Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the classical simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.Comment: 18 pages, 1 figur

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

임시 작업자를 활용한 혼합모델 조립라인의 통합적 균형화 연구

학위논문 (석사)-- 서울대학교 대학원 : 산업공학과, 2015. 2. 문일경.이 논문은 단일 제품을 조립하는 일반적인 조립라인 균형화 문제를 복수 제품들을 동시에 조립할 수 있는 혼합 모델 조립라인으로 확장하였으며, 임시 작업자를 고용하여 조립라인을 효율화할 수 있도록 하였다. 이를 고려한 세 가지 버전의 수학적 모형들을 개발하였다. 각 모형의 목표는 모든 직원의 임금과 작업장 비용을 합친 총 비용을 최소화하는 것, 작업장 수가 주어진 상황에서 사이클 시간을 최소화하는 것, 그리고 정해진 작업장 안에서 업무 과부하를 최소화하는 것이다. 제안된 모형들은 숙련된 작업자와 임시 작업자를 동시에 할당하는 사안과 작업들 사이의 선행관계 등 실제 현장에서 적용되는 실용적 특성들을 고려하고 있다. 뿐만 아니라, 총 비용을 최소화할 수 있는 복합유전알고리즘도 개발되었다. 해의 타당성을 보장하고 복합유전알고리즘의 우수성을 높이기 위해 특별한 유전연산자들과 발견적 기법이 사용되었다. 수치실험들을 통해서 복합유전알고리즘의 우수성을 입증하기 위하여 수학적 모형과 비교하였다.This study extends a single-model assembly line balancing problem to an integrated mixed-model assembly line balancing problem by incorporating temporary unskilled workers, who enhance productivity. Three mathematical models are developed to minimize the sum of total workstation costs, salaries of all workers, and cycle times and potential work overload of a predetermined number of workstations. The proposed models are based on particular features of the real-world problem, such as simultaneous assignments of skilled and temporary unskilled workers as well as precedent restrictions among the tasks. Furthermore, a hybrid genetic algorithm that minimizes total operation costs is developed. Special genetic operators and heuristic algorithms are used to ensure feasibility of solutions and make the hybrid genetic algorithm efficient. Computational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical models.Chapter 1. Introduction 1 1.1 The assembly line 1 1.1.1 Characteristics of assembly line problem 1 1.1.2 Assembly line balancing problem 2 1.2 The mixed-model assembly line 3 1.2.1 Characteristics of mixed model assembly line problem 3 1.2.2 Mixed model assembly line balancing problem 4 1.3 Literature review 5 1.4 Contributions 9 Chapter 2. Mathematical Models 11 2.1 General features of mathematical models 11 2.2 Problem description 11 2.3 Model Ⅰ 14 2.4 Model Ⅱ 18 2.5 Model Ⅲ 20 Chapter 3. A Hybrid Genetic Algorithm 23 3.1 Chromosome representation 24 3.2 Objective and fitness function 26 3.3 Genetic operator 27 3.3.1 Selection 27 3.3.2 Crossover 28 3.3.3 Mutation 29 3.4 Terminating conditions and parameters 30 Chapter 4. Computational Experiments 31 4.1 Experiments for Model Ⅰ 39 4.2 Experiments for Model Ⅱ 42 4.3 Experiments for Model Ⅲ 46 4.4 Validation of a hybrid genetic algorithm 48 Chapter 5. Conclusions 50 Bibliography 51 Abstract 54Maste

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

Balancing parallel assembly lines with disabled workers

[EN] In this paper, we study an assembly line balancing problem that occurs in sheltered worker centres for the disabled, where workers with very different characteristics are present. We are interested in the situation in which complete parallel assembly lines are allowed and name the resulting problem as parallel assembly line worker assignment and balancing problem (PALWABP). This approach enables many new possible worker-tasks assignments, what is beneficial in terms of both labour integration and productivity. We present a linear mixed-integer formulation and two heuristic solution methods: one is based on tabu search and the other is a biased random-key genetic algorithm (BRKGA). Computational results with a large set of instances recently proposed in the literature show the advantages of allowing such alternative line layouts.This research was supported by CAPES-Brazil and MEC-Spain (coordinated project CAPES DGU 258-12/PHB2011-0012-PC) and by FAPESP-Brazil. The authors thank Dr. Marcus Ritt, from Universidade Federal do Rio Grande do Sul (UFRGS - Brazil), for providing the optimal solutions for the serial ALWABP. The authors also thank three anonymous reviewers for their comments which have helped improve this paper.Araujo, FFB.; Costa, AM.; Miralles Insa, CJ. (2015). Balancing parallel assembly lines with disabled workers. European J of Industrial Engineering. 9(3):344-365. https://doi.org/10.1504/EJIE.2015.069343S3443659

การจัดสมดุลที่มีหลายวัตถุประสงค์บนสายการประกอบแบบขนานผลิตภัณฑ์ผสมด้วยการหาค่าที่เหมาะสมที่สุดแบบการกระจายตัวของสิ่งมีชีวิตตามภูมิศาสตร์

บทคัดย่อการหาค่าที่เหมาะสมที่สุดแบบการกระจายตัวของสิ่งมีชีวิตตามภูมิศาสตร์ (Biogeography-based Optimization:BBO) เป็นเมตาฮิวริสติกเชิงวิวัฒนาการที่ได้รับแนวคิดมาจากพฤติกรรมการอพยพของสิ่งมีชีวิตบนเกาะต่างๆบทความนี้นำเสนออัลกอริทึม BBO เพื่อใช้สำหรับแก้ปัญหาการจัดสมดุลที่มีหลายวัตถุประสงค์บนสายการประกอบแบบขนานผลิตภัณฑ์ผสม โดยมีวัตถุประสงค์จำนวนทั้งสิ้น 4 วัตถุประสงค์ที่จะถูกทำให้เหมาะสมที่สุดไปพร้อมๆ กัน ได้แก่จำนวนสถานีงานน้อยที่สุด จำนวนสถานีน้อยที่สุด ความสมดุลของภาระงานระหว่างสถานีงานสูงที่สุด และความสัมพันธ์ของงานสูงที่สุด ผลจากการทดลองแสดงให้เห็นอย่างชัดเจนว่า BBO มีสมรรถนะในการแก้ปัญหาที่สูงกว่าอัลกอริทึมเชิงพันธุกรรมแบบการจัดลำดับที่ไม่ถูกครอบงำ II (Non-dominated Sorting Genetic Algorithm II: NSGA-II) ซึ่งเป็นอีกอัลกอริทึมหนึ่งที่เป็นที่นิยม ทั้งในด้านการลู่เข้าสู่กลุ่มคำตอบที่เหมาะสมที่สุดแบบพาเรโต การกระจายตัวของกลุ่มคำตอบ อัตราส่วนของคำตอบที่ไม่ถูกครอบงำและเวลาที่ใช้ในการคำนวณหาคำตอบคำสำคัญ: สายการประกอบแบบขนานผลิตภัณฑ์ผสม การจัดสมดุลหลายวัตถุประสงค์การหาค่าที่เหมาะสมที่สุด แบบการกระจายตัวของสิ่งมีชีวิตตามภูมิศาสตร์AbstractBiogeography-based Optimization (BBO) is an evolutionary metaheuristic inspired by migratory behavior of species among islands. This article presents a BBO algorithm for solving multi-objective mixed-model parallel assembly line balancing problem where four objectives are optimized simultaneously; i.e. to minimize the number of workstations, to minimize the number of stations, a maximization of workload balancing between workstations, and placing an emphasis on maximizing work relatedness. The results from experiments clearly show that BBO promises better performance than does Non-dominated Sorting Genetic Algorithm II (NSGA-II), which indicates another well-known algorithm, in terms of convergence to the Pareto-optimal set, spread of solutions, ratio of non-dominated solutions, and computation time to solution.Keywords: Mixed-model Parallel Assembly Lines, Multi-objective Line Balancing, Biogeography-based Optimizatio

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

Development of Genetic Algorithm Procedure for Sequencing Problem in Mixed-Model Assembly Lines

One of the most important issues for manufacturing systems is to determine the optimal job sequence over the production period. Mixed model assembly line is a kind of manufacturing systems which is able to deal with variable market demand. In this research, an effective utilization of mixed-model assembly line is considered as problem statement through implementing different production strategies. The problem under study contains set of mixed-model assembly line where finding the optimal job sequence based on different production strategies is the objective of this research. Different production strategies have different objectives to be met, meanwhile the sequence of jobs can be varied based on different production strategies. The main contribution of the study was implementing four production strategies in mixed-model assembly line problems, so the company can take advantage of proposed production model in different situations to meet the challenges. The first production strategy aims to minimize the make span of assembly lines and release the products to the market as soon as possible. The second production strategies attempts to minimize the make-span, and also balancing the assembly lines. It helps to balance the workload among all assembly lines. Minimizing the variation of completion time is also considered as third production strategy. The last production strategy aims to provide ideal status for assembly lines by minimizing the make-span and variation of completion time, and balancing the assembly lines. Due to NP-hard nature of sequencing problem in mixed model assembly line, a genetic algorithm is applied to cope with problem complexity and obtain a near optimal solution in a reasonable amount of time. All data is taken from literature and the result obtained from genetic algorithm procedure for the first production strategy is compared to study mentioned in literature which represents an improvement of 5% in shortening the make-span for one set of product. For the rest of production strategies, simulated annealing algorithm is applied to check the well performance of proposed genetic algorithm through reaching the same solutions for each production strategy. In all production strategies both GA and SA reaches to the same job sequence and same value of objective functions. It confirms that the proposed genetic algorithm procedure is able to tackle the problem complexity and reach to optimal solutions in different production strategies

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