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  • طراحی یک مدل تولید کارگاهی سبز، با ایجاد توازن بین زمان تکمیل و مصرف انرژی (بررسی موردی: شرکت پدیده ماشین سازی غرب)

اشتراک گذاری

آدرس مقاله


کد مقاله : IMJ-2004-1365 (R2) بازدید : 179 صفحه: 26 - 52

نوع مقاله: پژوهشی

طراحی یک مدل تولید کارگاهی سبز، با ایجاد توازن بین زمان تکمیل و مصرف انرژی (بررسی موردی: شرکت پدیده ماشین سازی غرب)

محورهای موضوعی : مدیریت صنعتی

Maryam Rahimi alloghareh 1 , sayyed mohammad reza davoodi 2

1 - Master of Industrial Management, Production and Operational Orientation, Amin Institute of Higher Education, Foolad Shahr, Isfahan.
2 - department of management

تاریخ دریافت : 1399/01/28 تاریخ پذیرش : 1399/08/17 تاریخ انتشار : 1399/08/19

کلید واژه: پایداری, تولید, انرژی, زمان‌بندی سبز, زنجیره تأمین,

چکیده مقاله :

هدف تحقیق حاضر زمان‌بندی سبز در تولید کارگاهی دو ماشینه با موازنه نمودن زمان تکمیل و مصرف انرژی می‌باشد. روش تحقیق از نوع توصیفی ‌‌‌_تحلیلی و بر حسب هدف کاربردی است. این تحقیق به بررسی عملکرد مسئله زمان‌بندی فلوشاپ چند ماشین با در نظر گرفتن توابع هدف کمینه‌سازی زمان اتمام کل، مصرف انرژی و مجموع وزنی دیرکرد و زودکرد کارها پرداخته است. داده‌های مورد نیاز از طریق مصاحبه و اطلاعات موجود در شرکت پدیده ماشین‌سازی غرب گردآوری و سپس در نرم‌افزار متلب پیاده‌سازی شدند. تعداد 30 مسئله با ابعاد مختلف و براساس شیوه‌های رایج تولید و با الگوریتم فراابتکاری چندهدفه (NSGA-II) و الگوریتم تکاملی چندهدفه‌ی بهینه‌سازی ازدحام ذرات (MOPSO) مورد ارزیابی قرار گرفت. از سه معیار مقایسه MID، MS و SNS در کنار معیار زمان حل برای مقایسات حالات مختلف الگوریتم‌ها بهره‌گرفته شد. در نظر گرفتن ملاحظات پایداری در مسئله زمان‌بندی تولید و ساخت با کمینه کردن مصرف انرژی به عنوان یک معیار در برنامه‌ریزی کارگاهی در این پژوهش مورد توجه قرار گرفته است. این امر علاوه بر مزایای اقتصادی با کاهش انتشار کربن، به محیط زیست کمک شایانی می‌نماید. نتایج حاصل از مقایسه دو الگوریتم با استفاده از دو روش تحلیل سلسله مراتبی و تاپسیس نشان داد خروجی حاصل از مقایسه الگوریتم NSGA-II نسبت به الگوریتم MOPSO، برای این مسائل عملکرد بهتری دارد.

چکیده انگلیسی:

The purpose of this study was to evaluate the green timing in the production of two workshops by balancing the completion time and energy consumption. The research method is descriptive-analytic and also applicable to the purpose. This research has been designed to evaluate the performance of solving time-varying multi-machine flow shop problem considering the objective functions of minimizing total finishing time energy consumption, and the total weighted latency and early maturity of the work. Data were gathered through interviews and information from Padideh mashinsazi Gharb company and then implemented in MATLAB software. A total of 30 problems were produced with different dimensions based on commonly used methods and were evaluated by Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multiple Objective Particle Swarm Optimization algorithms (MOPSO). Three criteria for comparing MID, MS, and SNS were used along with the solution time to compare different states of the algorithm. Considering sustainability considerations in the timing of production and construction, in addition to the economic benefits of reducing carbon emissions, it helps the environment. The results of the comparison of the two algorithms using two hierarchical and tops analysis methods showed that the outputs from the comparison of the NSGA-II algorithm with the MOPSO algorithm are better for these problems.

منابع و مأخذ:


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  33. Verghese, K., & Lewis, H. (2007). Environmental innovation in industrial packaging a supply chain approach. International Journal of Production Research, 45, 4381-4401.
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    35.
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    36.
_||_

  1. Alam Tabriz, A, Modarressi, M and Arab, A. (2017). Study and analysis of sustainable supply chain management risks based on FSWARA method, 2nd International Conference on Industrial Management, Babolsar, Mazandaran University.
    2.
  2. Ali Nejad, AS; Shahriari, Z; Seyed Rahmati, H.; Simiari, K. (2015). Dynamic multi-facility location in a supply chain in fuzzy conditions. Industrial Management Studies, 12(35), 151-178.
    3.
  3. Azar A, Hashemi M. (2016). Providing A Method To Assess the Assaluyeh Petrochemical Plants Green Supply Chain Performance By Using a Combination of Fuzzy Method and Nonlinear Modeling. QEER, 12 (48), 173-194.
    4.
  4. Azimi Fard, A., Mousavi Rad, Sa. and Ariafard, Sh. (2017). Prioritization of Sustainable Green Supply Chain Criteria in Steel Industry, 4th International Conference on Environmental Planning and Management, Tehran, Faculty of Environment, University of Tehran.
    5.
  5. Beheshtinia, M.A., Akbari, E.(2015). Rescheduling Of Three-Stage Supply Chain with a Focus on Integration of the Stages. Journal of Industrial Engineering Research in Production Systems (Ierps) 3 (6), 191-205.
    6.
  6. Cabral, I., Espadinha-Cruz, P., & Grilo, A. (2011). Decision- making Models for Interoperable Lean, Agile, Resilient and Green supply chains. Proceedings of the International Symposium on the Analyitc Hierarchy Process.
    7.
  7. Chen, J. (2009). Performance Evaluation of Green Supply Chain Based on Entropy Weight Grey System Model. IEE, 474-478.
    8.
  8. Cheng, C. B., Cheng, C. J. (2011). Available-to-promise based bidding decision by fuzzy mathematical programming and genetic algorithm. Computers & Industrial Engineering,61(4), 993-1002.
    9.
  9. Diabat, A., Abdallah, T., Al-Refaie A., Svetinovic, D & Govindan K. (2013). Strategic closed- loop facility location problem with carbon market trading. IEEE Transactions on Engineering Management, 60 (2), 398-408.
    10.
  10. Etrazian, F., Kharazian Akhavan, M. and Barati, M. (2016). Investigating Technological Barriers in Implementing Green Supply Chain Management in Iran's Oil Refining Industries (Case Study: Isfahan Oil Refining Industry). First National Conference on Strategic Services Management, Islamic Azad University, Najafabad Branch, Isfahan (Najafabad).
    11.
  11. Fattahi, Parviz; Samouei, Parvaneh & Zandiyeh, Mostafa. (2017). A Multi-objective Simulated Annealing for Simultaneous Two-Sided Assembly Line Balancing and Operators Assignment. Production and Operational Management, 14 (15), 1-20.
    12.
  12. Ghasemi Sahebi, Hani & Zanjirchi, Seyyed Mahmoud. (2013). Measuring Supply Chain Agility Using Fuzzy Rule Base and Fuzzy Agility Index in the Electronics Industry (Case Study: Pishraneh Company, Sari, Iran). Journal of Industrial Management Studies, 11, (30), 57-76.
    13.
  13. Ghayebloo, S., and Tarokh, M. (2015). Designing an integrated direct and reverse supply chain network compatible with the environment. Specialized Journal of Industrial Engineering, No. 1, 93-106.
    14.
  14. Ghomi-Avili, M., Tavakkoli-Moghaddam, R., Jalali, G., & Jabbarzadeh, A. (2017). A network design model for a resilient closed-loop supply chain with lateral transshipment. International Journal of Engineering-Transactions C: Aspects, 30(3), 374-383.‏
    15.
  15. Ghorbanpoor, A., Pooya, A., Nazemi S., & Naji Azimi, Z. (2017).The Design Structural Model Of Green Supply Chain Management Practices To Using Fuzzy Interpretive Structural Modeling Approach. Journal of Operational Research and Its Applications (Journal of Applied Mathematics), 13 (4 (51)),1-20.
    16.
  16. Hahn, G.J & Kuhn, H. (2012). Value-based performance and risk management in supply chains،A robust optimization approach. Int. J. Production Economics, 139, 135–144.
    17.
  17. Hassani, H., Danesh, M. A., Javan, A., Pospiech, R., & Odulaja, A. (2017). A statisticsbased approach for crude oil supply risk assessment. OPEC Energy Review, 41(3), 187-200.‏
    18.
  18. Ismailpour, R., Azar, A. and Malekzadeh, M. (2017). Presenting an integrated model of green business partner selection and green supply chain management. Second International Conference on Industrial Management, Babolsar, Mazandaran University.
    19.
  19. Jamali, G., Karimi Asl, E. (2018). Competitive positioning for LARG Supply Chain in Cement Industry and its Strategic Requirements Importance-Performance Analysis. Industrial Management Studies, 16(50), 53-77. doi: 10.22054/jims.2018.9106.
    20.
  20. Kailun HE, H. X. (2010). The Application of Probabilistic Neural Network Model in the Green Supply Chain Performance Evaluation for Pig Industry. International Conference on E-Business and E-Government, 3310-3313.
    21.
  21. Mansouri, S.A., Aktas, E & Besikci, U. (2015). Green scheduling of a two-machine flowshop، Trade-off  between makespan and energy consumption. European Journal of Operational Research, 1–17.
    22.
  22. Lee C.H., Liao, C.J., Chung, T.P., (2014). Scheduling with multiattribute setup times on two identical parallel machines. International Journal of Production Economics, 153, 130-138.
    23.
  23. Mansouri, S. A., Aktas, E., & Besikci, U. (2016). Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research, 248(3), 772-788.
    24.
  24. Manzini, R., Accorsi, Ch.Pini, S.Penazzi. (2015). On the design of closed-loop networks for product life cycle management، Economic, environmental and geography considerations. Journal of Transport Geography 48, 121-134
    25.
  25. Mocquillon, C. d., Lente, C., et al. (2011). An efficient heuristic for medium-term planning in shampoo production. International Journal of Production Economics, 129(1), 178-185.
    26.
  26. Nilipour Tabatabai, A., Khayamian, B., Karbasian, M. and Shariati, M. (2013). Optimizing the Application of Information Technology in Supply Chain Management and Marketing of Aviation Products by AHP Method. New Marketing Research, 2 (2), 52-47.
    27.
  27. Norol, Holcomb. (2016). Development of a measure to assess quality management in certified firms. European journal of operational research, 156, 683-697.
    28.
  28. Olfat, Laya; Khatami Firouzabadi, Ali & Khodaverdi, Roohollah. (2012). Green Supply Chain Management within Iranian Automobile Industry. Iranian Journal of Management Sciences, 6 (21), 123 - 140.
    29.
  29. Qile, H., Ghobadian, A., Gallear, D. 2013. Knowledge acquisition in supply chain partnerships. The role of power. Int. J. Production Economics, 141, 605–618.
    30.
  30. Rezaee Kelidbari, H., Goudarzvand Chegini, M., Alavi Foumani, S. (2014). The Impact of Supply Chain Management on Improving the Performance of Automotive Parts Industry through Competitive Advantage. Journal of Business Management, 6(1), 67-88. doi: 10.22059/jibm.2014.51605.
    31.
  31. Shah Bandarzadeh, H., Jamali, Gh and Hashemi, M. (2014). Application of fuzzy network analysis process with nonlinear modeling approach in identifying and ranking the indicators affecting the evaluation of green supply chain performance of industrial companies in the country, 9th Management Conference, Sharif University of Technology, Tehran.
    32.
  32. Taghizadeh, H., Mohammad pour Shattery, M. (2009). Analyzing the Reasons for Not Using the Value Analysis and Lean Approach (case study). The Journal of Productivity Managment, 3, 2 (9), 77-101.
    33.
  33. Verghese, K., & Lewis, H. (2007). Environmental innovation in industrial packaging a supply chain approach. International Journal of Production Research, 45, 4381-4401.
    34.
  34. Vinodh, S., Anesh Ramiya, R & Gautham, S.G. (2011). Application of fuzzy Analytic network Process for supplier selection in a manufacturing organization, Expert Systems with Applications, 38, 272-280.
    35.
  35. Wu, G.C., Ding, J.H., & Chen, P.S. (2012). The effects if GSCM drivers and institutional pressures GSCM practices in Taiwan's textile and apparel industry. International Journal Production Economics, 135, 618-636.
    36.

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