Solving a Cubic Cell Formation Problem with Quality Index Using a Hybrid Meta-Heuristic Approach

Year 2023, Volume: 36 Issue: 2, 752 - 771, 01.06.2023

Burak Urazel , Yeliz Buruk Şahin

https://doi.org/10.35378/gujs.1003331

Cited By: 2

Abstract

Although most previous studies on cell formation have involved the assignment of parts and machines to cells, in recent years the assignment of workers has also been considered and the studies have taken into account the human factor. An important step for the successful implementation of a Cellular Manufacturing System is to decide appropriate groups of parts, machines, and workers and then assign them to cells. Consideration of the skills of workers and machines in processing parts has enhanced cell performance. In this study, the problem of a cubic cell formation that takes into account the three-dimensional part-machine-worker matrix is addressed, and the minimization of the exceptional element and void as well as the maximization of the part quality index is aimed. The mathematical model used in this study was coded in the GAMS 24.2.1 software. A hybrid GA-SA approach was also proposed for the solution of large instances. The relative Percentage Deviation performance index was utilized to evaluate the performance of the algorithm. According to the results, the hybrid technique developed, considering technical cell performance criteria together with worker skills, shows promising results from the standpoint of the considered objective and the computational time.

Keywords

Cubic cell formation, Quality index, Hybrid GA-SA, Cellular manufacturing systems

References

• [1] Danilovic, M., Ilic, O., “A novel hybrid algorithm for manufacturing cell formation problem”, Expert Systems with Applications, 135: 327-350, (2019).
• [2] Li, M., “A novel algorithm of cell formation with alternative machines and multiple-operation-type machines”, Computers & Industrial Engineering, 154: 107172, (2021).
• [3] Rafiee, M., Kayvanfar, V., Mohammadi, A., Werner, F., “A robust optimization approach for a cellular manufacturing system considering skill-leveled operators and multi-functional machines”, Applied Mathematical Modelling, 107: 379-397, (2022).
• [4] Ameli, M. S. J., Arkat, J., “Cell formation with alternative process routings and machine reliability consideration”, The International Journal of Advanced Manufacturing Technology, 35: 761–768, (2008).
• [5] Dalfard, V.M., “New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements”, Applied Mathematical Modelling, 37(4): 1884-1896, (2013).
• [6] Rafiee, M., Mohamaditalab, A., “Investigation into skill leveled operators in a multi-period cellular manufacturing system with the existence of multi-functional machines”, Scientia Iranica, 27(6): 3219-3232, (2020).
• [7] Brown, J. R., “A capacity constrained mathematical programming model for cellular manufacturing with exceptional elements”, Journal of Manufacturing Systems, 37: 227-232, (2015).
• [8] Bychkov, I., Batsyn, M., “An efficient exact model for the cell formation problem with a variable number of production cells”, Computers & Operations Research, 91: 112-120, (2018).
• [9] Sahin, Y. B., Alpay, S., “A New Mathematical Model for the Integrated Solution of Cell Formation and Part Scheduling Problem”, Gazi University Journal of Science, 32(4): 1196-1210, (2019).
• [10] Danilovic, M., Ilic, O., “A novel hybrid algorithm for manufacturing cell formation problem”, Expert Systems with Applications, 135: 327-350, (2019).
• [11] Sahin, Y. B., Alpay, S., “A metaheuristic approach for a cubic cell formation problem”, Expert Systems with Applications, 65: 40-51, (2016).
• [12] Firouzian, S., Mahdavi, I., Paydar, M. M., Saadat, M., “Simulated annealing and artificial immune system algorithms for cell formation with part family clustering”, Journal of Industrial Engineering and Management, 7(1): 191-219, (2020).
• [13] Feng, H., Da, W., Xi, L., Pan, E., Xia, T., “Solving the integrated cell formation and worker assignment problem using particle swarm optimization and linear programming”, Computers & Industrial Engineering, 110: 126-137, (2017).
• [14] Mahmoodian, V., Jabbarzadeh, A., Rezazadeh, H., Barzinpour, F., “A novel intelligent particle swarm optimization algorithm for solving cell formation problem”, Neural Computing and Applications, 31(2): 801-815, (2019).
• [15] Liu, C., Wang, J., Leung, J. Y. T., Li, K., “Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm”, International Journal of Production Research, 54(3): 923-944, (2016).
• [16] Hazarika, M., Laha, D., “Genetic algorithm approach for machine cell formation with alternative routings”, Materials Today, 5(1): 1766-1775, (2018).
• [17] Adinarayanan, A., Uthayakumar, M., Prabhakaran, G., “Machine cell formation using simulated annealing algorithm in cellular manufacturing system”, International Journal of Computer Aided Engineering and Technology, 10(1-2): 111-125, (2018).
• [18] Kamalakannan, R., Pandian, R. S., Sivakumar, P., “A simulated annealing for the cell formation problem with ratio level data”, International Journal of Enterprise Network Management, 10(1): 78-90, (2019).
• [19] Deep, K., “Machine cell formation for dynamic part population considering part operation trade-off and worker assignment using simulated annealing-based genetic algorithm”, European Journal of Industrial Engineering, 14(2): 189-216, (2020).
• [20] Ariafar, S., Firoozi, Z., Ismail, N., A triangular stochastic facility layout problem in a cellular manufacturing system. In international conference on Mathematical Sciences and Statistics, Springer, Singapore, 45-52, (2013).
• [21] Zohrevand, A. M., Rafiei, H., Zohrevand, A. H., “Multi-objective dynamic cell formation problem: A stochastic programming approach”, Computers & Industrial Engineering, 98: 323-332, (2016).
• [22] Islier, A., “Cellular Manufacturing Systems: Organization, Trends and Innovative Methods”, Alphanumeric Journal, 3(2): 13-26, (2015).
• [23] Min, H., Shin, D., “Simultaneous formation of machine and human cells in group technology: A multiple objective approach”, International Journal of Production Research, 31(10): 2307–2318, (1993).
• [24] Bootaki, B., Mahdavi, I., Paydar, M. M., “A hybrid GA-AUGMECON method to solve a cubic cell formation problem considering different worker skills”, Computers & Industrial Engineering, 75: 31-40, (2014).
• [25] Bootaki, B., Mahdavi, I., Paydar, M. M., “New bi-objective robust design-based utilisation towards dynamic cell formation problem with fuzzy random demands”, International Journal of Computer Integrated Manufacturing, 28(6): 577-592, (2015).
• [26] Delgoshaei, A., Ariffin, M. K. A., Ali, A., “A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS”, International Journal of Production Research, 55(4), 997-1039 (2017).
• [27] Goli, A., Tirkolaee, E. B., Aydın, N. S., “Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors”, IEEE Transactions on Fuzzy Systems, 29(12), 3686-3695 (2021).
• [28] Delgoshaei, A., Ali, A., “An applicable method for scheduling temporary and skilled-workers in dynamic cellular manufacturing systems using hybrid ant colony optimization and tabu search algorithms”, Journal of Industrial and Production Engineering, 34(6): 425-449, (2017).
• [29] Delgoshaei, A., Mirzazadeh, A., Ali, A., “A hybrid ant colony system and tabu search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs”, Brazilian Journal of Operations & Production Management, 15(4): 499-516, (2018).
• [30] Mahdavi, I., Aalaei, A., Paydar, M. M., Solimanpur, M., “Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems”, International Journal of Production Research, 49(21), 6517-6537, (2011).
• [31] Nouri, H., “Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system”, Applied Mathematical Modelling, 40(2): 1514-1531, (2016).
• [32] Bagheri, F., Safaei, A. S., Kermanshahi, M., Paydar, M. M., “Robust Design of Dynamic Cell Formation Problem Considering the Workers Interest”, International Journal of Engineering, 32(12): 1790-1797, (2019).
• [33] Niakan, F., Baboli, A., Moyaux, T., Botta-Genoulaz, V., “A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment”, Journal of Manufacturing Systems, 38: 46-62, (2016).
• [34] Mahdavi, I., Aalaei, A., Paydar, M. M., Solimanpur, M., “A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system”, Journal of Manufacturing Systems, 31(2): 214-223, (2012).
• [35] Mahdavi, I, Bootaki, B., Paydar, M. M., “Manufacturing Cell Configuration Considering Worker Interest Concept Applying a Bi-Objective Programming Approach”, International Journal of Industrial Engineering & Production Research, 25(1): 41-53, (2014).
• [36] Bouaziz, H., Berghida, M., Lemouari, A., “Solving the generalized cubic cell formation problem using discrete flower pollination algorithm”, Expert Systems with Applications, 113345, (2020).
• [37] Delgoshaei, A., Gomes, C., “A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost”, Applied Soft Computing, 49, 27-55, (2016).
• [38] Chen, A., Jiang, T., Chen, Z., Zhang, Y., “A genetic and simulated annealing combined algorithm for optimization of wideband antenna matching networks”, International Journal of Antennas and Propagation, 251624, (2012).
• [39] Delgoshaei, A., Ali, A., Ariffin, M. K. A., Gomes, C., “A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty”, Computers & Industrial Engineering, 100, 110-132, (2016).
• [40] Holland, J. H., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 1st ed., U Michagan Press, Oxford, England, (1992).
• [41] Gen, M., Cheng, R., Genetic Algorithms and Engineering Optimization, 1st ed., John Wiley and Sons, New York, 53-61, (2000).
• [42] Rabunal, J. R., Dorado, J., Artificial neural networks in real-life applications, 1st ed., IGI Global, United Kingdom, London, 105-112, (2006).
• [43] Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., “Optimization by simulated annealing”, Science, 220(4598): 671-680, (1983).
• [44] Sahab, M., Toropov, V., Gandomi, A., A Review on Traditional and Modern Structural Optimization 1st ed., Waltham, USA, 25-47, (2013).
• [45] Eglese, R. W., “Simulated annealing: a tool for operational research”, European Journal of Operational Research, 46(3): 271-281, (1990).
• [46] https://www.mathworks.com/help/gads/genetic-algorithm-options.html. Access date: 15.10.2021
• [47] Attar, S. F., Mohammadi, M., Tavakkoli-Moghaddam, R., “Hybrid flexible flowshop scheduling problem with unrelated parallel machines and limited waiting times”, International Journal of Advanced Manufacturing Technology, 68(5-8): 1583-1599, (2013).