مدل ترکیبی جدید بر پایه الگوریتم ژنتیک و بهینه سازی ازدحام ذرات با هدف بهینه سازی زمان‌بندی نگهداری و تعمیرات

نوع مقاله: مقاله پژوهشی

نویسندگان

1 پژوهشگر، گروه علمی مهندسی مکانیک، دانشگاه جامع امام حسین (ع)، تهران، ایران.

2 پژوهشگر، گروه علمی مهندسی صنایع، دانشگاه جامع امام حسین (ع)، تهران، ایران

3 مربی و پژوهشگر، گروه علمی مهندسی نت و بهینه‌سازی انرژی، دانشگاه افسری و تربیت پاسداری امام حسین (ع)، تهران، ایران.

dmej/dmej.2020.643.1006

چکیده

با توجه به توسعه صنایع و شرکت­ها، تعداد و ابعاد ساختمان­ها و تسهیلات در سازمان­ها در حال افزایش می­باشد که این امر موجب افزایش ارزش این دارایی­ها و همچنین افزایش اهمیت نگهداری و تعمیرات این تسهیلات شده است. از طرف دیگر، با افزایش تعداد دارایی­ها، فرآیند مدیریت­ نگهداری و تعمیرات بسیار پیچیده­تر از گذشته شده است. بدین منظور در این تحقیق مدلی برای زمان­بندی نگهداری و تعمیرات، باهدف تعیین زمان بهینه و تعیین برنامه نگهداری و تعمیرات تعریف‌شده است. بدین منظور در این تحقیق، مدلی جدید از ادغام الگوریتم ژنتیک و بهینه­سازی ازدحام ذرات برای زمان­بندی نگهداری و تعمیرات سازمان­هایی با چندین مکان­ مختلف ارائه‌شده است. نوآوری اصلی این تحقیق عبارت‌اند از: (آ) تعریف مسئله اصلی در مکان­های چندتایی و تعیین زمان­های سفر بین هر مکان (ب) تعیین مهارت­های کاری مختلف برای طرح­ریزی نگهداری تعمیرات. (پ) تعیین احتمال برون‌سپاری هر فعالیت. همچنین 10 سناریو برای تعداد تسهیلات، 3 سناریو برای تعداد متخصصان در هر مکان و 2 سناریو برای انواع هزینه­ها در نظر گرفته‌شده است. نتایج حاصل از این تحقیق نشان می­دهد که مدل پیشنهادی در این تحقیق، در مقایسه با سه مدل مختلف ارائه‌شده توسط گالپیرس[1]، کوآی[2] و جوانمرد عملکرد بهتری داشته و نتایج نشان می­دهد که مدل پیشنهادی می­تواند منجر به کاهش 34 درصدی در هزینه­ها شود.



[1].Gulpiras


[2]. Koay

کلیدواژه‌ها


عنوان مقاله [English]

A new hybrid model based on GA and PSO to the optimization of facility maintenance scheduling for organizations with assets in multiple sites

نویسندگان [English]

  • Javad Gholami 1
  • Seyed Ahmad Razavi Al_e_Hashem 2
  • Seyed Abbas Safavi 3
1 Researcher, Department of Mechanical Engineering, Imam Hossein Comprehensive University.
2 Researcher, Department of Industrial engineering, Imam Hossein Comprehensive University.
3 Instructor and Researcher,Faculty of Defense Science & Engineering, Imam Hussein Officers and Guard Training University, Tehran, Iran.
چکیده [English]

With the expansion of industries and companies, the number and dimensions of buildings and facilities of organizations are increasing, which increases the value of these assets, increasing the importance of maintenance and repairs of these facilities. On the other hand, as the number of assets increases, the process of managing their maintenance and repairs becomes more difficult than in the past. It is better to define a model for scheduling the maintenance, to proposed the optimal time and order of maintenance program. In this paper, by the combination of Genetic algorithm (GA) and Particle Swarm Optimizations (PSO), a new model for scheduling maintenance for organizations with different locations is presented. The main contributions of this paper are (a)        Definition of the main problem in multiple locations and consideration of travel times between each location. (b) Consideration of different work skills for maintenance plan. (c) Consideration of the possibility of outsourcing each task. Considering 10 scenarios for the number of facilities, 3 scenarios for the number of specialists in each location and 2 scenarios for the type of costs, the results show the better performance of the proposed model in compare of three different models as Gulpiras model, Koay’s Model, and Javanmard’s model, where the results have been shown, by using the proposed model, it is possible to reduce costs just over by 34%.

کلیدواژه‌ها [English]

  • Maintenance
  • Scheduling
  • Genetic Algorithm
  • Particle Swarm Optimizations
Grussing, M.N. and L.R. Marrano, Building component lifecycle repair/replacement model for institutional facility management, in Computing in Civil Engineering (2007). 2007. p. 550-557.

Olanrewaju, A.L. A. Idrus, and M.F. Khamidi, Investigating building maintenance practices in Malaysia: a case study. Structural Survey, 2011.

Ighravwe, D.E. and S.A. Oke, A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria. Journal of Building Engineering, 2019. 24: p. 100753.

Lee, H. and D. Scott, Development of a conceptual framework for the study of building maintenance operation processes in the context of facility management. Surveying and Built Environment, 2008. 19(1): p. 81-101.

Poór, P. N. Kuchtová, and M. Šimon, Machinery maintenance as part of facility management. Procedia Engineering, 2014. 69: p. 1276-1280.

Lavy, S. and I.M. Shohet, Integrated maintenance management of hospital buildings: a case study. Construction Management and Economics, 2004. 22(1): p. 25-34.

Lind, H. and H. Muyingo, Building maintenance strategies: planning under uncertainty. Property Management, 2012.

Chen, C. and L. Tang, BIM-based integrated management workflow design for schedule and cost planning of building fabric maintenance. Automation in Construction, 2019. 107: p. 102944.

Institution, B.S. Glossary of maintenance management terms in terotechnology. 1984.

Manzini, R. et al. The scheduling of maintenance. A resource-constraints mixed integer linear programming model. Computers & Industrial Engineering, 2015. 87: p. 561-568.

Duffuaa, S.O. and K. Al-Sultan, A stochastic programming model for scheduling maintenance personnel. Applied Mathematical Modelling, 1999. 23(5): p. 385-397.

de Jonge, B. and P.A. Scarf, A review on maintenance optimization. European Journal of Operational Research, 2019.

Chung, S.H. F.T. Chan, and H.K. Chan, A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling. Engineering Applications of Artificial Intelligence, 2009. 22(7): p. 1005-1014.

Abdollahzadeh, H. K. Atashgar, and M. Abbasi, Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups. Renewable Energy, 2016. 88: p. 247-261.

Wang, Y. H. Zuo, and D. Lv. Improved Multiobjective Maintenance Optimization of Aircraft Equipment Using Strength Pareto Genetic Algorithms with Immunity. in 2008 Fourth International Conference on Natural Computation. 2008. IEEE.

Busacca, P.G. M. Marseguerra, and E. Zio. Application of Genetic Algorithms to the Multi-Objective Optimization of the Inspection Times of a Safety System of a Pressurized Water Reactor. in Proceedings of the European Safety & Reliability International Conference (ESREL’2001). 2001.

Tan, Z. et al. An evaluation of maintenance strategy using risk based inspection. Safety science, 2011. 49(6): p. 852-860.

Siddiqui, M.A. et al. A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques. Mathematical Problems in Engineering, 2017. 2017.

Ding, S.H. and S. Kamaruddin, Maintenance policy optimization—literature review and directions. The International Journal of Advanced Manufacturing Technology, 2015. 76(5-8): p. 1263-1283.

Chikezie, C.U. A.T. Olowosulu, and O.S. Abejide, Multiobjective optimization for pavement maintenance and rehabilitation programming using genetic algorithms. Arch Appl Sci Res, 2013. 5(4): p. 76-83.

Garg, H., Fuzzy multiobjective reliability optimization problem of industrial systems using particle swarm optimization. Journal of Industrial Mathematics, 2013. 2013.

Deb, K. and S. Karthik. Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. in International conference on evolutionary multi-criterion optimization. 2007. Springer.