نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار، دانشگاه پیام نور.
2 کارشناس ارشد، دانشگاه پیام نور.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
This paper aims at proposing a novel quadratic assignment-based mathematical model for designing an optimal facility layout in each period of the stochastic dynamic facility layout problem (SDFLP). Considering routing flexibility is the main assumption of this problem so that parts can pass through multiple routes. It is also assumed that product demands are independent, normally distributed random variables with known expected value and variance changing from period to period at random. In addition, to solve the proposed model, a new hybrid meta-heuristic algorithm is developed by combining simulated annealing (SA) and the CRAFT approaches. Finally, the proposed model and the hybrid algorithm are verified and validated using design of experiment, real case study and sensitivity analysis methods as well as solving some numerical examples.The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time perspectives. Moreover, the proposed model can be used to design the layout of facilities in both of the stochastic and deterministic environments of traditional and modern manufacturing systems.
کلیدواژهها [English]
10. Eslaminia, A., & Azimi, P. (2020). Solving the Electric Vehicle Routing Problem Considering the Vehicle Volume Limitation Using a Simulated Annealing Algorithm. Journal of Industrial Management Perspective, 36, 165-188. (In Persian).
11. Fazlelahi, F. Z., Pournader, M., Gharakhani, M., & Sadjadi. (2015). A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm. Journal of Engineering Manufacture, 230(12), 2264-2274.
12. Forghani, K., Mohammadi, M., & Ghezavati, V. (2013). Designing robust layout in cellular manufacturing systems with uncertain demands. International Journal of Industrial Engineering Computations, 4(2), 215-226.
13. Groover, M. P. (2008). Automation, production systems, and Computer-Integrated manufacturing. New Jersey: Pearson Education Inc.
14. Ghadirpour, S. M., Rahmani, D., Moslemipour, G. (2020). Routing flexibility for unequal–area stochastic dynamic facility layout problem in flexible manufacturing systems. International Journal of Industrial Engineering & Production Research, 31(2), 269-285.
15. Jithavech, I., & Krishnan, K. (2010). A simulation-based approach for risk assessment of facility layout designs under stochastic product demands. International Journal of Advanced Manufaturing Technology, 49, 27-40.
16. Krishnan, K. K., Cheraghi, S. H., & Nayak, C. N. (2006). Dynamic From-Between Chart: a new tool for solving dynamic facility layout problems. International Journal of Industrial and Systems Engineering, 1(1/2), 182-200.
17. Krishnan, K. K., Cheraghi, S., & Nayak, C. (2008). Dynamic facility layout design for multiple production scenarios in a dynamic environment. International Journal of Industrial and Systems Engineering, 3(2), 105-133.
18. Kirkpatrick, S., & al, e. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.
19. Lee, H. Y., Kang, S., & Chae, J. (2015). Mutation effects in a genetic algorithm for a facility layout problem in QAP form. International Journal of Advanced Logistics, 4(3), 170-179.
20. Lee, T. S., Moslemipour, G., Ting, T. O., & Rilling, D. (2012). A Novel Hybrid ACO/SA Approach to Solve Stochastic Dynamic Facility Layout Problem (SDFLP). Communication in Computer and Information Science, special issue: Emerging Intelligent Computing Technology and Applications, 304, 100-108.
21. Lee, T., Moslemipour, G., (2012). Intelligent design of a flexible cell layout with maximum stability in a stochastic dynamic situation. Trends in intelligent robotics, automation, and manufacturing. Springer, 398-405.
22. Misevicius, A. (2003). A modified simulated annealing algorithm for quadratic assignment problem. Informatica, 14, 497-514.
23. Montreuil, B., & LaForge, A. (1992). Dynamic layout design given a scenario tree of probable futures. European Journal of Operational Research, 63(2), 271-286.
24. Moslemipour, G. (2017).Robust inter and intra-cell layouts design model dealing with stochastic dynamic problems. Journal of Industrial and Systems Engineering, 10(4), 123-40.
25. Moslemipour, G. (2016). Dynamic intracellular layout design using simulated annealing algorithm in random environment of cellular manufacturing systems. National Conference of decision making in engineering and management. Aliabad Katool. COI in civilica: EMDM01_100. (In Persian).
26. Moslemipour, G., Lee, T. S. (2012). Intelligent design of a dynamic machine layout in uncertain environment of flexible manufacturing systems. International Journal of Flexibility Manufacturing System, 1849-1860.
27. Moslemipour, G., Lee, T. S., & Rilling, D. (2012). A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems. International Journal of Advanced Manufaturing Technology, 60, 11-27.
28. Moslemipour, G., Lee, T. S., & Loong, Y.T. (2018).Solving stochastic dynamic facility layout problems using proposed hybrid AC-CS-SA meta-heuristic algorithm.Int. J. Industrial and Systems Engineering, 28(1), 1-31.
29. Nematian, J. (2014). A robust single row facility layout problem with fuzzy random variables. International Journal of Advanced Manufaturing Technology, 72, 255–267.
30. Palekar US et al. (1992). Modeling uncertainties in plant layout problems. European Journal of Operational Research, 63, 347-359.
31. Pourvaziri, H., & Naderib, B. (2014). A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Applied Soft Computing, 24, 457–469.
32. Sahni, S., & Gonzalez, T. (1976). P-complete approximation problem. Journal of the ACM, 23(3), 555-565.
33. Vitayasak, S., Pongcharoen, P., & Hicks, C. (2019). Robust machine layout design under dynamic environment: Dynamic customer demand and machine maintenance.Expert Systems with Applications, doi.org/10.1016/j.eswax.2019.10 0 015.
34. Sheikh, R., & Shambiati, H. (2016). Facility Location Problem in uncertainty conditions based on D numbers. Journal of Industrial Management Perspective, 20, 143-166. (In Persian).
35. Suman, B., & Kumar, P. (2006). A Survey of simulated annealing as a tool for single and multiobjective optimization. Operational Research Society, 57(10), 1143-1160.
36. Tayal, A. & Singh, S.P. (2017). Integrated SA-DEA-TOPSIS-based solution approach for multi-objective stochastic dynamic facility layout problem, International Journal of Business and Systems Research, 11(1/2), 82–100.
37. Tayal, A., & Singh, S. (2014). Chaotic Simulated Annealing for Solving Stochastic Dynamic Facility Layout Problem. Journal of International Management Studies, 14(2), 67-74.
38. 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.
39. Tompkins, J., White, J., Bozer, Y., Frazelle, E., Tanchoco, J., & Trevino, J. (2010). Facility planning 4th Edition. NY: John Wiley & Sons, New York, Inc.
40. Vitayasak, S., Pongcharoen, P., & Chris Hicks, C. (2016). A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm. International Journal of Production Economics, 190, 146-157.