Robotic assembly line outperforms labor assembly line in terms of efficiency and flexibility. This paper focuses on the mixed-model U-shaped robotic assembly line balancing and sequencing problem ...(MURALBSP). Different from most reported works, this paper includes the energy consideration. Two conflict objectives, i.e., energy consumption and makespan, are studied for energy saving and efficient production. To this end, a hybrid multi-objective dragonfly algorithm (HMODA) is proposed. First, the mathematical model of this bi-objective problem is formulated. Second, the basic dragonfly algorithm is improved to solve the problem. The specific encoding and decoding method is designed and the chaotic map is used to improve algorithm randomness. Besides, the solution update method is amended and multi-point crossover mechanism is employed. Finally, several multiple size benchmark problems are designed and comparisons are conducted, the sensitivity of decision variables is analyzed to provide managerial insights. The results suggest that HMODA is more efficient in solving the proposed problem than compared algorithms.
•The effect of task assignment, model sequence and robot allocation on energy consumption and makespan are investigated.•The mathematic model for the energy oriented mixed-model U-shaped robotic assembly line balancing and sequencing problem is proposed.•A hybrid multi-objective dragonfly algorithm is designed.•The sensitivity of decision variables is analyzed, and the managerial insights are provided.
A mixed-model assembly U-line is a flexible production system capable of manufacturing a variety of similar models, and it has become popular as an important component of the just-in-time production ...system. However, it poses new challenges for the optimal design of assembly lines because both the task assignment and the production sequence affect the workload variance among workstations. As a consequence, this paper addresses the line balancing problem and the model sequencing problem jointly and proposes a 0-1 stochastic programming model. In this model, task times are assumed to be stochastic variables independently distributed with normal distributions and the objective is to minimise the expectation of work overload time for a given combination of cycle time and number of workstations. To solve the problem, a simulated annealing-based algorithm is developed, which can also be used to minimise the absolute deviation of workloads in a deterministic environment. The experimental results for a set of benchmark problems show that the proposed algorithm outperforms the existing algorithms in terms of solution quality and running time.
•This paper presents a simulated annealing algorithm based method.•It aims at solving the balancing and sequencing problems of the mixed-model U-lines.•It also aims at minimizing the number of ...workstation.•The evaluation of the finesses is done by another simulated annealing algorithm.•It is found that ‘ADW’ is an insufficient performance criterion.
It is known that two interrelated problems called as line balancing and model sequencing should be solved simultaneously for an efficient implementation of a mixed-model U-shape assembly line in a JIT (Just in Time) environment. On the other hand, three versions of assembly line balancing problem can be identified: Type I, Type II, and Type E. There are only two articles (Kara, Ozcan, & Peker, 2007a and Hamzadayi & Yildiz, 2012) related to simultaneous balancing and sequencing of mixed-model U-lines for minimizing the number of stations (Type 1 problem) by ignoring the fixed model sequence in the current literature. In this paper, a simulated annealing algorithm is proposed for solving a problem of type 1 by ignoring the fixed model sequence. Accordingly, simulated annealing based fitness evaluation approach proposed by Hamzadayi and Yildiz (2012) is enhanced by adding the tabu list, and inserted into the proposed algorithm. Implementation difficulties experienced in meta-heuristics based on solution modification for solving these types of problems are demonstrated. ‘Absolute deviation of workloads’ (ADW) is quite frequently used as performance criteria in the literature. It is found that ADW is an insufficient performance criterion for evaluating the performance of the solutions, and this is showed by means of an illustrative example. The parameters of the proposed algorithm are reviewed for calibrating the algorithm by means of Taguchi design of experiments. Performance of the proposed approach is tested through a set of test problems. The results of computational experiments indicate that the proposed approach is an effective method in solving simultaneous line balancing/model sequencing problems for mixed-model U-lines for minimizing the number of stations.
► This paper presents a genetic algorithm based method. ► It aims at solving the balancing and sequencing problems of the mixed-model U-lines. ► Our method considers parallel workstations assignment ...and zoning constraints. ► A new fitness function aiming at minimizing the number of workstations is adapted. ► It also deals with smoothing the workload balance between and within workstations.
This paper presents a Priority-Based Genetic Algorithm (
PGA) based method for the simultaneously tackling of the mixed-model U-shape assembly line (
MMUL) line balancing/model sequencing problems (
MMUL/
BS) with parallel workstations and zoning constraints and allows the decision maker to control the process to create parallel workstations and to work in different scenarios. In the presented method, simulated annealing based fitness evaluation approach (
SABFEA) is developed to be able to make fitness function calculations easily and effectively. A new fitness function is adapted to
MMULs for aiming at minimizing the number of workstations as primary goal and smoothing the workload between-within workstations by taking all cycles into consideration. A numerical example to clarify the solution methodology is presented. Performance of the proposed approach is tested through sets of test problem with randomly generated minimum part sets. The results of the computational experiments indicate that
SABFEA works with
PGA very concordantly; and it is an effective method in solving
MMUL/
BS with parallel workstations and zoning constraints.
This paper proposes a new evolutionary approach to deal with both balancing and sequencing problems in mixed-model U-shaped lines. The use of U-shaped lines is an important element in Just-In-Time ...production. For an efficient operation of the lines, it is important to have a proper line balancing and model sequencing. A new genetic approach, called endosymbiotic evolutionary algorithm, is proposed to solve the two problems of line balancing and model sequencing at the same time. The algorithm imitates the natural evolution process of endosymbionts that is an extension of existing cooperative or symbiotic evolutionary algorithm. The distinguishing feature of the proposed algorithm is that it maintains endosymbionts that are a combination of an individual and its symbiotic partner. The existence of endosymbionts can accelerate the speed that individuals converge to good solutions. This enhanced capability of exploitation together with the parallel search capability of traditional symbiotic algorithms results in finding better quality solutions than existing hierarchical approaches and symbiotic algorithms. A set of experiments are carried out, and the results are reported.
Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time ...production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic algorithm. Computational results and comparisons show that the proposed algorithms can improve the results obtained by existing algorithms developed for MMUL/BS.
A widespread supposition on mixed-model assembly line-balancing problems assigns a task, which is shared between two or more models to a single station. Bukchin and Rabinowitch (European Journal of ...Operational Research, 174:492–508,
2006
) relaxed the restriction for mixed-model straight-line assembly line problems and allowed tasks common to multiple models to be assigned to different stations, called task duplication. In this paper, considering the same relaxation but for mixed-model U-shaped assembly lines, a novel genetic algorithm (GA) approach for solving large-scale problems is developed. Although superiorities of U-shaped assembly lines over straight lines have been discussed in several articles, this paper makes the advantage more tangible by providing a quantitative example. This paper also presents a novel two-stage genetic algorithm which is fittingly devised for solving the new proposed model. In order to evaluate the effectiveness of the GA, one small-scale and one medium-scale problem are solved using both the proposed GA and Lingo 8.0 software, and the obtained outcomes are compared. The computational results indicate that the GA is capable of providing high-quality solutions for small- and medium-scale problems in negligible central processing unit (CPU) times. It is worth mentioning that, for large-scale problems, such as Kim and Arcus test problems, no analogous results for those obtained by our proposed GA exist. To conclude, it can be said that the proposed GA performs well and is able to solve large-scale problems within acceptable CPU times.
This study deals with the mixed-model U-lines utilized in just-in-time (JIT) production systems. Successful implementations of mixed-model U-lines requires solutions to two important problems called ...line balancing and model sequencing. In terms of some balance-dependent performance measures the effectiveness of a mixed-model U-line can be increased by solving line balancing and model sequencing problems simultaneously. However, this may lead to inefficient values of sequence-dependent performance measures. Hence, increasing the effectiveness of a mixed-model U-line requires balancing and sequencing problems that be dealt with multiple objectives. Balancing and sequencing mixed-model U-lines with multiple objectives has not been considered in the literature to date. In this study, a multi-objective approach for balancing and sequencing mixed-model U-lines to simultaneously minimize the absolute deviations of workloads across workstations, part usage rate, and cost of setups is presented. To increase the performance of the proposed algorithm, a newly developed neighbourhood generation method is also employed. Since the performance measures considered in the study are conflicting with each other, the proposed algorithm suggests much flexibility and more realistic results to decision makers. Solution methodology is illustrated using an example and a two-stage comprehensive experimental study is conducted to determine the effective values of algorithm parameters and investigate the relationships between performance measures. Results show that the proposed approach is more realistic than the limited number of existing methodologies. The proposed approach is also extended to consider the stochastic completion times of tasks.
Avoiding work overload (imbalance) in mixed model U-line production systems entails an investigation into both balancing and sequencing problems at the same time and that is why some authors have ...considered both planning problems simultaneously. However because of the existing differences between planning horizons of balancing and sequencing problems (the former is a long to mid-term planning problem whereas the latter has a short term planning horizon) this simultaneous approach is only practical under very special conditions. It is also known that installation of an assembly line usually needs considerable capital investments and consequently it is necessary to design and balance such a system so that it works as efficiently as possible. To do so, in this paper, we develop a new approach to balance a mixed model U-shaped production system independent of what product sequences may be. This new approach is based on minimization of crossover workstations. Due to utilization of crossover workstations, balancing mixed model assembly lines in U-shaped line layouts is more complicated than that of straight lines. Some kind of issues including the ‘model mixes’ appearing in such workstations and the time taken for an operator to move from one side of the line to another increase the complexity of mixed model U-line balancing problems (MMULBP). Therefore it seems reasonable to develop a model in which minimizing the number of crossover workstations and maximizing the line efficiency are considered at the same time. Such a model is presented in this paper. In the proposed model, minimizing the variation of workload is also considered and taking into account operator's travel times, an extra time is assigned to workload of crossover workstations. Furthermore a genetic algorithm (GA) is proposed and a number of well-known test problems are solved by the GA and the related results are illustrated. Finally, the conclusion is presented.
With the growth in customers’ demand diversification, mixed-model U-lines (MMUL) have acquired increasing importance in the area of assembly systems. There are generally two different approaches in ...the literature for balancing such systems. Some researchers believe that since the types of models can be very diverse, a balancing approach without simultaneously sequencing of models will not yield an optimum configuration. On the other hand, another group of researchers point to the high cost of balancing systems and prefer to do it only one time regardless of the models’ sequences. In this paper, we aim to compare these two approaches by introducing an economic indicator. To do so, two models as representatives of the two different viewpoints are taken from the literature. To check the validity of this methodology, it is implemented by Lingo 11.0, for small scale, and GA, for a large scale. The obtained results indicate that, from the proposed economic indicator point of view, mixed-model U-lines balancing and sequencing (MMUL/BS) is preferred to its counterpart, mixed-model U-lines balancing (MMULB). This paper offers economic guidelines for managers to choose between only balancing and implementing it by sequencing at the same time.
