, making it difficult to quickly adapt to the changing frequency of data stream, and sometimes failing the balancing strategies that try to address the skewed load in the cluster. To address these issues, we propose to use the prediction results of a deep reinforcement learning model and adjust the grouping strategy in advance before the frequency change of data stream. It will enable the system to quickly adapt to data stream fluctuation, while managing the resources for effective resource utilization. The following contributions are made in this paper: 1) Explore the main factors that trigger the load skewness problem in distributed stream join systems and carefully model the load balancing problem at the application level. 2) Develop a Gated Recurrent Unit Sequence to Sequence model to predict key frequency distribution of streams, and propose a dynamic grouping algorithm and a feedback-based resource elasticity scaling algorithm to solve the load imbalance problem caused by hot keys in real time. 3) Design and implement an adaptive stream join system Aj-Stream based on the prediction model and the proposed algorithm on Apache Storm. 4) Evaluate the system performance through extensive experiments on a large scale real-world dataset and multiple synthetic datasets. The experimental results demonstrate that the Aj-Stream proposed in this paper exhibits stable throughput and latency performance with both static data streams of varying skewnesses and dynamic data streams. In comparison to existing stream-connected systems, Aj-Stream demonstrated a 22.1% increase in system throughput and a 45.5% decrease in system latency when dealing with frequently fluctuating data streams.

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.

An approach for unbalanced fully rough interval transportation problem

In this study, we consider an unbalanced fully rough interval transportation problem, where all the parameters and decision variables are represented by rough interval numbers. A method named as split and separation method has been proposed in the literature to find the optimal solution of balanced fully rough interval transportation problem. As per our knowledge, no method exists in the literature to solve an unbalanced fully rough interval transportation problem. Therefore, a new method is proposed in this study to solve such problem. Using proposed methodology, firstly the unbalanced problem is converted into a balanced one and then the optimal solution of the balanced problem is obtained. To show the applicability of the proposed methodology, a numerical example is solved. Finally, the study’s conclusions and future research directions are discussed.

A Three-link Gravity-balanced Mechanism for Operating Room Assistance

The compensation of gravity is not a new problem and gravity-balanced mechanisms have been extensively studied. Some of these studies present practical applications of the gravity-balanced mechanisms, while others focus on the theoretical modelling of the mechanism. In this study, a practical application, a gravity-balanced planar robotic manipulator for operating room assistance is presented. In this design, an auxiliary parallelogram with two linear springs is utilized to accomplish gravity compensation. As a passive gravity-balanced mechanism, it does not have any actuators. The novelty of the mechanism is the exchangeable end-effector and changeable spring attachment location accordingly. The simulation results show that the designed mechanism is gravity-balanced in all configurations. The presented mechanism in this study can also be extended to solve a multi-link gravity balance problem.

Research on the Influence of Electric Vehicle Integration in Island Microgrid, Vietnam

Vietnam's economy is developing strongly, and the demand for energy use will increase rapidly. The development of smart grids contributes significantly to the transition and sustainable development of energy from renewable energy sources to improve the quality of the national power supply and promote the sustainable use of electricity economically and efficiently. Thus, this is highly beneficial in reducing carbon emissions and other types of pollution. Besides, electrification in the transportation industry is developing rapidly, such as Electric Vehicles (EVs) and Metros in recent years. Integrating electric vehicles into the grid will enable two-way energy exchange, reactive power compensation and load balancing. However, the number of EVs participating in charging at a time will cause some conflicts, such as voltage and power loss at the nodes. Therefore, the balancing problem between load demand and generation source is a difficult task in planning operations. This paper presents a method to optimize island Microgrid (MG) operation with the participation of electric vehicles based on renewable energy sources. Optimization techniques in intelligent resource forecasting and management algorithms are built in MATLAB to achieve different requirements. The proposed Microgrid manages energy efficiency that adapts to the variability of Renewable Energy with improved efficiency.

Two sided disassembly line balancing problem with rest time of works: A constraint programming model and an improved NSGA II algorithm

In the process of disassembly, it is difficult for workers who have been in working condition for a long time to ensure the high-quality completion of the tasks they undertake, which will lead to the reduction of disassembly profit. Providing workers with longer rest time within a certain range can improve the quality of work and thus increase dismantling profits. In response to the above phenomenon, this study proposes for the first time a two sided disassembly line balancing problem with rest time of works (TDLBP-RTW), aiming to optimize the disassembly profit, number of workstations, number of sub workstations, workload smoothness, and hazard index. In addition, the existing algorithms for solving the disassembly line balance problem often do not consider the distribution of feasible solutions. Their search process for the solution space tree is quite random, leading to serious repeated searches and resulting in a significant waste of search resources. To solve the above problems, this study improves the NSGAII algorithm and designs a multi-objective evolutionary algorithm (NSGAII-FSDRSA) considering feasible solution distribution and real-time search resource allocation. The biggest difference between NSGAII-FSDRSA and traditional methods is that NSGAII-FSDRSA can analyze the distribution of feasible solutions and the allocation of search resources, and allocate more search resources to areas with dense feasible solution distribution and less search resource allocation based on the analysis results. The performance of the proposed algorithm is verified by solving three classical examples and by comparing with many meta-heuristic algorithms. Then apply the proposed model and method to the refrigerator disassembly production line of a disassembly enterprise in China. The disassembly scheme of NSGAⅡ-FSDRSA is superior to five classic multi-objective algorithms. The results indicate that this method can improve the performance of the disassembly line.

Construction of cascade circuits for dynamic temporal regulation and its application to PHB production

BackgroundTo maximize the production capacity and yield of microbial cell factories, metabolic pathways are generally modified with dynamic regulatory strategies, which can effectively solve the problems of low biological yield, growth retardation and metabolic imbalance. However, the strategy of dynamic regulating multiple genes in different time and order is still not effectively solved. Based on the quorum-sensing (QS) system and the principle of cascade regulation, we studied the sequence and time interval of gene expression in metabolic pathways.ResultsWe designed and constructed a self-induced dynamic temporal regulatory cascade circuit in Escherichia coli using the QS system and dual regulatory protein cascade and found that the time intervals of the cascade circuits based on the Tra, Las system and the Lux, Tra system reached 200 min and 150 min, respectively. Furthermore, a dynamic temporal regulatory cascade circuit library with time intervals ranging from 110 to 310 min was obtained based on this circuit using promoter engineering and ribosome binding site replacement, which can provide more selective synthetic biology universal components for metabolic applications. Finally, poly-β-hydroxybutyric acid (PHB) production was taken as an example to demonstrate the performance of the cascade circuit library. The content of PHB increased 1.5-fold. Moreover, circuits with different time intervals and different expression orders were found to have different potentials for application in PHB production, and the preferred time-interval circuit strain C2-max was identified by screening.ConclusionsThe self-induced dynamic temporal regulation cascade circuit library can enable the expression of target genes with sequential changes at different times, effectively solving the balance problem between cell growth and product synthesis in two-stage fermentation and expanding the application of dynamic regulatory strategies in the field of metabolic engineering.

Effects of progressive functional ankle exercises in spastic cerebral palsy, plantarflexors versus dorsiflexors: a randomized trial.

Children with cerebral palsy (CP), even those who have very mild impairment, have lower muscle strength than their typically developing peers. The ankle dorsiflexors (DFs) and plantarflexors (PFs) of children with CP are especially weak. Weakness in the ankle muscles causes problems in functional skills, mobility, and balance in spastic CP (SCP). The aim of this study was to investigate the effects of progressive functional exercises (PFEs) on the DF, PF, or dorsi-plantar flexor (DPF) muscles in children with SCP, specifically, the functional mobility, balance, and maximum voluntary contraction (MVC), and compare the effects of strengthening these muscles individually or combined. This randomized trial was conducted between December 1st, 2018, and May 15th, 2019, at Gazi University, Department of Physiotherapy and Rehabilitation. Randomly assigned into groups were 27 independently ambulant patients with unilateral/bilateral SCP, where PFEs were applied to the DF, PF, or DPF muscles. Muscle tone, balance, and functional mobility were assessed. The MVC was evaluated by surface electromyography. PFEs were performed 4 times a week, for 6 weeks. The spasticity of the PF muscles decreased in all of the groups. PFE of the DF muscles led to an increase in ankle joint range of motion (ROM) and improved functional mobility (p < 0.05). PFE of the PF muscles resulted in improvements in balance and functional mobility (p < 0.05). PFE of the DPF muscles brought about improvements in balance but not in functional mobility (p < 0.05). No significant difference in the MVC was observed in any of the groups (p > 0.05). Gains are obtained according to the function of a muscle group. By training the DF muscles, it is possible to improve function and ROM. Furthermore, training the PF muscles led to improvements in balance and functional mobility, indicating that it is possible to bring about positive changes in spastic muscles. This study showed that muscle groups must be exercised according to the intended goal.

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

PurposeAssembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.Design/methodology/approachThis paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.FindingsThe experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.Originality/valueThis research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

Balancing mixed-model assembly lines for random sequences

Assembly lines are the standard choice for the manufacturing of large products and must combine efficiency with flexibility. Since multiple product variants are assembled on the same line, the variation in the processing time and the production sequence must be accounted for. This is especially hard in the planning phase of assembly lines when the demand is only forecast and the production sequence is not yet known. In this paper, an exact method and heuristic extensions are proposed to tackle the paced assembly-line balancing problem considering a random production sequence. Along with the assignment of tasks, the length of the stations is optimized in a Branch-and-Bound algorithm using Markov chains to evaluate the expected costs, which require the solution of a convex subproblem on each node. Product variants are modelled with a given probability, which can be either independent or correlated. Results of the model can be then further improved by optimizing the sequencing once the orders are known. The exact method can solve instances of small and medium-size containing up to 1012 product variants and the heuristic extensions can give good solutions with up to 1020 products. Results show that the optimization of station lengths is more important for the total cost than the difference between optimal and good task assignment solutions.

An Asymmetrical Active-Neutral-Point-Clamped Five-Level H-bridge Inverter for Open-Winding Motor Applications With Its Modulation Strategy

Asymmetrical active-neutral-point-clamped five-level H-bridge (ANPC5L-HB) inverter has reduced switch count and fewer high-frequency switches compared with other existing five-level H-bridge topologies. However, multiphase ANPC5L-HB inverter for open-winding motor applications is rarely researched. In this paper, a three-phase ANPC5L-HB inverter for open-winding motor applications is proposed. The modulation and neutral point (NP) voltage balance strategies for the inverter are also proposed. By using the proposed modulation strategy, transitions between different switching states are constrained to avoid unexpected intermediate states. Meanwhile, zero voltage switching (ZVS) of low-frequency switches could be achieved. For the NP voltage balance problem, optimal switching states are selected by using the MPC approach, and a better NP control performance is realized compared with the classical method. The effectiveness and high efficiency of the inverter are validated by a 25kW experimental prototype. And a peak efficiency of 98.685% is confirmed under 10kHz switching frequency. The good performances of the proposed strategies have been fully verified.

The Mechanism of Optimal Development of the Regional Spatial and Sectoral Structure in the Conditions of Transition to the Digital Economy

In article approaches to justification of the mechanism of optimum development of regional spatial-industry structure in the conditions of transition to digital economy are generalized theoretical-methodological. In the conditions of the next multidimensional crisis of a world economic system caused by competitive wars and local wars with use of lethal weapon the frailty of the existing economic theories at their checking is shown by order and chaos. It indicates the need of application of the basic theory for economy for which the existing economic theories are the first and second derivatives with the specific set of the principles of development. As such basic theory the chaotic theory of inflation of economic space (CTIES) as an analog of the chaotic theory of inflation of the Universe which is the basic theory in a modern general scientific picture of the world can serve in economy. The author offers based on CTIES use of the principles of sustainable development as the transitional stage to a noonomy, at the same time needs the solution of problems of balancing of developments on the chosen parameters by means of application of a triad of matrixes paradigmatic, management and matrixes of technological effectiveness within trinities «society – ​economy – ​ecology», «organization – ​motivation – ​informatization» and «labor instrument – ​objects of the labor – ​work» respectively. The most acceptable methodology for this purpose is the methodology based on the geometrical theory of optimal management. Such approach assumes work with Big Data, Data Science, Data mining, Machine learning, etc. characterizing transition to digital economy and, respectively, to researches on the basis of the Fourth paradigm of science (Science 4.0, E-science).

Comparison of MILP and CP models for balancing partially automated assembly lines

The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved.

Big Data Processing over Cloud Computing Environment

The topics of this special paper are mainly devoted to the most recent research, development and applications in the field of cloud computing, big data processing in the various fields, e.g. Web blog, search engine, image processing, and industry. Among all of the submitted manuscripts, 15 papers were selected and included in this special paper. Creative thoughts and interesting inspirations are presented, discussed, and disseminated in this paper. First, there are three papers on the cloud architecture and optimization for big data processing. J. Peng, et al. discussed the problems of load balance for massive data processing on cloud considering the data processing efficiency and nodes’ capability. A load balance approach for massive data, based on the consistent hashing method, was presented to improve the data processing equalization and high processing efficiency. Large-scale sensor networks and internet of things are one of the most important application fields in big data. Y. Sun, et al. proposed a cloud computing system to support PaaS in energy power applications—PROXZONE. PROXZONE provides the cloud services uniform monitoring and authorization, and cloud message service, which can support good performance when dealing with big data. Meanwhile, S. Xu, et al. discussed an improved cooperative dynamic cluster model in multitasks mobile network, and proposed a cooperative message forwarding mechanism to improve the network performance.

Optimal design for efficiency enhanced hierarchical cell-to-cell equalization systems based on centralized model predictive control

Effective topology is particularly significant for enabling high-efficiency hierarchical cell-to-cell (CC) equalization systems for large-scale batteries. This paper proposes an efficiency-enhanced hierarchical battery equalization systems (BESs) by formulating a topology graph-balancing connectivity evaluation method. As a performance evaluation index, connectivity takes both balance time and energy loss into consideration, which can be measured by the second small eigenvalue of the Laplacian matrix of the topology graph. Then, a centralized model predictive control (MPC) balancing strategy is formulated and integrated with hierarchical topologies to tackle optimal residual available energy (RAE) balancing problems. Making use of simulation tests of hierarchical BESs with different configurations, results show that the balancing performance and measured connectivity are consistent, and the balancing connectivity gradually rises and then decreases with an increasing number of module groupings. The optimal hierarchical BES with 96-series cells can achieve a low balancing time of 229 s and an energy loss of 0.67%. The balancing time and energy loss of centralized MPC can be reduced by 80.28% and 34.02%, respectively, relative to decentralized MPC balancing. For practical implementation consideration, dynamic balancing simulation and hardware-in-the-loop (HIL) experiments are conducted, and the experimental results are well matched with the simulation.

An uncertain multi-objective model for simultaneous design of supply chain and assemblers considering sustainable development: a case study

Supply chain network design (SCND) problems are used to formulate and make strategic decisions on the number of facilities, network facility location and plant capacity, to ensure the optimal delivery of products from manufacturers to end consumers. On the other hand, assembly line balancing (ALB) problems provide details on operational decisions which encompass production levels and inventories and therefore should be flexible enough to react when changes in resource availability and product demands occur. Integration of the SCND and ALB problems has thus been explored to develop a more holistic model and to further improve the efficiency of the supply chain. This study develops a novel SCND-ALB optimization model which considers economic, environmental, and social objectives in determining the optimal design and operational conditions of the supply chain under flexible cycle time of the assembly lines. Furthermore, for the first time a zigzag type belief degree model is integrated to address system uncertainties of the SCND-ALB problem. Two multi-objective solution approaches are then considered to simultaneously account for the three sustainability objectives. A case study on the production of a nozzle is explored to demonstrate the capabilities of the model.

Evaluation of cerebellum volume and trunk oscillation velocity in cases with adolescent idiopathic scoliosis: a preliminary report.

It has been suggested that the cause of the balance disorder seen in adolescent idiopathic scoliosis (AIS) originates from the central nervous system. However, the extent of the balance problem and the dysfunction of which part of the central nervous system has not been investigated in detail. This study aimed to correlate the values obtained by balance analysis and cerebellum volume measurement in female individuals with AIS with healthy individuals. Cerebellum volume was calculated via the cloud-based software " https://volbrain.upv.es " using brain magnetic resonance images of 27 healthy and 26 individuals with AIS. The duration of stay in the test positions, the movement strategy used during this time and the amount of postural sway were analyzed by using a computer-assisted force platform and compared statistically. Significant differences were found between the AIS and control groups in cerebellum total volume, vermis cerebelli volume (cm3), and trunk oscillation velocity (mm/s) parameters (p < 0.05). Cerebellum and vermis cerebelli volumes were found to be lower and trunk oscillation velocity was found to be greater in patients with AIS. Balance problems in patients with AIS are correlated with decreased cerebellum volume and increased trunk oscillation velocity.

Integration of the methods of algebra of algorithms and computational intelligence for automation of program systems design

An approach to the creation of intelligent systems is considered, which combines the ideas of Glushkov’s algebra of algorithms with the well-known neuroevolutionary algorithms of computational intelligence, which can be used to automate the design and synthesis of programs. The method of neuroevolution of augmenting topologies is intended to reduce the dimensionality of the space for searching for neural network parameters in the form of gradual development of its structure in the process of evolution. A software toolkit has been developed to support the approach, which is based on the construction of high-level specifications of algorithms represented in systems of algorithmic algebras, and generation of corresponding programs based on implementation templates in a target programming language. Parameterized algorithms called hyperschemes are used to solve a certain class of problems. Setting specific values of parameters and subsequent interpretation of hyperschemes allows obtaining algorithms adapted to specific application conditions. The developed program design and synthesis toolkit provides step-by-step development of programs, starting from a high-level algebraic-algorithmic specification. At the output of the toolkit, a program is automatically generated in one of the target programming languages (С, С++, Java, Python), that also include a subject-oriented language for designing neural networks. The mentioned language includes operators and conditions for working with populations, configurations, genomes, and fitness function. The work of the toolkit is illustrated by the example of designing a parameterized evaluation algorithm for a binary multiplexer and generation of a program for the single-pole balancing problem