Modeling and solving the multi-objective robust facilities layout under uncertainty with multi-objective meta-heuristic algorithms

Ghaseminejad, Amin; Fallah, Mohammad; Kazemipoor, Hamed

doi:10.22105/dmor.2021.289735.1418

Document Type : Original Article

Authors

¹ Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

² Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.22105/dmor.2021.289735.1418

Abstract

Purpose: The present paper deals with modeling and solving a multi-objective problem of robust facility layout problem under uncertainty with NSGA-II, MOPSO and MOGWO algorithms. Since the problem of facility layout is NP-Hard, the need to use meta-algorithms by providing a suitable chromosome to achieve near-optimal solutions has been investigated in this article. The issue under consideration in this article includes several departments that are based on 5 different aspects (minimizing the flow time between departments, maximizing the number of equipment and facilities, minimizing the distance traveled to access firefighting equipment, minimizing the distance to access optimal climatic conditions and maximization of noisy departments from each other) should be arranged in different parts of the hall. In order to achieve the above objective functions at the same time, assigning departments to each section, equipping each section with different equipments and arranging the departments together are among the main objectives of the article.
Methodology: In this paper, GA, PSO and GWO single-objective meta-heuristic algorithms and NSGA-II, MOPSO and MOGWO multi-objective meta-heuristic algorithms have been used to solve the problem.
Findings: Computational results show that GA, PSO and GWO single-objective algorithms have high efficiency in achieving the optimal value of the objective function in a much shorter time, and their multi-objective methods show the high efficiency of the NSGA-II algorithm in achieving the average value of the objective function. First, second and fifth; the MOPSO algorithm has the highest expansion and metric distance in achieving the average number of efficient answers and computational time, and finally the MOGWO algorithm in obtaining the average value of the third and fourth objective functions. Statistical comparisons also showed a significant difference between the means of computational time. To evaluate and rank the algorithms, the TOPSIS method is used and the results show the high efficiency of the MOGWO algorithm in solving the model.
Originality/Value: In this paper, a new model of the multi-objective robust facility layout problem under uncertainty conditions is modeled with respect to health and environmental safety aspects.

Keywords

20.1001.1.25385097.1402.8.5.11.1

Main Subjects

Robust optimization

References

[1] Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B., & Safaei, N. (2007). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied mathematics and computation, 184(2), 721–728.

[2] Jafari, H., & Sheykhan, A. (2021). Using a new algorithm to improve the search answer in quadratic assignment problem (QAP). International journal of research in industrial engineering, 10(2), 165–173.

[3] McKendall Jr, A. R., & Shang, J. (2006). Hybrid ant systems for the dynamic facility layout problem. Computers & operations research, 33(3), 790–803.

[4] Aiello, G., La Scalia, G., & Enea, M. (2012). A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding. Expert systems with applications, 39(12), 10352–10358. DOI:10.1016/j.eswa.2012.01.125

[5] Pourvaziri, H., Pierreval, H., & Marian, H. (2021). Integrating facility layout design and aisle structure in manufacturing systems: formulation and exact solution. European journal of operational research, 290(2), 499–513. DOI:10.1016/j.ejor.2020.08.012

[6] Anjos, M. F., & Vieira, M. V. C. (2017). Mathematical optimization approaches for facility layout problems: the state-of-the-art and future research directions. European journal of operational research, 261(1), 1–16. DOI:10.1016/j.ejor.2017.01.049

[7] Allahyari, M. Z., & Azab, A. (2018). Mathematical modeling and multi-start search simulated annealing for unequal-area facility layout problem. Expert systems with applications, 91, 46–62.

[8] Liu, S., Zhang, Z., Guan, C., Zhu, L., Zhang, M., & Guo, P. (2021). An improved fireworks algorithm for the constrained single-row facility layout problem. International journal of production research, 59(8), 2309–2327.

[9] Ghahremani-Nahr, J., Nozari, H., & Bathaee, M. (2021). Robust box approach for blood supply chain network design under uncertainty: hybrid moth-flame optimization and genetic algorithm. International journal of innovation in engineering, 1(2), 40–62.

[10] Anjos, M. F., & Vieira, M. V. C. (2021). Mathematical optimization approach for facility layout on several rows. Optimization letters, 15(1), 9–23.

[11] Drira, A., Pierreval, H., & Hajri-Gabouj, S. (2007). Facility layout problems: a survey. Annual reviews in control, 31(2), 255–267.

[12] Samarghandi, H., & Eshghi, K. (2010). An efficient tabu algorithm for the single row facility layout problem. European journal of operational research, 205(1), 98–105.

[13] Jiang, S., & Nee, A. Y. C. (2013). A novel facility layout planning and optimization methodology. CIRP annals - manufacturing technology, 62(1), 483–486. DOI:10.1016/j.cirp.2013.03.133

[14] Xu, J., & Song, X. (2015). Multi-objective dynamic layout problem for temporary construction facilities with unequal-area departments under fuzzy random environment. Knowledge-based systems, 81, 30–45.

[15] Wang, S., Zuo, X., Liu, X., Zhao, X., & Li, J. (2015). Solving dynamic double row layout problem via combining simulated annealing and mathematical programming. Applied soft computing, 37, 303–310.

[16] Ulutas, B., & Islier, A. A. (2015). Dynamic facility layout problem in footwear industry. Journal of manufacturing systems, 36, 55–61. DOI:10.1016/j.jmsy.2015.03.004

[17] Azevedo, M. M., Crispim, J. A., & de Sousa, J. P. (2017). A dynamic multi-objective approach for the reconfigurable multi-facility layout problem. Journal of manufacturing systems, 42, 140–152.

[18] Paes, F. G., Pessoa, A. A., & Vidal, T. (2017). A hybrid genetic algorithm with decomposition phases for the unequal area facility layout problem. European journal of operational research, 256(3), 742–756.

[19] Liu, J., Wang, D., He, K., & Xue, Y. (2017). Combining Wang–Landau sampling algorithm and heuristics for solving the unequal-area dynamic facility layout problem. European journal of operational research, 262(3), 1052–1063. DOI:10.1016/j.ejor.2017.04.002

[20] Turanoğlu, B., & Akkaya, G. (2018). A new hybrid heuristic algorithm based on bacterial foraging optimization for the dynamic facility layout problem. Expert systems with applications, 98, 93–104.

[21] Guan, C., Zhang, Z., Liu, S., & Gong, J. (2019). Multi-objective particle swarm optimization for multi-workshop facility layout problem. Journal of manufacturing systems, 53, 32–48.

[22] García-Hernández, L., Salas-Morera, L., Garcia-Hernandez, J. A., Salcedo-Sanz, S., & de Oliveira, J. V. (2019). Applying the coral reefs optimization algorithm for solving unequal area facility layout problems. Expert systems with applications, 138, 112819.

[23] Liu, J., & Liu, J. (2019). Applying multi-objective ant colony optimization algorithm for solving the unequal area facility layout problems. Applied soft computing, 74, 167–189.

[24] García-Hernández, L., Salas-Morera, L., Carmona-Muñoz, C., Garcia-Hernandez, J. A., & Salcedo-Sanz, S. (2020). A novel island model based on coral reefs optimization algorithm for solving the unequal area facility layout problem. Engineering applications of artificial intelligence, 89, 103445.

[25] Dahlbeck, M. (2021). A mixed-integer linear programming approach for the T-row and the multi-bay facility layout problem. European journal of operational research, 295(2), 443–462.

[26] Ahmadi-Javid, A., & Ardestani-Jaafari, A. (2021). The unequal area facility layout problem with shortest single-loop AGV path: how material handling method matters. International journal of production research, 59(8), 2352–2374. DOI:10.1080/00207543.2020.1733124

[27] Ghahremani Nahr, J., & Bathaee, M. (2021). Design of a humanitarian logistics network considering the purchase contract. Journal of decisions and operations research, 6(3), 423–444.

[28] Nozari, H., Najafi, E., Fallah, M., & Hosseinzadeh Lotfi, F. (2019). Quantitative analysis of key performance indicators of green supply chain in FMCG industries using non-linear fuzzy method. Mathematics, 7(11), 1020. https://doi.org/10.3390/math7111020

[29] Nory, F., & Ghahremani Nahr, J. (2019). Robust-possibilistic optimization method at design of a pharmaceutical supply chain network under uncertainty and discount on purchase the raw material. Journal of modeling in engineering, 17(58), 249–266. (In Persian). https://modelling.semnan.ac.ir/article_4015_en.html?lang=fa

[30] Ghahremani Nahr, J. (2020). Improvement the efficiency and efficiency of the closed loop supply chain: whale optimization algorithm and novel priority-based encoding approach. Journal of decisions and operations research, 4(4), 299–315. (In Persian). https://www.journal-dmor.ir/article_103943_en.html?lang=fa

Journal of Decisions and Operations Research

Modeling and solving the multi-objective robust facilities layout under uncertainty with multi-objective meta-heuristic algorithms

References

References

Volume 8, Issue 1
June 2023
Pages 196-223

Modeling and solving the multi-objective robust facilities layout under uncertainty with multi-objective meta-heuristic algorithms

References

References

Volume 8, Issue 1June 2023Pages 196-223

Volume 8, Issue 1
June 2023
Pages 196-223