U-shaped Assembly Line Balancing Research Articles

Modeling and balancing of parallel U-shaped assembly line based on improved genetic algorithms

Aiming at the improvement of the efficiency of the assembly line in the intelligent manufacturing field, a parallel U-shaped assembly line balancing problem (PUALBP) appears. In this paper, a mathematical model of the PUALBP-I is established, and an improved genetic algorithm (IGA) is innovatively designed based on the allocation strategy. Combined with 56 classic examples, the IGA is used to solve the mathematical models of SUAL and PUAL respectively, and the balance results and the balance effect evaluation indicators are obtained. The comparison with Parallel U-line Heuristic (PUH) shows that the results of PUAL are better than SUAL, and verifies that the IGA is effective. The results demonstrate that the IGA in calculating large-scale problems is superior to the small-scale problems, which provides a useful reference for solving PUALBP.

An optimization model for sustainable multi-product multi-echelon supply chain networks with U-shaped assembly line balancing under uncertainty

This study presents an integrated supply chain network with suppliers, manufacturers, assemblers, and customers. The proposed model considers a U-shaped assembly line with three sustainability objective functions. We consider assumptions considering different types of raw materials, multiple products, location selection of manufacturers, location selection of assemblers, and capacity of suppliers. The problem is formulated non-linear and then linearized as a multi-objective model. Some cost and demand parameters are considered uncertain and are represented by fuzzy sets and theory. The proposed uncertain model is first converted to a multi-objective crisp model by applying the modified robust possibilistic programming approach. Then, the obtained crisp multi-objective model is solved by an interactive-fuzzy optimization approach in the literature. For computational study, some test problems are generated and solved using an original deterministic formulation and the crisp form of the uncertain formulation. The obtained results are analyzed and compared according to the objective function values. Finally, an extensive sensitivity analysis is performed on the parameters of the models.

Balancing of parallel U-shaped assembly lines with a heuristic algorithm based on bidirectional priority values

Aiming at improving assembly line efficiency and flexibility, a balancing method of parallel U-shaped assembly line system is proposed. Based on the improved product priority diagram, the bidirectional priority value formula is obtained. Then, assembly lines are partitioned into z-q partitions and workstations are defined. After that, the mathematical model of the parallel U-shaped assembly line balancing problem is established. A heuristic algorithm based on bidirectional priority values is used to solve explanatory examples and test examples. It can be seen from the results and the effect indicators of the assembly line balancing problem that the heuristic algorithm is suitable for large balancing problems. The proposed method has higher calculation accuracy and shorter calculation time. The balancing effect of the parallel U-shaped assembly line is better than that of single U-shaped assembly line, which verifies the superiority of the parallel U-type assembly line and effectiveness of the proposed method. It provides a theoretical and practical reference for parallel U-type assembly line balancing problem.

An integrated bi-objective U-shaped assembly line balancing and parts feeding problem: optimization model and exact solution method

In this study, an integrated bi-objective objective U-shaped assembly line balancing and parts feeding problem is explored by considering the heterogeneity inherent of workers. An optimization model is developed to formulate the addressed problem. Since the problem includes two different objectives, namely the minimizing the operational cost and maximum workload imbalance, the Pareto-optimal solutions are found by employing the second version of the augmented e-constrained (AUGMECON2) method. To investigate the impact of qualification of workers on the system performance, a set of scenarios is constructed based on the worker skill levels. Each scenario is determined based on the nature of the worker pool in which workers are assigned to the stations. The optimization model and implemented method are validated through data taken from water-meter and elevator producers. The computational results reveal that the scenarios have a great impact on system performance. In particular, it is revealed that as the skill levels of workers increases, the quality of the Pareto-optimal solutions increase by up to 30% in terms of the comparison metrics. Therefore, an order release mechanism and worker training activities are suggested to be performed to enhance system performance.

An enhanced multi-objective JAYA algorithm for U-shaped assembly line balancing considering preventive maintenance scenarios

Given that a U-shaped assembly line is usually regarded as a serial flow production system, preventive maintenance (PM) on machines in any station of the line may lead to stoppage of the whole line and sequentially cause great loss of production cost. To improve production continuity during PM, this paper proposes multiple alternative assignment plans with interchangeable abilities to ascertain the production recovery from changes of line structure. A mixed-integer mathematical model is further formulated to make decisions on alternative assignment plans. In this model, cycle time and total assignment plan alteration cost are minimised simultaneously. And an enhanced JAYA algorithm is developed to obtain well-distributed Pareto frontier solutions effectively and efficiently. The proposed algorithm suggests a similarity-based selection and various crossover/neighbourhood search operators to enhance its search ability for global optima. Numerical experiment results prove the effectiveness of the proposed model and enhanced JAYA. This novel methodology of integration optimisation bridges assembly line balancing and PM, and thus promotes production continuity and achieves economic benefits under maintenance within large-scaled assembly lines in real situations. Furthermore, a real-world case study is conducted to illustrate the significance of this novel methodology.

Enhanced beam search heuristic for U-shaped assembly line balancing problems

U-shaped assembly lines have drawn increasing attention from the modern assembly line industry owing to their high flexibility. This work develops an enhanced beam search heuristic algorithm to solve types 1 and 2 U-shaped assembly line balancing problems (UALBP-1 and UALBP-2). The proposed beam search heuristic is enhanced by utilizing: (1) five lower bounds and four dominance rules to prune the dominated partial solutions; (2) a new station load selection criterion to select station load with a smaller lower bound, less idle time and fewer ‘easy’ tasks; and (3) a new method to extend partial solutions based on heuristic information to encourage full station load. Extensive numerical study is carried out to test and evaluate the performance of the proposed algorithm. Computational results demonstrate that the proposed method outperforms the state-of-the-art methods for both UALBP-1 and UALBP-2 by achieving 33 and 31 new optimal solutions for UALBP-1 and UALBP-2, respectively.

Robust optimization for U-shaped assembly line worker assignment and balancing problem with uncertain task times

Awareness of the importance of U-shaped assembly line balancing problems is all on the rise. In the U-shaped assembly line, balancing is affected by the uncertainty associated with the assembly task times. Therefore, it is crucial to develop an approach to respond to the uncertainty caused by the task times. When the great majority of existing literature related to uncertainty in the assembly line is considered, it is observed that the U-shaped assembly line balancing problem under uncertainty is scarcely investigated. That being the case, we aim to fill this research gap by proposing a robust counterpart formulation for the addressed problem. In this study, a robust optimization model is developed for the U-shaped assembly line worker assignment and balancing problem (UALWABP) to cope with the task time uncertainty characterized by a combined interval and polyhedral uncertainty set. A real case study is conducted through data from a company producing water meters.

Type-1 U-shaped Assembly Line Balancing under uncertain task time

Abstract Recently, assembly line balancing problem with uncertain task time gains more and more attention in the literature. Task time uncertainty may overload workstations. Uncertain task time attributes were studied in the frameworks of the probability theory. In this paper, we use a new method, which is the uncertainty theory, to model the uncertain task time as the historical task time information is unavailable. We incorporate the uncertainty into the constraints of the type-1 U-shaped assembly line balancing problem. We derive some useful theorems related to the optimal solutions. Further, we develop an algorithm based on the branch and bound remember algorithm to solve the proposed problem. Finally, numerical studies are conducted to illustrate our model.

U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

The objective of this research is to develop metaheuristic methods by using the differential evolution (DE) algorithm for solving the U-shaped assembly line balancing problem Type 1 (UALBP-1). The proposed DE algorithm is applied for balancing the lines (manufacturing a single product within a fixed given cycle time), where the aim is to minimize the number of workstations. After establishing the method, the results from previous research studies were compared with the results from this study. For the UALBP, two groups of benchmark problems were used for the experiments: (1) For the medium-sized UALBP (21–45 tasks), it was found that the DE algorithm DE/best/2 to Exponential Crossover 1 produced better solutions when compared to the other metaheuristic methods: it could generate 25 optimal solutions from a total of 25 instances, and the average time used for the calculation was 0.10 seconds/instance; (2) for the large-scale UALBP (75–297 tasks), it was found that the basic DE algorithm and improved differential evolution algorithm generated better solutions, and DE/best/2 to Exponential Crossover 1 generated the optimal solutions and achieved the minimum solution search time when compared to the other metaheuristic methods: it could generate 36 optimal solutions from a total of 62 instances, and the average time used for the calculation was 4.88 seconds/instance. From the comparison of the DE algorithms, it was found that the improved differential evolution algorithm generated optimal solutions with a better solution search time than the search time of the basic differential evolution algorithm. The basic and improved DE algorithm are the effective methods for balancing UALBP-1 when compared to the other metaheuristic methods.

A novel meta-heuristic approach to solve fuzzy multi-objective straight and U-shaped assembly line balancing problems

The consideration of this study is devoted to deal with the straight and U-shaped assembly line balancing problems (ALBPs). The ALBP involves allocation of required tasks to a set of workstations, so that objective functions being optimized are subjected to set of constraint. While many efforts have been dedicated in the literature to develop deterministic model of the assembly line, the attention is not considerably paid to those in uncertain circumstances. In this paper, along with proposing a novel fuzzy model for ALBP, triangular fuzzy numbers are deployed with to respect vagueness and uncertainty subjected to the task processing times. For this purpose, two conflicting objectives are considered simultaneously with regard to set of constraints, so that the efficiency of the line has to be maximized. To solve the problem, a modified NSGA-II, which utilized a new repairing mechanism, is proposed in response to the need of appropriate method treating such complicated problems. The validity of the proposed model and algorithm is evaluated and proved though a benchmark test problem. The obtained results reveal that in contrast to benchmark that applied an exact solution procedure, the proposed algorithm is capable of delivering the astonishing solutions in a more effective procedure. Along with the use of NSGA-II, in this study, three well-known meta-heuristic algorithms, namely PESA-II, NSACO and NPGA-II, are also employed for solving the problem in order to evaluate the effectiveness of the proposed algorithm, so that the results demonstrate the high performance for the NSGA-II over them. Finally, in light of the obtained results, this study offers an efficient framework enabling the decision maker to handle uncertainty in ALBPs along with the use of an efficient algorithm to solve them.

Enhanced migrating birds optimization algorithm for U-shaped assembly line balancing problems with workers assignment

U-shaped assembly lines have been popularly adopted in electronics and appliances to improve their flexibility and efficiency. However, most past studies assumed that the processing time of each task is fixed and hence just considered the task allocation but ignored worker assignment. In this paper, the processing time of each task depends on the workers and then the cooperative optimization of task allocation and workers assignment is considered in U-shaped assembly line balancing problems to optimize the cycle time. Later, an enhanced migrating birds optimization algorithm (EMBO) is proposed to solve it. In the EMBO algorithm, since this new problem has two subproblems: task allocation and worker assignment, the prevent work designs two neighborhood structures to improve the leader and following birds. Furthermore, the temperature acceptance criteria, to judge whether the neighbor replaces current following bird, are developed to ensure the diversity of population and avoid being trapped in the local optimum. And a competitive mechanism is introduced to increase the probability of the promising birds locating in the front of the line. The proposed algorithm is compared with other well-known algorithms in the literature, and the numerical results demonstrate that the proposed algorithm outperforms other algorithms.

An enhanced NSGA-II algorithm for fuzzy bi-objective assembly line balancing problems

Abstract Due to immense role of an efficient assembly line in today’s manufacturing systems, the consideration of this paper is devoted to address the straight and U-shaped assembly line balancing problems. Assembly Line Balancing Problem (ALBP) considers conflicting objective functions that should be optimized simultaneously subject to set of constraints. To this end, this paper spends endeavor to develop a new fuzzy linear programming model. Having dealt with uncertain nature of the real production system, triangular fuzzy numbers (TFNs) are employed in order to represent uncertainty and vagueness associated with the task processing times. The proposed model can be regarded as a basis of fuzzy programming for further practical development in assembly line problems. To solve the problem, an efficient Multi Objective Genetic Algorithm (MOGA) is proposed. For this purpose, having respected several special characteristics of both fuzzy straight and U-shaped assembly line balancing problems for algorithm segments, including initial generation, encoding and decoding schemes, and GA’s operations, an new approach is proposed to promote population diversity and search efficiency. Finally, the proposed algorithm is evaluated though several benchmarks as well as that of exact method. The obtained results proved high efficiency of our method over other approaches suggested in the literature.

A novel competitive hybrid approach based on grouping evolution strategy algorithm for solving U-shaped assembly line balancing problems

Assembly line balancing problems (ALBPs) are among the well-known problems in manufacturing systems that belong to NP-hard class of problems. In the literature, there are various metaheuristic methods proposed to solve different models of such a problem under various assumptions. This research considers the U-shaped ALBP and proposes a hybrid solution method based on grouping evolution strategy algorithm. To develop a competitive approach, two most popular constructive methods of solving ALBP including the ranked positional weight method, and COMSOAL algorithm are modified and improved. We investigate the effectiveness of the proposed improvements and evaluate the performance of the proposed approach via solving a number of existing problems in the literature and compare the results with some current methods in the literature. Computational results indicate that the proposed approach for solving U-shaped ALBP test problems performs efficiently and is able to obtain the global optimal solution of the most of high dimensional problems.

An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

U-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values.

Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem

Nowadays, robots are used extensively in robotic assembly line balancing system because of the capabilities of the robots. Robotic assembly lines are used to manufacture high volume product in customization and specialization production. In this paper, type II robotic mixed-model assembly line balancing is considered. The goals are to minimize robot purchasing costs, robot setup costs, sequence dependent setup costs, and cycle time. The proposed model tries to determine an optimal or near-optimal configuration of tasks, workstations in U-shaped assembly line balancing. In this model, two types of tasks including the special task for one product model and the common task for several products models exist. The problem with the aforementioned conditions is NP-hard problem. So, we used two different multi- objective evolutionary algorithms (MOEAs) to solve the problem. First algorithm is non-dominated sorting genetic algorithm (NSGA-II) and the second one is multi-objective particle swarm optimization (MOPSO). Also, we used GAMS software to solve the problem in small size problem to validate our proposed model. Then, some numerical examples are presented and the experimental results and the performance of the algorithms are compared with each other.

A new heuristic-based bi-objective simulated annealing method for U-shaped assembly line balancing

The U-shaped assembly line has received a considerable attention and has been widely used in industry in recent years due to the pressures of the just-in-time (JIT) manufacturing philosophy. However, balancing the U-shaped line is more difficult than balancing the traditional straight line. This study aims at balancing the U-shaped assembly line by introducing a novel heuristic which is based on the simulated annealing (SA) algorithm. The objectives to be optimised in this study are the number of workstations and balance efficiency. The performance of the proposed algorithm is examined by solving a set of standard test problems and a real case study. The results attained by the proposed algorithm were compared against the best known solution in the literature and it was found that the proposed algorithm is able to find good solutions in a reasonably short computational time. [Received 29 December 2012; Revised 28 March 2014; Revised 25 August 2014; Revised 22 May 2015; Accepted 26 May 2015]

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm.

Robotic U-shaped assembly line balancing using particle swarm optimization

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.

On the MILP model for the U-shaped assembly line balancing problems

U-shaped assembly lines are an important configuration of modern manufacturing systems due to their flexibility to adapt to varying market demands. In U-shaped lines, tasks are assigned after their predecessors or successors. Some MILP models have been proposed to formulate the U-shaped assembly line balancing problem using either–or constraints to express precedence relationships. We show that this modeling approach reported in the literature may often find optimal solutions that are infeasible and verify this on a large set of benchmark problems. We present a revision to this model to accurately express the precedence relationships without introducing additional variables or constraints. We also illustrate on the same benchmark problems that our revision always reports solutions that are feasible.

Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times

This paper aims at the straight and U-shaped assembly line balancing. Due to the uncertainty, variability and imprecision in actual production systems, the processing time of tasks are presented in triangular fuzzy numbers. In this case, it is intended to optimize the efficiency and idleness percentage of the assembly line as well as and concurrently with minimizing the number of workstations. To solve the problem, a modified genetic algorithm is proposed. One-fifth success rule in selection operator to improve the genetic algorithm performance. This leads genetic algorithm being controlled in convergence and diversity simultaneously by the means of controlling the selective pressure. Also a fuzzy controller in selective pressure employed for one-fifth success rule better implementation in genetic algorithm. In addition, Taguchi design of experiments used for parameter control and calibration. Finally, numerical examples are presented to compare the performance of proposed method with existing ones. Results show the high performance of the proposed algorithm.