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Mehdizadeh, Esmaeil, Ghazizadeh, Rasa. (1395). Solving a Fuzzy Multi-objective Aggregate Production Planning Model with Learning and Deterioration Effects by Using Genetic and Tabu Search Algorithms. , 50(2), 341-354. doi: 10.22059/jieng.2016.60734
Esmaeil Mehdizadeh; Rasa Ghazizadeh. "Solving a Fuzzy Multi-objective Aggregate Production Planning Model with Learning and Deterioration Effects by Using Genetic and Tabu Search Algorithms". , 50, 2, 1395, 341-354. doi: 10.22059/jieng.2016.60734
Mehdizadeh, Esmaeil, Ghazizadeh, Rasa. (1395). 'Solving a Fuzzy Multi-objective Aggregate Production Planning Model with Learning and Deterioration Effects by Using Genetic and Tabu Search Algorithms', , 50(2), pp. 341-354. doi: 10.22059/jieng.2016.60734
Mehdizadeh, Esmaeil, Ghazizadeh, Rasa. Solving a Fuzzy Multi-objective Aggregate Production Planning Model with Learning and Deterioration Effects by Using Genetic and Tabu Search Algorithms. , 1395; 50(2): 341-354. doi: 10.22059/jieng.2016.60734

Solving a Fuzzy Multi-objective Aggregate Production Planning Model with Learning and Deterioration Effects by Using Genetic and Tabu Search Algorithms

Advances in Industrial Engineering
مقاله 14، دوره 50، شماره 2، دی 2016، صفحه 341-354    اصل مقاله (738.88 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2016.60734
نویسندگان
Esmaeil Mehdizadeh* ؛ Rasa Ghazizadeh
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Iran
چکیده
In this paper a non linear integrated fuzzy multi-objective production planning model with the labor learning and machines deterioration effects is presented. The objective function consists of two quantitative objectives namely increase profits and reduces the cost of system failure and a qualitative objective namely increases the satisfaction rate of the customers. Different weights for objectives and modification of the objectives by using fuzzy goal programming method are considered to convert the fuzzy multi-objective model to a deterministic single-objective model and  the obtained model is solved by Genetic algorithm and Tabu search algorithm. Finally, the solution obtained from two algorithms compared together by using hypothesis test of equality of means. Experimental results show the proposed Genetic algorithm for solving the model has higher performance than the Tabu search algorithm.
کلیدواژه‌ها
Aggregate production planning؛ Fuzzy goal programming؛ Genetic Algorithm؛ Multi-Objective Programming؛ Tabu search algorithm
عنوان مقاله [English]
حل مدل برنامه‌ریزی تولید ادغامی چندهدفۀ فازی با آثار یادگیری و زوال به کمک الگوریتم‌های ژنتیک و جست‌وجوی ممنوعه
نویسندگان [English]
اسماعیل مهدی زاده؛ رسا قاضی زاده
دانشیار مهندسی صنایع، دانشکدة مهندسی صنایع و مکانیک، دانشگاه آزاد اسلامی واحد قزوین
چکیده [English]
در این پژوهش، یک مدل برنامه‌ریزی تولید ادغامی چندهدفة فازی با درنظرگرفتن دو عامل اثر یادگیری کارگران و اثر زوال ماشین‌آلات ارائه می‌شود. توابع هدف شامل اهداف کمی افزایش سود و کاهش هزینة خرابی دستگاه‌ها و هدف کیفی افزایش میزان رضایتمندی مشتری هستند. سپس با درنظرگرفتن اوزان متفاوت برای اهداف و اصلاح اهداف با روش برنامه‌ریزی آرمانی فازی، مدل چندهدفة فازی به یک مدل تک‌هدفة قطعی تبدیل شده و با الگوریتم‌های ژنتیک و جست‌وجوی ممنوعه حل شده است. در تنظیم پارامترهای دو الگوریتم از روش تاگوچی بهره گرفته می‌شود. در پایان، جواب به‌دست‌آمده از دو الگوریتم با استفاده از آزمون فرض برابری میانگین‌ها با هم مقایسه می‌شوند. نتایج نشان می‌دهند الگوریتم ژنتیک در حل مدل ارائه‌شده نسبت به الگوریتم جست‌وجوی ممنوعه کارایی بیشتری دارد.
کلیدواژه‌ها [English]
الگوریتم جست‌وجوی ممنوعه, الگوریتم ژنتیک, برنامه‌ریزی آرمانی فازی, برنامه‌ریزی تولید ادغامی, برنامه‌ریزی چندهدفه
مراجع
  1. Gen. M, Tsujimura. Y and Ida. K. (1992). “Method for solving multi-objective aggregate production planning problem with fuzzy parameters”, Computers and Industrial Engineering, Vol. 23, No. I- 4, PP. 117- 120.
  2. Wang, R. C. and Fang, H. H. (2001). “Aggregate production planning with multiple objectives in a fuzzy environment”, European journal of operational research, Vol. 133, No. 3, PP. 521- 536.
  3. Wang, R. Chen, Liang, T. F. (2004). “Application of fuzzy multi-objective linear programming to aggregate production planning”, Computers & Industrial Engineering, Vol. 46, No. 1, PP. 4617– 41.
  4. Wang, R. C. and Liang, T. F. (2005). “Applying possibilistic linear programming to aggregate production planning”, International Journal of Production Economics, Vol. 98, No. 3, PP. 328- 341.
  5. Chao, F. H., Chang-J. T. and Shao Y. L. (2007). “A fuzzy goal programming approach to multi-objective optimization problem with priorities”, European Journal of Operational Research, Vol. 176, No. 3, PP. 1319–1333.
  6. Shaoyuan, L. and Chaofang, H. (2009). “Satisfying optimization method based on goal programming for fuzzy multiple objective optimization problems”, Europe an Journal of Operational Research, Vol. 197, No. 2, PP. 675– 684.
  7. Jamalnia, A. and Soukhakian, M. A. (2009). “A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning”, Computers &Industrial Engineering, Vol. 56, No. 4, PP. 1474–1486.
  8. Ozcana, U. and Toklu, B. (2009). “Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models”, Computers &Operations Research, Vol. 36, No. 6, PP. 1955 – 1965.
  9. Baykasoglu, A. and Gocken, T. (2010). “Multi-objective aggregate production planning with fuzzy parameters”, Advances in Engineering Software, Vol. 41, No. 9, PP. 1124– 1131.
  10. Phruksaphanrat, B. (2011). “Preemptive possibilistic linear programming: Application to aggregate production planning”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, Vol. 5, No. 8, PP. 1592- 1599.
  11. Ramezanian, R. Rahmani, D. and Barzinpour, F. (2012). “An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabusearch”, Expert Systems with Applications, Vol. 39, No. 1, PP. 1256– 1263.
  12. Hung, Y. F. and Hu, Y. C. (1998). “Solving mixed integer programming production planning problems with setups by shadow price information”, Computers and Operations Research, Vol. 25, No. 12, PP. 1027– 1042.
  13. Mortezaei, N., Zulkifli, N., Hong, T. S. and Yusuff, R. M. (2013). “Multi-objective aggregate production planning model with fuzzy parameters and its solving methods”, Life Science Journal, Vol. 10, No. 4, PP. 2406- 2414.
  14. Kaveh, K. D. and Ayda, S. (2014). “Solving a new multi-period multi-objective multi-product aggregate production planning problem using fuzzy goal programming”, Industrial Engineering and Management Systems, Vol. 13, No. 4, PP. 369- 382.
  15. Madadi, N. and Wong, K. Y. (2014). “A multi objective fuzzy aggregate production planning model considering real capacity and quality of products”, Mathematical Problems in Engineering, Vol. 2014, Article ID 313829, 15 pages, doi:10.1155/2014/313829.
  16. Gholamian, N., Mahdavi I., Tavakkoli-Moghaddam R. and  Mahdavi-Amiri, N. (2015). “Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty”, Applied Soft Computing, Vol. 37, PP. 585– 607.
  17. Chen Z. and Sarker B. R. (2015). “Aggregate production planning with learning effect and uncertain demand: A case based study”, Journal of Modelling in Management, Vol. 10, No. 3, PP. 296– 324.
  18. Aneirson, F. Da S. and Fernando, A. S. M. (2014). “A fuzzy goal programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill”, Energy Economics, Vol. 45, , PP. 196– 204.
  19. Azadeh, A., Habibnejad-Ledari, H., Abdolhossein Zadeh, S. and Hosseinabadi Farahani, M. (2017). “A single-machine scheduling problem with learning effect, deterioration and non-monotonic time-dependent processing times”, International Journal of Computer Integrated Manufacturing, Vol. 30, No.2-3, PP. 292-304.
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