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

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Copyright © 2014 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 31 January 2014, available online: http://www.tandfonline.com/10.1080/00207543.2013.879618Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed by Ozcan et al. (Balancing parallel two-sided assembly lines, International Journal of Production Research, 48 (16), 4767-4784, 2010). The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles explained and discussed

Type-E Parallel Two-Sided Assembly Line Balancing Problem: Mathematical Model and Ant Colony Optimisation based Approach with Optimised Parameters

Copyright © 2015 Elsevier. This is a PDF file of an unedited manuscript that has been accepted for publication in Computers and Industrial Engineering (doi:10.1016/j.cie.2014.12.037). 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.There are many factors which affect the performance of a complex production system. Efficiency of an assembly line is one of the most important of these factors since assembly lines are generally constructed as the last stage of an entire production system. Parallel two-sided assembly line system is a new research domain in academia though these lines have been utilised to produce large sized products such as automobiles, trucks, and buses in industry for many years. Parallel two-sided assembly lines carry practical advantages of both parallel assembly lines and two-sided assembly lines. The main purpose of this paper is to introduce type-E parallel two-sided assembly line balancing problem for the first time in the literature and to propose a new ant colony optimisation based approach for solving the problem. Different from the existing studies on parallel assembly line balancing problems in the literature, this paper aims to minimise two conflicting objectives, namely cycle time and number of workstations at the same time and proposes a mathematical model for the formal description of the problem. To the best of our knowledge, this is the first study which addresses both conflicting objectives on a parallel two-sided assembly line configuration. The developed ant colony optimisation algorithm is illustrated with an example to explain its procedures. An experimental design is also conducted to calibrate the parameters of the proposed algorithm using response surface methodology. Results obtained from the performed computational study indicate that minimising cycle time as well as number of workstations help increase system efficiency. It is also observed that the proposed algorithm finds promising results for the studied cases of type-E parallel two-sided assembly line balancing problem when the results are compared with those obtained from other three well-known heuristics

On applications of ant colony optimisation techniques in solving assembly line balancing problems

PublishedArticleRecently, there is an increasing interest in applications of meta-heuristic approaches in solving various engineering problems. Meta-heuristics help both academics and practitioners to get not only feasible but also near optimal solutions where obtaining a solution for the relevant problem is not possible in a reasonable time using traditional optimisation techniques. Ant colony optimisation algorithm is inspired from the collective behaviour of ants and one of the most efficient meta-heuristics in solving combinatorial optimisation problems. One of the main application areas of ant colony optimisation algorithm is assembly line balancing problem. In this paper, we first give the running principle of ant colony optimisation algorithm and then review the applications of ant colony optimisation based algorithms on assembly line balancing problems in the literature. Strengths and weaknesses of proposed algorithms to solve various problem types in the literature have also been discussed in this research. The main aim is to lead new researches in this domain and spread the application areas of ant colony optimisation techniques in various aspects of line balancing problems. Existing researches in the literature indicate that ant colony optimisation methodology has a promising solution performance to solve line balancing problems especially when integrated with other heuristic and/or meta-heuristic methodologies

Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, DOI: 10.1016/j.ijpe.2014.08.010One of the key factors of a successfully implemented mixed-model line system is considering model sequencing problem as well as the line balancing problem. In the literature, there are many studies, which consider these two tightly interrelated problems individually. However, we integrate the model sequencing problem in the line balancing procedure to obtain a more efficient solution for the problem of Simultaneous Balancing and Sequencing of Mixed-Model Parallel Two-Sided Assembly Lines. A mathematical model is developed to present the problem and a novel agent based ant colony optimisation approach is proposed as the solution method. Different agents interact with each other to find a near optimal solution for the problem. Each ant selects a random behaviour from a predefined list of heuristics and builds a solution using this behaviour as a local search rule along with the pheromone value. Different cycle times are allowed for different two-sided lines located in parallel to each other and this yields a complex problem where different production cycles need to be considered to build a feasible solution. The performance of the proposed approach is tested through a set of test cases. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested

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

Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Assembly lines are frequently used as a production method to assemble complex products. Two-sided assembly lines are utilized to assemble large-sized products (e.g., cars, buses, trucks). Locating two lines in parallel helps improve line efficiency by enabling collaboration between the line workers. This paper proposes a mixed-model parallel two-sided assembly line system that can be utilized to produce large-sized items in an inter-mixed sequence. The mixed-model parallel two-sided line balancing problem is defined and the advantages of utilizing multi-line stations across the lines are discussed. A flexible agent-based ant colony optimization algorithm is developed to solve the problem and a numerical example is given to explain the method systematically. The proposed algorithm builds flexible balancing solutions suitable for any model sequence launched. The dynamically changing workloads of workstations (based on specific product models during the production process) are also explored. A comprehensive experimental study is conducted and the results are statistically analyzed using the well-known paired sample t-test. The test results indicate that the mixed-model parallel two-sided assembly line system reduces the workforce need in comparison with separately balanced mixed-model two-sided lines. It is also shown that the proposed algorithm outperforms the tabu search algorithm and six heuristics often used in the assembly line balancing domain

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

Balancing of parallel U-shaped assembly lines

Copyright © 2015 Elsevier. This is a PDF file of an unedited manuscript that has been accepted for publication in Computers & Operations Research (doi: 10.1016/j.cor.2015.05.014). As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable 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. Please cite this article as: Ibrahim Kucukkoc, David Z. Zhang, Balancing of parallel U-shaped assembly Lines, Computers & Operations Research, http://dx.doi.org/10.1016/j.cor.2015.05.014A new hybrid assembly line design, called Parallel U-shaped Assembly Line system, is introduced and characterised along with numerical examples for the first time. Different from existing studies on U-shaped lines, we combine the advantages of two individual line configurations (namely parallel lines and U-shaped lines) and create an opportunity for assigning tasks to multi-line workstations located in between two adjacent U-shaped lines with the aim of maximising resource utilisation. Utilisation of crossover workstations, in which tasks from opposite areas of a same U-shaped line can be performed, is also one of the main advantages of the U-shaped lines. As in traditional U-shaped line configurations, the newly proposed line configuration also supports the utilisation of crossover workstations. An efficient heuristic algorithm is developed to find well-balanced solutions for the proposed line configurations. Test cases derived from existing studies and modified in accordance with the proposed system in this study are solved using the proposed heuristic algorithm. The comparison of results obtained when the lines are balanced independently and when the lines are balanced together (in parallel to each other) clearly indicates that the parallelisation of U-shaped lines helps decrease the need for workforce significantly.Balikesir UniversityTurkish Council of Higher Educatio

A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Typically, the total number of required workstations are minimised for a given cycle time (this problem is referred to as type-1), or cycle time is minimised for a given number of workstations (this problem is referred to as type-2) in traditional balancing of assembly lines. However, variation in workload distributions of workstations is an important indicator of the quality of the obtained line balance. This needs to be taken into account to improve the reliability of an assembly line against unforeseeable circumstances, such as breakdowns or other failures. For this aim, a new problem, called lexicographic bottleneck mixed-model assembly line balancing problem (LB-MALBP), is presented and formalised. The lexicographic bottleneck objective, which was recently proposed for the simple single-model assembly line system in the literature, is considered for a mixed-model assembly line system. The mathematical model of the LB-MALBP is developed for the first time in the literature and coded in GAMS solver, and optimal solutions are presented for some small scale test problems available in the literature. As it is not possible to get optimal solutions for the large-scale instances, an artificial bee colony algorithm is also implemented for the solution of the LB-MALBP. The solution procedures of the algorithm are explored illustratively. The performance of the algorithm is also assessed using derived well-known test problems in this domain and promising results are observed in reasonable CPU times