Time and space multi-manned assembly line balancing problem using genetic algorithm

Purpose: Time and Space assembly line balancing problem (TSALBP) is the problem of balancing the line taking the area required by the task and to store the tools into consideration. This area is important to be considered to minimize unplanned traveling distance by the workers and consequently unplanned time waste. Although TSALBP is a realistic problem that express the real-life situation, and it became more practical to consider multi-manned assembly line to get better space utilization, few literatures addressed the problem of time and space in simple assembly line and only one in multi-manned assembly line. In this paper the problem of balancing bi-objective time and space multi-manned assembly line is proposed Design/methodology/approach: Hybrid genetic algorithm under time and space constraints besides assembly line conventional constraints is used to model this problem. The initial population is generated based on conventional assembly line heuristic added to random generations. The objective of this model is to minimize number of workers and number of stations. Findings: The results showed the effectiveness of the proposed model in solving multi-manned time and space assembly line problem. The proposed method gets better results in solving real-life Nissan problem compared to the literature. It is also found that there is a relationship between the variability of task time, maximum task time and cycle time on the solution of the problem. In some problem features it is more appropriate to solve the problem as simple assembly line than multi-manned assembly line. Originality/value: It is the first article to solve the problem of balancing multi-manned assembly line under time and area constraint using genetic algorithm. A relationship between the problem features and the solution is found according to it, the solution method (one sided or multi-manned) is definedPeer Reviewe

A mixed-integer programming model for cycle time minimization in assembly line balancing: Using rework stations for performing parallel tasks

[EN] In assembly lines, rework stations are generally used for reprocessing defective items. On the other hand, using rework stations for this purpose only might cause inefficient usage of the resources in this station especially in an assembly line with a low defective rate. In this study, a mixed-integer programming model for cycle time minimization is proposed by considering the use of rework stations for performing parallel tasks. By linearizing the non-linear constraint about parallel tasks using a variate transformation, the model is transformed to a linear-mixed-integer form. In addition to different defective rates, different rework station positions are also considered using the proposed model. The performance of the model is analyzed on several test problems from the related literature.Cavdur, F.; Kaymaz, E. (2020). A mixed-integer programming model for cycle time minimization in assembly line balancing: Using rework stations for performing parallel tasks. International Journal of Production Management and Engineering. 8(2):111-121. https://doi.org/10.4995/ijpme.2020.12368OJS11112182Altekin, F. T., Bayindir, Z. P., & Gümüskaya, V. (2016). Remedial actions for disassembly lines with stochastic task times. Computers & Industrial Engineering, 99, 78-96. https://doi.org/10.1016/j.cie.2016.06.027Anderson, E. J., & Ferris, M. C. (1994). Genetic algorithms for combinatorial optimization: the assemble line balancing problem. ORSA Journal on Computing, 6(2), 161-173. https://doi.org/10.1287/ijoc.6.2.161Askin, R. G., & Zhou, M. (1997). A parallel station heuristic for the mixed-model production line balancing problem. International Journal of Production Research, 35(11), 3095-3106. https://doi.org/ 10.1080/002075497194309Bard, J. F. (1989). Assembly line balancing with parallel workstations and dead time. The International Journal of Production Research, 27(6), 1005-1018. https://doi.org/10.1080/00207548908942604Bartholdi, J. J. (1993). Balancing two-sided assembly lines: a case study. International Journal of Production Research, 31(10), 2447-2461. https://doi.org/10.1080/00207549308956868Battaia, O., & Dolgui, A. (2013). A taxonomy of line balancing problems and their solution approaches. International Journal of Production Economics, 142(2), 259-277. https://doi.org/10.1016/j.ijpe.2012.10.020Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management science, 32(8), 909-932. https://doi.org/10.1287/mnsc.32.8.909Baykasoglu, A., & Demirkol Akyol, S. (2014). Ergonomic assembly line balancing. Journal of the Faculty of Engineering and Architecture of Gazi University, 29(4), 785-792. https://doi.org/10.17341/gummfd.00296Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European journal of operational research, 168(3), 694-715. https://doi.org/10.1016/j.ejor.2004.07.023Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European journal of operational research, 183(2), 674-693. https://doi.org/10.1016/j.ejor.2006.10.010Bryton, B. (1954). Balancing of a continuous production line. Master's Thesis, Northwestern University, Evanston.Cercioglu, H., Ozcan, U., Gokcen, H., & Toklu, B. (2009). A simulated annealing approach for parallel assembly line balancing problem. Journal of the Faculty of Engineering and Architecture of Gazi University, 24(2), 331-341.Efe, B., Kremer, G. E. O., & Kurt, M. (2018). Age and gender-based workload constraint for assembly line worker assignment and balancing problem in a textile firm. International Journal of Industrial Engineering, 25(1), 1-17.Ghosh, S., & Gagnon, R. J. (1989). A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems. The International Journal of Production Research, 27(4), 637-670. https://doi.org/10.1080/00207548908942574Gokcen, H., & Baykoc, Ö. F. (1999). A new line remedial policy for the paced lines with stochastic task times. International Journal of Production Economics, 58(2), 191-197. https://doi.org/10.1016/S0925-5273(98)00123-6Gokcen, H., Agpak, K., & Benzer, R. (2006). Balancing of parallel assembly lines. International Journal of Production Economics, 103(2), 600-609. https://doi.org/10.1016/j.ijpe.2005.12.001Guner, B., & Hasgul, S. (2012). U-Type assembly line balancing with ergonomic factors for balance stability. Journal of the Faculty of Engineering and Architecture of Gazi University, 27(2), 407-415.Kaplan, O. (2004). Assembly line balancing with task paralleling. Master's Thesis, METU, Ankara.Kara, Y., Ozguven, C., Yalcın, N., & Atasagun, Y. (2011). Balancing straight and U-shaped assembly lines with resource dependent task times. International Journal of Production Research, 49(21), 6387-6405. https://doi.org/10.1080/00207543.2010.535039Kara, Y., Atasagun, Y., Gokcen, H., Hezer, S., & Demirel, N. (2014). An integrated model to incorporate ergonomics and resource restrictions into assembly line balancing. International Journal of Computer Integrated Manufacturing, 27(11), 997-1007. https://doi.org/10.1080/0951192X.2013.874575Kazemi, S. M., Ghodsi, R., Rabbani, M., & Tavakkoli-Moghaddam, R. (2011). A novel two-stage genetic algorithm for a mixed-model U-line balancing problem with duplicated tasks. The International Journal of Advanced Manufacturing Technology, 55(9-12), 1111-1122. https://doi.org/10.1007/s00170-010-3120-6Kim, Y. K., Kim, Y., & Kim, Y. J. (2000). Two-sided assembly line balancing: a genetic algorithm approach. Production Planning & Control, 11(1), 44-53. https://doi.org/10.1080/095372800232478Kottas, J. F., & Lau, H. S. (1976). A total operating cost model for paced lines with stochastic task times. AIIE Transactions, 8(2), 234-240. https://doi.org/10.1080/05695557608975072Lau, H. S., & Shtub, A. (1987). An exploratory study on stopping a paced line when incompletions occur. IIE transactions, 19(4), 463-467. https://doi.org/10.1080/07408178708975421Lee, T. O., Kim, Y., & Kim, Y. K. (2001). Two-sided assembly line balancing to maximize work relatedness and slackness. Computers & Industrial Engineering, 40(3), 273-292. https://doi.org/10.1016/S0360-8352(01)00029-8Mutlu, O., & Ozgormus, E. (2012). A fuzzy assembly line balancing problem with physical workload constraints. International Journal of Production Research, 50(18), 5281-5291. https://doi.org/10.1080/00207543.2012.709647Ozcan, U., & Toklu, B. (2010). Balancing two-sided assembly lines with sequence-dependent setup times. International Journal of Production Research, 48(18), 5363-5383. https://doi.org/10.1080/00207540903140750Pinto, P., Dannenbring, D. G., & Khumawala, B. M. (1975). A branch and bound algorithm for assembly line balancing with paralleling. The International Journal of Production Research, 13(2), 183-196. https://doi.org/10.1080/00207547508942985Sabuncuoglu, I., Erel, E., & Alp, A. (2009). Ant colony optimization for the single model U-type assembly line balancing problem. International Journal of Production Economics, 120(2), 287-300. https://doi.org/10.1016/j.ijpe.2008.11.017Salveson, M. E. (1955). The assembly line balancing problem. The Journal of Industrial Engineering, 18-25.Scholl, A., & Becker, C. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research, 168(3), 666-693. https://doi.org/10.1016/j.ejor.2004.07.022Shtub, A. (1984). The effect of incompletion cost on line balancing with multiple manning of work stations. The International Journal of Production Research, 22(2), 235-245. https://doi.org/10.1080/00207548408942450Silverman, F. N., & Carter, J. C. (1986). A cost-based methodology for stochastic line balancing with intermittent line stoppages. Management Science, 32(4), 455-463. https://doi.org/10.1287/mnsc.32.4.455Simaria, A. S., & Vilarinho, P. M. (2001). The simple assembly line balancing problem with parallel workstations-a simulated annealing approach. Int J Ind Eng-Theory, 8(3), 230-240.Sivasankaran, P., & Shahabudeen, P. (2014). Literature review of assembly line balancing problems. The International Journal of Advanced Manufacturing Technology, 73(9-12), 1665-1694. https://doi.org/10.1007/s00170-014-5944-ySuer, G. A. (1998). Designing parallel assembly lines. Computers & industrial engineering, 35(3-4), 467-470. https://doi.org/10.1016/S0360-8352(98)00135-1Suwannarongsri, S., & Puangdownreong, D. (2008). Optimal assembly line balancing using tabu search with partial random permutation technique. International Journal of Management Science and Engineering Management, 3(1), 3-18. https://doi.org/10.1080/17509653.2008.10671032Tiacci, L., Saetta, S., & Martini, A. (2006). Balancing mixed-model assembly lines with parallel workstations through a genetic algorithm approach. International Journal of Industrial Engineering, 13(4), 402.Ugurdag, H. F., Rachamadugu, R., & Papachristou, C. A. (1997). Designing paced assembly lines with fixed number of stations. European Journal of Operational Research, 102(3), 488-501. https://doi.org/10.1016/S0377-2217(96)00248-2Wei, N. C., & Chao, I. M. (2011). A solution procedure for type E simple assembly line balancing problem. Computers & Industrial Engineering, 61(3), 824-830. https://doi.org/10.1016/j.cie.2011.05.01

Worker Skills and Equipment Optimization in Assembly Line Balancing by a Genetic Approach

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

An efficient genetic algorithm application in assembly line balancing.

The main achievement of this research is the development of a genetic algorithm model as a solution approach to the single model assembly line balancing problem (SMALBP), considered a difficult combinatorial optimisation problem. This is accomplished by developing a genetic algorithm with a new fitness function and genetic operators. The novel fitness function is based on a new front-loading concept capable of yielding substantially improved and sometimes optimum solutions for the SMALBP. The new genetic operators include a modified selection technique, moving crossover point technique, rank positional weight based repair method and dynamic mutation technique. The moving crossover point technique addressed the issue of propagating best attributes from parents to offspring and also supports the forward loading process. The new selection technique was developed by modifying the original rank-based selection scheme. This eliminates the high selective pressure associate with the original rank-based technique. Furthermore, the modified selection technique allows the algorithm to run long enough, if required, without premature convergence and this feature is very useful for balancing more complex real world problems. The repair technique included in this model repairs a higher proportion of distorted chromosomes after crossover than previous methods. Moreover, a third innovative feature, a moving adjacent mutation technique, strengthens the forward loading procedure and accelerates convergence. The performance of the front-loading fitness function currently outperforms the published fitness functions and fifty-four published test cases generated from sixteen precedence networks are used to assess the overall performance of the model. Encompassing the new genetic algorithm concepts, forty-four test problems (81%) achieved the best solutions obtained by published techniques and twenty-four problems (44%) produced better results than the benchmark Hoffmann precedence procedure, the closest non-genetic algorithm method. The superiority of the genetic model over other heuristics is identified in this research and future developments of this genetic algorithm application for assembly line balancing problems is evident

Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

Copyright © 2015 Springer. This is a PDF file of an unedited manuscript that has been accepted for publication in The International Journal of Advanced Manufacturing Technology. The final publication is available at: http://link.springer.com/article/10.1007/s00170-015-7320-y. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions

A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem

Copyright © 2015 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Production Planning & Control on 27 April 2015, available online: http://dx.doi.org/10.1080/09537287.2014.994685Assembly lines are usually constructed as the last stage of the entire production system and efficiency of an assembly line is one of the most important factors which affect the performance of a complex production system. The main purpose of this paper is to mathematically formulate and to provide an insight for modelling the parallel two-sided assembly line balancing problem, where two or more two-sided assembly lines are constructed in parallel to each other. We also propose a new genetic algorithm (GA)-based approach in alternatively to the existing only solution approach in the literature, which is a tabu search algorithm. To the best of our knowledge, this is the first formal presentation of the problem as well as the proposed algorithm is the first attempt to solve the problem with a GA-based approach in the literature. The proposed approach is illustrated with an example to explain the procedures of the algorithm. Test problems are solved and promising results are obtained. Statistical tests are designed to analyse the advantage of line parallelisation in two-sided assembly lines through obtained test results. The response of the overall system to the changes in the cycle times of the parallel lines is also analysed through test problems for the first time in the literature

Simple assembly line balancing problem under task deterioration

This paper introduces the effect of task deterioration in simple assembly line balancing problem. In many realistic assembly lines, a deterioration task is considered when a task is started earlier than the assigned time since the station time is constant and the earliness of the task does not reduce the cycle time. This phenomenon is known as deteriorating tasks. Therefore, we seek an optimal assignment and schedule of tasks in workstations, in order to minimize the number of stations for a given cycle time, which is known as SALBP-1. For this purpose, a mathematical model is proposed. Since the pure SALBP-1 is proved to be NP-hard and considering task deterioration complicates problem further, we propose a genetic algorithm for solving such problem. Several well-known test problems are solved to study the performance of the proposed approach

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Copyright © 2013 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 09 June 2013, available online: http://www.tandfonline.com/10.1080/00207543.2013.784411Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I)

Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set