Search

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has convinced many industries to shift towards semi-automated assembly lines with human-robot collaboration (HRC). In the HRC environment, robot agility can support human skill upon efficiently balancing tasks among the stations and operators. On the other hand, the robot energy consumption in today's energy crisis area demands that tasks be performed with as little energy utilization as possible by robots. In this context, the cycle time (CT) and total energy cost (TEC) of robots are among two conflicting objectives. Thus, this study balances HRC lines where a trade-off between CT and TEC of robots is sought. A mixed-integer linear programming model is proposed to formulate the problem. In addition, a multi-objective optimization approach based on ε-constraint is developed to address a case study from the automotive industry and a set of generated test problems. The computational results show that promising Pareto solutions in terms of CT and TEC can be obtained using the proposed approach. [ABSTRACT FROM AUTHOR]

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today's mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered. • DHM is incorporated for musculoskeletal risk assessment. • The MMALBP with musculoskeletal risk is formulated as a MILP model. • An enhanced multi-objective optimization algorithm is developed. • Analysis of results conducted on a case study and standard test problems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the automotive industry, decentralized in-house logistic areas called supermarkets are used for parts feeding to mixed-model assembly lines. In the literature, generally, the number of supermarkets and their locations are determined according to a previously balanced assembly line. As a result, supermarket locations have to be completely dependent on the line balance. There may be more than one solution that gives the same performance criterion value in the line balancing. Among these alternative solutions, it is more reasonable to use a solution that provides lower logistics costs than others. For this purpose, in this study, the mixed-model assembly line balancing problem and the supermarket location problem are considered simultaneously. Accordingly, in order to minimize the total costs of the assembly line and supermarkets, a mixed-integer mathematical model is developed, and the developed model is solved by using constraint programming (CP). In addition, a new approach based on Ant Colony Optimization (ACO) and Simulated Annealing (SA) is presented for large-sized problems. An illustrative example problem is solved using both the developed model and the proposed algorithm. The effectiveness of the proposed approach is tested through a new data set of test problems presented to the literature for a computational experiment. The experimental results show that the total costs are reduced meaningfully and a more realistic and applicable structure is achieved by the proposed approach. [ABSTRACT FROM AUTHOR]

The ever-shorter time to market demanded by customers and an increasing number of variants lead to high-frequency product development and production system planning cycles. This article therefore presents an approach that aims at increasing efficiency in the repetitive planning of variant-specific assembly systems. Compared to the state-of-the-art manual, document-based planning prevailing in industry, formal optimization methods have the potential of considering multiple optimization criteria. This article therefore proposes an approach for assigning individual process steps to stations for a given customer cycle time while minimizing the number of stations as well as optimizing additional criteria such as increasing the production system flexibility, achieving good ergonomics or reducing the deviation within provided tolerances. The solution space of the optimization is constrained by the mathematical modeling of a limited number of stations. The operations research (OR) model developed for this purpose considers given assembly precedence graphs as an input and includes variables, parameters, objective function and constraints. Especially the latter considers the process precedence relations in terms of the Assembly Line Balancing (ALB) problem as well as a divisibility of process steps. The approach is applied to the variant-specific planning of a highly automated welding assembly in the automotive supplier industry as use case. The results show its applicability and potential in reducing planning time. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, assembly processes involve most of the workforce to deal with the many manual operations. Thus, the design of workplaces must take into account ergonomics to promote workers well-being and safeguard their health and safety, also enhancing productivity. The occupational ergonomic risk not only depends on the physical workload of a task, but also on environmental characteristics of the workplace, including noise, the assessment of which may contribute to prevent workers from possible health issues associated to hearing injuries. In this regard, the present study proposes a software tool based on a genetic algorithm for solving the mixed-model assembly line balancing problem with job rotation and collaborative robots to improve workers' ergonomics, for the evaluation of which noise exposure is also considered. In particular, the objectives of the problem concern economic aspects, which are taken into account through the optimization of the cost of the line, and ergonomics, which is pursued by reducing and smoothing both workers' energy expenditure and noise exposure for performing operations on the line. To test the effectiveness of the proposed approach, an industrial case study is finally discussed. [ABSTRACT FROM AUTHOR]

Car sequencing is a widely adopted approach to implicitly enhance the short-term balancing of mixed-model assembly lines in practical applications. In this research, we investigate this problem in the context of a real-world case taken from a truck final assembly line. Our study considers cross-ratio constraints that address limitations related to dependent features of the products. Additionally, to manage the distribution of violation errors in cases where predefined congestion level thresholds (soft rules) exist, we propose a piecewise linear penalty function. To tackle these considerations, we formulate a mathematical programming model aimed at minimizing the total weighted penalty incurred from violating both independent and dependent sequencing rules. As the problem falls into the NP-hard category, we develop a multi-start parallel local search algorithm based on multi-agent approach to efficiently solve it. The algorithm is augmented by incorporating a self-adaptive diversification mechanism and a novel intelligent search range detection heuristic. The proposed heuristic enables the searching agents to intelligently explore the solution space rather than relying on a purely random search. The numerical experiments are conducted using 30 real-world instances from three truck assembly lines to validate the quality of the proposed algorithm. The comparison of the results indicates that our algorithm outperforms state-of-the-art algorithms. Furthermore, computational experiments demonstrate the effectiveness of each proposed mechanism in enhancing the algorithm's performance. [ABSTRACT FROM AUTHOR]

Two-sided assembly lines are typically employed in the production of medium and large-sized products with the aim of reducing the length of the assembly line, enhancing assembly efficiency and consequently reducing the time required for product assembly. However, traditional Two-sided assembly lines lack effective resource scheduling management methods in production scheduling, which results in low productivity and high resource costs. In order to address this issue, we propose a new two-sided resource-constrained assembly line balancing problem (TRCLBP) model. The model takes the minimum number of workstations and the minimum assembly cost as its objective function and proposes an improved genetic algorithm (I-GA) to solve it. A three-layer chromosome initialization method is proposed for the assembly tasks and resource decisions, which effectively improves the diversity and quality of the initial population. Furthermore, the algorithm employs strategies such as matching crossover and redistributing variants to ensure rapid convergence of the populations and to prevent them from falling into local optimums. Finally, the efficacy of the model and algorithm proposed in this paper is validated through a comprehensive analysis of arithmetic case studies and enterprise engineering examples. This analysis reveals a reduction of approximately 18 % in the total cost of assembly. Furthermore, the model enables enterprises to make informed decisions regarding the optimal allocation of resources, thereby reducing production costs and improving the efficiency of assembly operations during periods of expansion. [ABSTRACT FROM AUTHOR]

This paper deals with an optimization problem, which arises when a new simple assembly line has to be designed subject to a fixed number of available workstations, cycle time constraint, and precedence relations between necessary assembly tasks. The studied problem consists in assigning a given set of tasks to workstations so as to find the most robust line configuration, which can withstand processing time uncertainty as much as possible. The line robustness is measured by a new indicator, called stability factor. In this work, the studied problem is proven to be strongly NP-hard, upper bounds are proposed, and the relation of the stability factor with another robustness indicator, known as stability radius , is investigated. A mixed-integer linear program (MILP) is proposed for maximizing the stability factor in the general case, and an alternative formulation is also derived when uncertainty originates in workstations only. Computational results are reported on a collection of instances derived from classic benchmark data used in the literature for the Simple Assembly Line Balancing Problem (SALBP). [ABSTRACT FROM AUTHOR]

This paper proposes an exact Constraint Programming (CP) method with an extensive focus on real-world constraints for the Multi-manned Assembly Line Balancing Problem with Assignment Restrictions (MALBP-AR). We perform an in-depth literature review to gather examples from real assembly lines and organize the AR regarding tasks, stations, workers, and mounting positions. Our study classifies the AR related to transformed resources and provides a general and unified model. We explore the concept of variable workplaces to dynamically assign workers to mounting positions and aggregate no overlap restrictions to avoid interference between workers. The classic MALBP model is extended by gradually incrementing the number of restrictions. The model variant found in the literature is herein called Partial MALBP-AR. Compared to the previous state-of-the-art Tabu Search Algorithm (TSA) for this problem, the Partial MALBP-AR found twelve additional optimality proofs. Besides the relevant results regarding solution quality, the CP method also has a satisfactory CPU performance. We also propose an entirely new set of AR and test these practical conditions with the so-called Extended MALBP-AR. Such an extended model, which covers all the AR presented here, reached optimality within the computational time limit for 36 out of 38 instances. The worst-case gap for an open instance is 8.20%. The results show a trade-off between the number of deemed restrictions and the computational performance. However, considering the detailed set of AR, we can obtain more representative solutions regarding the final balancing implementation compared to theoretical cases. The method can be used to design experiments, turning certain constraints on and off and allowing managers to evaluate different resource allocation scenarios. • A MALBP with assignment restrictions related to transforming resources is presented. • Development of an exact and competitive Constraint Programming (CP) model. • Proposal of the most complete ARALBP model in the literature. • Twelve instances are improved compared to the current state-of-the-art. • The model allows experiments to be performed by (de)activating specific constraints. [ABSTRACT FROM AUTHOR]

Our work aims to investigate methods for solving the mixed-model assembly line balancing problem (MALBP) under uncertainty with the objective of minimizing the number of workstations. Specifically, we model task processing time as fuzzy stochastic variables (FRVs) due to the inherent uncertainties and variations in the manufacturing environment. Additionally, we introduce a ranking method for FRVs and propose a mathematical model to address MALBP. The recently developed Red Fox Optimization (RFO) algorithm is also discretized for the first time to support solving this problem. Finally, matheuristic algorithms combine a metaheuristic such as the popular Genetic Algorithm (GA), Particle Swarm Optimization (PSO), or the Discretized Red Fox Optimization (DRFO) algorithm with the Mixed-Integer Programming (MIP) model to generate the best solution in a reasonable time. Our comparative results demonstrate that the GA-MIP combination outperforms the others in both objective value and computational time. • We are the first to consider the Assembly Line Balancing Problem with fuzzy random processing time. • a new method to rank fuzzy stochastic processing times is introduced by comparing interval values. • The problem is formulated as chance-constrained programming model. • The recently developed Red Fox Optimization (RFO) algorithm is also discretized for the first time to support solution process. • A matheuristic combines a metaheuristic and the MIP model to search for better solutions for large-sized problems. • The metaheuristic algorithm to generate the starting solution for the MIP model to shorten finding the optimal solution. • Our results are nearly the same quality as the exact solution of the MIP model in a much shorter time for small-sized instances. MGA matheuristic yields significantly better performance than others in both terms of objective value and computational time for large-sized problems. [ABSTRACT FROM AUTHOR]

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP. • The problem considering both cycle time and trailer fuel consumption is formulated. • Several properties of the energy-efficient JRALB-FP are analyzed and provided. • A specific sampling mechanism and two local search operators are designed. [ABSTRACT FROM AUTHOR]

We study linear quadratic Gaussian (LQG) control design for linear port-Hamiltonian systems. To this end, we exploit the freedom in choosing the weighting matrices and propose a specific choice which leads to an LQG controller which is port-Hamiltonian and, thus, in particular stable and passive. Furthermore, we construct a reduced-order controller via balancing and subsequent truncation. This approach is closely related to classical LQG balanced truncation and shares a similar a priori error bound with respect to the gap metric. By exploiting the non-uniqueness of the Hamiltonian, we are able to determine an optimal pH representation of the full-order system in the sense that the error bound is minimized. In addition, we discuss consequences for pH-preserving balanced truncation model reduction which results in two different classical H ∞ -error bounds. Finally, we illustrate the theoretical findings by means of two numerical examples. [ABSTRACT FROM AUTHOR]

Mixed-model assembly lines are almost never perfectly balanced in industrial practice, nor can sequencing fully compensate imbalances. A promising approach to achieve both equal loads for operators and high throughput for manufacturers is dynamic line balancing. Besides the tasks fixed allocated to stations, some tasks can be shared between adjacent stations. Decisions are made on the tactical design regarding the identification of shared tasks and during the operation assigning shared tasks to stations for each job. This work provides a classification of the respective tactical and operational decision problems, classifies existing literature accordingly and derives an agenda for future research. [ABSTRACT FROM AUTHOR]

Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators' skills, particularly when a fewer number of humans and robots exist at the stations. [ABSTRACT FROM AUTHOR]

This paper presents an application of a simple assembly line balancing problem (SALB) in a lead-acid battery factory in Colombia. SALBP-1 was the selected approach to carry out the research. In this type of SALBP, there is a fixed cycle time, and the purpose is to minimize the number of workstations. To this aim, a process characterization was conducted, and a mixed-integer linear model was formulated. Then, an exact solution was found using GAMS® software. The main findings included reducing the number of workstations from 10 to 4. This change impacted the line efficiency and the idle time achieving a 150% and a 94% improvement, respectively. This research demonstrated the impact of dealing with SALBP on production companies over the line performance in terms of idle time minimization and productivity increase. [ABSTRACT FROM AUTHOR]

In assembly line balancing problems, time is balanced among workers. This does not necessarily balance their physical effort. The effort exerted by workers varies depending on the type of the job, and the size and weight of the component. In the two-sided assembly lines, commonly used for heavy industries, the variation in physical effort among workers is more emphasized. The unbalanced effort leads to fatigue, and may lower productivity and quality. This work aims at balancing the physical effort among workers in the two-sided assembly lines, while considering other conventional objectives. A genetic algorithm and a new decoding scheme are proposed. An effort smoothness index (ESI) is introduced to measure the variation in effort among workers. Results prove that balancing the physical effort is influential and can be achieved at no extra cost for most cases. Effort balancing is harder to achieve in difficult problems; of high order of strength. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel repair priority rule for spare parts in a repair station with limited repair capacity to minimize total inventory cost per time unit. Inventory cost is composed of holding costs and backorder costs. The proposed rule uses Remaining Useful Life (RUL) predictions of functioning machines obtained from a Prognostics and Health Monitoring (PHM) system. An inventory system comprising a finite number of machines, one warehouse, and one single-server repair station is considered. Numerical experiments were conducted to compare the performance of the proposed model with the performance of three existing priority rules: a first-come, first-served (FCFS) rule, a static priority rule, and a dynamic priority rule. A testbed with 20 instances of the problem was considered. The results showed that the proposed PHM-based rule consistently reduces the inventory system cost. [ABSTRACT FROM AUTHOR]

Robotic mixed-model two-sided assembly lines (RMTALs) have become increasingly prevalent in manufacturing industries for productivity enhancement. However, only limited attention has been paid to the RMTAL balancing problems that contain both setup times and multiple constraints such as positional constraints, zoning constraints, and synchronism constraints. Given the coexistence of these aspects in real-world scenarios, this study aims to address the RMTAL balancing problem with setup times and the mentioned constraints. To formulate the problem, a mixed integer programming model is proposed to optimize cycle time and total energy consumption. To handle multiple constraints and setup times, a new method based on four-vector encoding is introduced. This method addresses the three constraints during the encoding phase using four intervals and five rules, while setup times are processed during the decoding phase. To solve the problem, a Pareto entropy-based two-mode multi-objective simulated annealing algorithm is developed. The algorithm employs variable neighborhood descent algorithm with switchable objectives to generate the initial Pareto archive. Subsequently, it selects the initial solution with minimal potential in the Pareto archive, alternating between two search modes of exploration and exploitation based on the entropy difference of Pareto solutions. Comparative experiments with four state-of-the-art algorithms on benchmark instances of varying scales demonstrate that the proposed algorithm outperforms the others in over 93% of instances for the hypervolume ratio and inverted generational distance metrics. • A new MIP model is proposed for robotic mixed-model two-sided assembly line balancing. • A new method is presented to handle setup times and multiple constraints. • A Pareto entropy-based two-mode simulated annealing algorithm is developed to solve the model. • The effectivity of the proposed algorithm is verified by a set of test instances. [ABSTRACT FROM AUTHOR]

• Developing a new framework to determine ergonomic risks of assembly line tasks and optimize them. • Developing mixed-integer linear programming and constraint programming models for an assembly line balancing problem. • Balancing ergonomic risks of allocated tasks to stations besides minimizing the number of stations. • Developing a heuristic algorithm and comparing its performance with a constraint programming model and metaheuristics. Considering ergonomic factors in assembly line balancing problems can significantly improve human ergonomic conditions. In this study, a two-stage framework is developed to balance a mixed-model parallel U-shaped assembly line by considering ergonomic risks. In the first stage, according to various ergonomic standards and using the best-worst method and ELECTRE TRI, ergonomic risks of each task are identified and each task is classified into hard, normal, or easy class. In the second stage, a mathematical model of the problem is developed by considering a new objective function to level the number of tasks of each class between stations. This objective ensures that the tasks of each class are distributed equally among stations, resulting in having a roughly equal number of hard, normal, and easy tasks in each station. To solve the problem, a constraint programming model and a heuristic algorithm are developed. Numerical results show that the proposed objective function can level ergonomic risks well. The performance of the proposed solution approaches is examined against six different metaheuristic algorithms. Numerical results indicate the superiority of the constraint programming model for small and medium-size problems and that of the heuristic algorithm for large-size problems. [ABSTRACT FROM AUTHOR]

Assembly line balancing problems have been much investigated in the last decades. Most work affects static problems in which process times and resources are known in advance due to the complexity and diversity of such problems. However, the past has shown that static resource allocation for dynamic process scheduling is insufficient due to volatile market demands required by the reconfigurable production paradigm. Consequent change of resource selection by production requests affect feasible assembly lines. This paper introduces a mixed integer programming based linear optimization approach for solving the dynamic resource allocation for assembly lines modelled as Assembly Line Balancing Problem. [ABSTRACT FROM AUTHOR]

The Fish School Search (FSS) algorithm is a heuristic technique for finding globally optimal solutions. This algorithm is characterized by its simplicity in implementation, and high performance. Since the first mention of FSS, this effective optimization algorithm has been of a great interest among researches and practitioners around the globe. Modifications of FSS exist, applied to solve practical problems, including image reconstruction in electrical impedance tomography, finding optimal solutions in assembly line balancing problems, neural network structure optimization. In this paper, we consider a modification of the FSS algorithm, which uses chaos theory to generate uniformly distributed pseudorandom numbers, and incorporates exponential step decay. The described modified optimization algorithm is known as ETFSS, and is characterized by faster convergence speed and better performance. In order to further investigate the performance of the novel optimization algorithm, we apply ETFSS to neural network loss function optimization. In addition, we compare the described approach with other machine learning techniques, such as the support vector machine (SVM) algorithm, k-nearest neighbors (KNN) algorithm and back propagation-based neural network, trained using the adaptive moment estimation (Adam) optimizer. We visualize classification results using T-distributed stochastic neighbor embedding (TSNE) method, and uniform manifold approximation and projection (UMAP) method, in order to provide more details considering classification performance and dataset shape. The obtained results confirm, that ETFSS can produce slightly more accurate classifications when compared to backpropagation. [ABSTRACT FROM AUTHOR]

Fog computing has been widely deployed in intelligent production lines to provide real-time computing services for terminal devices to alleviate the transmission problem between cloud and terminal. Nevertheless, when there are many services provided in a single fog node, how to choose an optimal data processing path is a challenge to minimize optimization latency and power consumption while ensuring the reliable transmission of data with different priorities. To address the problems as mentioned above in this paper, we first propose a joint evaluation model of time latency and power consumption based on task priority. Then, we formulate a fog computing adaptive scheduling algorithm (FCAS) based on dynamic programming to obtain the optimal path for processing data with different priorities at fog nodes. Experimental results illustrate that our proposed evaluation model and algorithm have lower power consumption, high efficiency, and higher reliability. [ABSTRACT FROM AUTHOR]

The design process of flow-oriented assembly systems is characterized by being both highly complex and time consuming. Especially those design processes categorized into robotic and multi variant encountered in the automotive body-in-white production stages. Unlike established manual and template-based assembly system design models, which are currently applied in industry, the here presented novel approach uses a knowledge-based search algorithm and automatically generates optimal assembly system configurations. The algorithm has been implemented in a software prototype and the results have been validated against different large-size industrial scenarios in the automotive field of body-in-white production. [ABSTRACT FROM AUTHOR]

The problem of optimization assembly lines for rarely repeatable products with low volumes will be addressed in this paper. In this kind of production each product should be treated as a project with different cycle time on each station. Balance of assembly lines processing separate projects is impacted by uncertainty, assembly methods are not fully automated. Daily operations in this kind of activity needed an effective and fast method to support decision making by assembly line managers and production planners. For this purpose Analytical Hierarchy Method (AHP) and TOPSIS methods are used. The empirical study presents four possible optimization configurations for comparison and evaluation using the AHP and TOPSIS methods. [ABSTRACT FROM AUTHOR]

This article aims to minimize cycle time for a simple assembly line balancing problem type 2 by presenting a variable neighborhood strategy adaptive search method (VaNSAS) in a case study of the garment industry considering the number and types of machines used in each workstation in a simple assembly line balancing problem type 2 (SALBP-2M). The variable neighborhood strategy adaptive search method (VaNSAS) is a new method that includes five main steps, which are (1) generate a set of tracks, (2) make all tracks operate in a specified black box, (3)operate the black box, (4) update the track, and (5) repeat the second to fourth steps until the termination condition is met. The proposed methods have been tested with two groups of test instances, which are datasets of (1) SALBP-2 and (2) SALBP-2M. The computational results show that the proposed methods outperform the best existing solution found by the LINGO modeling program. Therefore, the VaNSAS method provides a better solution and features a much lower computational time. [ABSTRACT FROM AUTHOR]

The multi-man concept is usually applied for assembly lines of complex large sized products incorporating large number of tasks with varying sized components. Most researchers consider multi-manned assembly line problem under the objective of minimizing number of stations resulting in workers walking around the two sides of the station. The aim of the present work is to balance a two-sided multi-manned assembly line using a genetic algorithm combined with a proposed assignment heuristic. The model is studied under the objective of minimizing the number of workers and mated stations simultaneously, with additional space constraint. This helps to minimize workers interference and time waste in workers movement. For the first time a dynamic crossover and mutation ratio technique is used in the assembly line balancing problem. Applying the proposed algorithm gives competitive results. The use of dynamic crossover and mutation proved to yield better results compared to the stationary values. [ABSTRACT FROM AUTHOR]

Energy-saving, one of the most significant strategies for green manufacturing, has become the focus of more and more scholars and enterprise managers. Hence, this work addresses the minimization of energy and cost requirements on mixed-model multi-manned assembly line balancing with multi-skilled workers. A new mixed-integer linear programming model is proposed to define the problem. Additionally, a novel multi-objective co-evolutionary algorithm is designed to achieve the trade-off between energy and cost requirements. This algorithm includes a two-layer solution representation to achieve full coverage of the solution space and a new decoding mechanism with idle time reduction. A collaborative initialization as the first stage of the algorithm is extended to get high-quality and great-diversity initial solutions. A self-learning evolution for each sub-population with four problem-specific evolutionary operators is developed to explore task or worker assignment sequences, and a dual-cooperation strategy is proposed to enhance the interaction between sub-populations. The final experiments, based on 269 instances, demonstrate that the improvement components are effective and the proposed algorithm is superior to seven latest multi-objective evolutionary algorithms from numerical, statistical and differential analyses. • Define the MALBP-MW by a MILP model to minimize the energy and cost requirements. • Design a new decoding with idle time reduction and a collaborative initialization. • Propose four problem-specific evolutionary operators for MOCEA. • Develop MOCEA with a self-learning selection and dual-cooperation. [ABSTRACT FROM AUTHOR]

Ergonomics is an essential aspect to deal with while solving problems related to manual assembly, for the effects it may have on both productivity and human factor related issues. The frequent execution of repetitive movements and the handling of heavy components are among the main factors that characterize the assembly process, potentially resulting in worker's overload. An appropriate distribution of assembly operations and, therefore of relative workloads, on a production line can improve ergonomic aspects, according to the worker's anthropometric and physiological characteristics. In this paper, a multi-objective genetic algorithm for solving the assembly line balancing problem taking into account ergonomics based on energy expenditure is proposed. The novelty of the contribution relies in the assignment of assembly tasks to workstations considering a set of human operators actually available in a company. The assignment of workers is based on their physical capabilities and limits, evaluated according to their anthropometric and physiological characteristics. The objectives of the problem, besides the minimization of the number of workstations, are related to the human factor; in particular, the distribution of assembly tasks to workstations according to worker technical skills and to worker physical capabilities are optimized. A practical case study taken from the industrial reality is finally tested and discussed. [ABSTRACT FROM AUTHOR]

In this study, we present two new mathematical models for mixed-model assembly line balancing. The line is semi-automated and characterized by various alternative formats for the operations performed, which range from human operators and assisting robots to robotics solutions, where the operators may have different skill levels and operators on adjacent stations may cooperate. Given the cycle time, the first model minimizes the fixed and variable costs associated with setting up the assembly line. However, the second mathematical model involves a cycle time, which is variable and it changes between a lower bound and an upper bound. These bounds reflect the expected future changes in demand. The objective is to minimize both the costs and the cycle time. To obtain more accurate and reliable results, we present a robust solution for the different confidence levels (α) determined by managers. To verify the mathematical models, we considered different test problems and compared the robust solutions under three different conditions. The results showed that the robust modeling approach obtains a more reliable design than the traditional cost trade-off alone. The model allows decision makers to select assembly operations based on a better understanding of their decision impacts in both the short term and long term under conditions of demand uncertainty. [ABSTRACT FROM AUTHOR]

It is widely known that the assembly line balancing problem (ALBP) is an NP-hard optimization problem. Although different meta-heuristics have been proposed for solving this problem so far, there is no convincing support that what type of algorithms can perform more efficiently than the others. Thus, using some statistical measures, the landscape of the simple ALBP is studied for the first time in the literature. The results indicate a flat landscape for the problem where the local optima are uniformly scattered over the search space. Accordingly, the efficiency of population-based algorithms in addressing the considered problem is statistically validated. [ABSTRACT FROM AUTHOR]

Highlight • A single product robotic assembly line balancing problem is addressed. • Sequence-dependent robot setup times are considered. • The objective is the minimization of the cycle time. • Metaheuristic algorithms involving migrating birds optimization are proposed. • Comparisons indicate the superiority of the migrating birds optimization. Abstract Industries are incorporating robots into assembly lines due to their greater flexibility and reduced costs. Most of the reported studies did not consider scheduling of tasks or the sequence-dependent setup times in an assembly line, which cannot be neglected in a real-world scenario. This paper presents a study on robotic assembly line balancing, with the aim of minimizing cycle time by considering sequence-dependent setup times. A mathematical model for the problem is formulated and CPLEX solver is utilized to solve small-sized problems. A recently developed metaheuristic Migrating Birds Optimization (MBO) algorithm and set of metaheuristics have been implemented to solve the problem. Three different scenarios are tested (with no setup time, and low and high setup times). The comparative experimental study demonstrates that the performance of the MBO algorithm is superior for the tested datasets. The outcomes of this study can help production managers improve their production system in order to perform the assembly tasks with high levels of efficiency and quality. [ABSTRACT FROM AUTHOR]

Human-robot collaboration represents a significant evolutionary step in manufacturing. A crucial point is to establish a proper task assignment to combine robot productivity with human flexibility. In this regard, this paper proposes a genetic algorithm to approach the Assembly Line Balancing Problem (ALBP) in the case of human-robot collaborative work. The aim is the minimization of: i) the assembly line cost, evaluated according to the number of workers and equipment on the line, including collaborative robots, ii) the number of skilled workers on the line, iii) the energy load variance among workers, based on their energy expenditures and thus on their physical capabilities and on the level of collaboration with robots. [ABSTRACT FROM AUTHOR]

The semiautomated assembly line balancing problem (SAALBP) is a complex planning problem in which the production process comprises human workers, robots, and human-robot collaboration. With increasing awareness of sustainable practices in the manufacturing environment, this study addresses a sustainable SSALBP, where a mixed integer linear programming (MILP) model is formulated for the first time for this type of assembly systems. The model consists of three objectives and each of the objectives represents the three pillars of sustainability. The problem becomes more realistic by considering uncertainty in processing time and the influence of human workers' age and skill in processing time. The uncertainty is adopted in the MILP model by using a chance-constraint programing approach. Since the problem is complex, Q-learning and Monte-Carlo simulation-assisted based genetic algorithm (GA) based memetic algorithm (MA) are proposed to solve the problem. The performance of MA is evaluated against the well-known non-dominated sorting GA-III (NSGA-III) and state-of-the-art algorithms for solving the problem. The experimental results show that MA surpasses its contest significantly in finding better Pareto-fronts. [ABSTRACT FROM AUTHOR]

Multi-energy industrial parks, composed of the district energy supply system and terminal industrial loads, are dominant energy consumers with over 50% occupation of total energy consumption. Considering the energy conversion in the district energy supply system and adjustment of production subtasks in terminal industrial loads, the industrial parks could provide considerable flexibility for utility multi-energy systems to deal with contingencies. However, industrial parks may have multiple uncertainties originating from the production subtasks' variability, distributed renewable energy's fluctuation, and random failures of equipment. These uncertainties could make the flexibility assessment of industrial parks unreliable, which may further exacerbate utility system risks. To address the problem, this paper innovatively proposes the concept of probabilistic integrated flexible regions and corresponding characterization approaches, which can effectively describe the credible multi-energy adjustment ability of industrial parks. First, the energy-material flows in the industrial park are modeled considering multiple uncertainties while subject to constraints such as operating limits of equipment, energy and material balancing, and production targets. Then, the modeling and calculation methods of probabilistic integrated flexible regions are given. These regions are labeled with certain probability requirements and visualized in the input multi-energy space. Using a real-world test system that produces air conditioning equipment in China, the results show that the proposed model can effectively reflect the probability characteristics of the integrated flexibility under multiple uncertainties. For this test system, the area of a highly credible integrated flexibility, represented by the probabilistic integrated flexible region with 99% probability, is about half of that obtained by the deterministic model. That is, half of the integrated flexibility evaluated by the deterministic method in the test system may not be practically utilized considering the impacts of uncertainties. Therefore, the proposed method could provide a credible and comprehensive evaluation of the multi-energy industrial park's flexibility considering multiple uncertainties. • Exploiting the multi-energy(integrated) flexibility of industrial parks • Describing the probability characteristics of the integrated flexibility • Uncertainties in industrial parks shrink their integrated flexibility • Facilitating the interaction between demand and utility side in energy systems [ABSTRACT FROM AUTHOR]

Research on the assembly line balancing problem has been continuously incorporated the characteristics of actual production lines without considering the mixed-model two-sided assembly line with unpaced synchronous transfer, which is common in body shops of cars or trucks. This study attempts to model a mixed-model two-sided assembly line balancing and sequencing problem with unpaced synchronous transfer, and designs a genetic algorithm to solve this problem. First, to describe the process whereby model sequence and its execution, station and its number, and the process time caused by task allocation lead to the variable output of an unpaced synchronous line, a model without an observation point restriction is proposed. Based on this, a mathematical model of a balancing and sequencing problem of a mixed-model two-sided assembly line with an unpaced synchronous transfer is established, in which the balancing problem is aimed at Type-II. Then, an improved genetic algorithm adapted to the complexity of the problem is proposed. The algorithm designs a combination and evaluation mechanism to ensure that the solution populations of the balancing and sequencing problems are sufficiently combined and evaluated in each iteration to improve efficiency. The proposed algorithm is illustrated using a small numerical example. Finally, the algorithm is experimentally investigated using a set of test cases and several comparison algorithms. The experimental results show that the combination and evaluation mechanism is suitable for these problems, especially for large-sized cases, and that the proposed algorithm is effective and has better performance than the comparison algorithms. • To the best of our knowledge, this is the first study of mixed-model two-sided assembly line balancing problem with unpaced synchronous transfer. • A model without observation point restriction is proposed for the variable output of unpaced synchronous line. • A new genetic algorithm adapted to the complexity of the problem is proposed. • Extensive experiments are carried out to illustrate the effectiveness and good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to deal with assembly line design from both line balancing and parts feeding (PF) aspects as two-interrelated decision problems while supermarkets are used. These problems arise in the real-world assembly lines (ALs) where decision makers are planning to simultaneously determine the optimal number of stations and the optimal number of supermarkets so that the total installation costs of ALs including line balancing and PF costs are minimized. To this purpose an integrated mathematical model is proposed and its performance is tested through solving a number of benchmark problems and a real case taken from industry. [ABSTRACT FROM AUTHOR]

In this study, a new pull production control strategy called Basestock-Constant Work-in-Process (B-CONWIP) is proposed. It is used to control the flow of materials and information in balanced assembly production systems. This proposed control strategy uses one type of authorization cards called CONWIP card that limits the work-in-process (WIP) in the whole system. It has been applied in a single-product and a mixed-product assembly system balanced by two efficient Genetic algorithms introduced in literature. The performance of this control strategy is compared with another pull production control strategy called Basestock Kanban CONWIP (BK-CONWIP), which is a very promising production control strategy found in literature. The proposed strategy has two control parameters, CONWIP authorization cards and basestock levels while BK-CONWIP has three control parameters Kanban authorization cards, CONWIP authorization cards and basestock levels. The comparison is based on three performance measures average system WIP, percentage of satisfied customer demand (service level) and WIP variation between workstations. The performance of the proposed strategy B-CONWIP and BK-CONWIP is mainly similar in both types of assembly systems when mean demand rates are low with respect to mean service rates with the proposed strategy being easier to control and optimize. On the other hand, when mean demand rates are high with respect to mean service rates; B-CONWIP is preferable if service level is more important, while BK-CONWIP is preferable if WIP level is more important. Regarding WIP variation, it mainly depends on the efficiency of the balancing approach. The more efficient the balancing approach, the less WIP variation. Treating demand as lost instead of backordered results in decreased average system WIP and does not affect service levels in both PCSs. It is also shown that S-KDP is more flexible in dealing with situations of variable product mixes than d -KDP because control parameters can be used by any product which minimizes the effect of the unbalanced systems. [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]

In a robotic assembly line, a series of stations are arranged along a conveyor belt and a robot performs on tasks at each station. Parallel assembly lines can provide improving line balance, productivity and so on. Combining robotic and parallel assembly lines ensure increasing flexibility of system, capacity and decreasing breakdown sensitivity. Although aforementioned benefits, balancing of robotic parallel assembly lines is lacking – to the best knowledge of the authors- in the literature. Therefore, a mathematical model is proposed to define/solve the problem and also iterative beam search (IBS), best search method based on IBS (BIBS) and cutting BIBS (CBIBS) algorithms are presented to solve the large-size problem due to the complexity of the problem. The algorithm also tested on the generated benchmark problems for robotic parallel assembly line balancing problem. The superior performances of the proposed algorithms are verified by using a statistical test. The results show that the algorithms are very competitive and promising tool for further researches in the literature. [ABSTRACT FROM AUTHOR]

To better reflect the uncertainty existing in the actual disassembly environment, the multi-objective disassembly line balancing problem with fuzzy disassembly times is investigated in this paper. First, a mathematical model of the multi-objective fuzzy disassembly line balancing problem (MFDLBP) is presented, in which task disassembly times are assumed as triangular fuzzy numbers (TFNs). Then a Pareto improved artificial fish swarm algorithm (IAFSA) is proposed to solve the problem. The proposed algorithm is inspired from the food searching behaviors of fish including prey, swarm and follow behaviors. An order crossover operator of the traditional genetic algorithm is employed in the prey stage. The Pareto optimal solutions filter mechanism is adopted to filter non-inferior solutions. The proposed model after the defuzzification is validated by the LINGO solver. And the validity and the superiority of the proposed algorithm are proved by comparing with a kind of hybrid discrete artificial bee colony (HDABC) algorithm using two test problems. Finally, the proposed algorithm is applied to a printer disassembly instance including 55 disassembly tasks, for which the computational results containing 12 non-inferior solutions further confirm the practicality of the proposed Pareto IAFSA in solving the MFDLBP. [ABSTRACT FROM AUTHOR]

This paper proposes a new genetic algorithm approach for solving a multi-objective assembly line balancing problem. The objectives concern the minimization of the number of workstations and the workload variance, typically faced by most systems presented in literature, but also the minimization of three further aspects, not simultaneously treated in literature and very important in manual assembly lines: the number of skilled workers among workstations, the number of assembly equipment (e.g.: automatic screwdrivers, pressing machines, etc.) and the number of assembly direction changes along the sequence. To demonstrate the effectiveness of the proposed method in finding optimized assembly sequences, a classical case study taken from the literature is finally discussed. [ABSTRACT FROM AUTHOR]

Combining Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) is now of increasing interest. The customary approach is the sequential approach, where ASP is optimised before ALB. Recently, interest in the integrated approach has begun to pick up. In an integrated approach, both ASP and ALB are optimised at the same time. Various claims have been made regarding the benefits of integrated optimisation compared with sequential optimisation, such as access to a larger search space that leads to better solution quality, reduced error rate in planning and expedited product time-to-market. These benefits are often cited but no existing work has substantiated the claimed benefits by publishing a quantitative comparison between sequential and integrated approaches. This paper therefore compares the sequential and integrated optimisation approaches for ASP and ALB using 51 test problems. This is done so that the behaviour of each approach in optimising ASP and ALB problems at different difficulty levels can be properly understood. An algorithm named Multi-Objective Discrete Particle Swarm Optimisation (MODPSO) is applied in both approaches. For ASP, the optimisation results indicate that the integrated approach is suitable to be used in small and medium-sized problems, according to the number of non-dominated solution and error ratio indicators. Meanwhile, the sequential approach converges more quickly in large-sized problems. For pure ALB, the integrated approach is preferable in all cases. When both ASP and ALB are considered, the integrated approach is superior to the sequential approach. [ABSTRACT FROM AUTHOR]

An assembly line system is a manufacturing process in which parts are added in sequence from workstation to workstation until the final assembly is produced. In a mixed-model assembly line balancing problem, tasks belonging to different product models, are allocated to workstations according to their processing times and precedence relationships amongst tasks. The research parlayed two features, learning effect and uncertain demand, into the conventional mixed-model assembly line balancing model, which is the main contribution of our paper. Both features can affect the new decision appeared in the stated problem — the level of production. The problem setup as well as the new decisions considered in the problem are novel. The proposed model optimized two objectives, total expected cost and average cycle time. To solve the model, a mixed integer-based heuristic and a customized variable neighborhood search method is proposed. The algorithms are examined for two different system response time requirements. Computational results showed that the mixed integer-based heuristic is more efficient if there is enough response time for the decision making process. On the contrary, the customized variable neighborhood search method can deliver promising results under real-time conditions. The Pareto-optimal set can be generated, which provides the managers with multiple choices for different cost and cycle time combinations. • An assembly line balancing problem under learning effect and uncertain demand. • Task assignment and production decisions for different product models. • A mixed integer-based heuristic and a variable neighborhood search method. [ABSTRACT FROM AUTHOR]

• RMALBSP considering PM scenarios promotes productivity and smoothness. • Mathematical model generate allocation and sequencing plans for scenarios. • Intra-population crossover facilitates the exchange of information. • Four neighborhood structures are designed to replace repetitive solutions. • Results indicate that MOCDE outperforms the other eight algorithms. Considering preventive maintenance scenarios (PMS) in assembly line balancing problems can significantly influence machine reliability and production continuity. However, no research considers PMS in robust mixed-model assembly line balancing and sequencing problems (RMALBSP) with interval processing times. This paper designs a robust mathematical model to solve RMALBSP considering PMS (RMALBSP_PMS) with two types of objectives: makespan and task alteration. Then, it is linearized by duality to be directly solved by Cplex. We also develop a multi-objective cooperative differential evolution algorithm (MOCDE) to solve large-scale instances. In this algorithm, we design a coevolutionary mechanism to partition the RMALBSP and optimize each part separately. The priority-based encoding and decoding methods are proposed to represent solutions in each population. Evolutionary operators for populations and the archive are designed to explore the solution space. Four neighbor search operators are developed to replace repetitive solutions. The first experimental result illustrates the model can obtain the Pareto solutions of small-scale instances, and the MOCDE is more suitable for solving large-scale instances. The remaining two experiments demonstrate that the designed coevolutionary mechanism and improvements are effective, and the MOCDE algorithm outperforms eight state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Croatia's manufacturing industry faces many problems and obstacles that have a large impact on its competitiveness. Insufficiently educated and unskilled personnel, particularly in the production and management fields, are decreasing competitiveness that is necessary for survival in the global market. Objective of project Innovative Smart Enterprise is to establish a special learning environment in one Laboratory asLean Learning Factory, i.e. simulation of a real factory through specialized equipment. The Lean Learning Factory's mission is to integrate needed knowledge into the engineering curriculum. Therefore, Lean Learning Factory at University of Split is in continuous developing process to support practice-based engineering curriculum with possibility of learning necessary tools and methods, using didactic games or real life products and equipment. Solution proposal for best balance between toys and real products consider design and production line development for product Karet. It is a traditional and original product from Croatia, so it will raise enthusiasmin learning process in both students and industry employees. Two assembly lines will be developed, one traditionally equipped and one intelligent, networked, flexible, and fully improved by Lean tools. By deeper analysis of both assembly lines, hybrid assembly lines could be designed, to balance on one side assembly tact time according to customer demand and total cost of installation and running on the other side. Methods and tools adapted and implemented, in both design and analysis process for optimization of this hybrid assembly line would be scaled and adjusted for industry use as part as knowledge transfer from university to enterprises. [ABSTRACT FROM AUTHOR]

Two-sided assembly lines are usually utilized to produce large-sized high-volume products. Recently robots are widely utilized in this line to replace the manual handling and manage the allocated tasks. For a robotic assembly line, the energy consumption is a major expense and the increased energy cost draws much more attentions from manufacturing enterprises. To the best knowledge of the authors, there is no research reported on the energy consumption of two-sided robotic assembly line. This paper presents a new mixed-integer programming model to minimize the energy consumption and cycle time simultaneously. A restarted simulated annealing algorithm is developed to deal with the complexity of the model, which utilizes new local search with three neighbor structures and restart phase based on the crowding distance assignment procedure to obtain well-spread Pareto-optimal set. Testing cases are designed to measure the performance of the proposed method and the restarted simulated annealing algorithm is compared with the fast elitist non-dominated sorting genetic algorithm. The computational results demonstrate that the proposed model is useful to reduce the total energy consumption and the restarted simulated annealing algorithm outperforms the non-dominated sorting genetic algorithm in both convergence and spread criteria. [ABSTRACT FROM AUTHOR]

Two-sided assembly line balancing (two-ALB) problems usually occur in plants that produce large high-volume products, such as buses, trucks, locomotives, and home products. In this study, we consider a two-ALB using a new approach that allows the parallel performance of tasks in a real-world locomotive production plant. The problem is formulated as a mixed-integer program and a new heuristic algorithm is proposed, which produces robust results as well as obtaining better quality solutions to some basic two-ALB benchmarks. In fact, the proposed algorithm can obtain optimal solutions for some small-scale problems and near-optimal solutions for some medium-scale problems. [ABSTRACT FROM AUTHOR]

The Assembly Line Balancing Problem (ALBP) is to determine the optimal allocation of assembly operations to a set of workstations, with respect to precedence constraints. This paper proposes a multi-objective optimization to solve the ALBP using a Genetic Algorithm (GA) approach. The aim is to minimize, besides the number of workstations, two aspects, very important from an economic point of view, but poorly treated in literature: the number of high skilled workers needed to correctly accomplish the operations and the number of assembly equipment along the line. A case study was finally discussed in order to demonstrate the capability of the proposed method in finding optimized solutions in different scenarios. [ABSTRACT FROM AUTHOR]

In this study, we propose an integrated heuristic approach for the fuzzy mixed-model assembly line balancing problem. The proposed approach considers bottleneck easing-based assignment of work and product mixture determination by using a fuzzy revised theory of constraints in a capacitated line to maximize the total throughput and minimize the cycle time for the maximum given number of stations. A bi-level fuzzy mathematical model is developed that considers the assignment of workers and tasks, where the task times depend on the skill levels of workers. Furthermore, various probes are used to verify the appropriate selection rules for workers and task assignments using different test problems. The results show that only considering assembly line balancing is not sufficient when capacity constraints are present. Thus, maximization of the available capacity is essential but this should not overload certain stations along the assembly line. The proposed approach is efficient for obtaining good results within a short time. [ABSTRACT FROM AUTHOR]