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A multi-product multi-objective optimization model for aggregate production planning considering production preferences: A case study

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Yazd University, Yazd, Iran

2 School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran

Abstract

Aggregate production planning is a multi-objective problem which is influenced by managerial preferences which is rarely considered with these preferences in many researches. In this paper, a multi-product multi-objective aggregate-production-planning model has been proposed and implemented in an industrial ball-valves manufacturing company. In the first phase, preferences of various product groups have been determined via a multiple-attribute-decision-making method which is used as an input for the second phase. To do this, one of the outranking methods has been used because of the variety in the dimension and the nature of different attributes. In the second phase, a deterministic multi-objective mixed-integer mathematical model has been designed considering the needs of the company. This model not only concentrates on the benefits, but also considers the preferences of the products. The third objective function is decreasing work in process. To solve this model, ϵ-constraint method has been used leading to a set of Pareto-optimal solutions, enabling the decision-maker to choose the best solution by trading off between the three objective functions. So top managers are able to decide how to provide product preferences and how to decrease WIP products while the benefits remain reasonable. The results show that using the proposed approach in the case study has improved 35%, 28%, and 56% total benefit, total utility, and WIP products, respectively.

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References
حسینی، س.م.ح. (1399). مدل‌سازی و حل مساله یکپارچه برنامه‌ریزی تولید ادغامی و تعمیرات و نگهداری در حالت دو هدفه و با رویکرد کاهش نارضایتی مشتریان، مطالعات مدیریت صنعتی، 18(56)، 129-169.‎
خاتمی‌فیروزآبادی، س.م.ع.، ماکویی، ا.، پیری، و. (1392). برنامه‌ریزی تولید ادغامی شرکت لوله و ماشین‌سازی ایران با رویکرد برنامه‌ریزی آرمانی خاکستری، مدیریت فردا، 35(12)، 51-64.‎
 
References
Akçay, Y. (2002). Three essays on resource allocation problems: Inventory management in assemble-to-order systems and online assignment of flexible resources. The Pennsylvania State University.
Chakrabortty, R., & Hasin, M. (2013). Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach. International Journal of Industrial Engineering Computations, 4(1), 1-12.
Chen, L. (2010). The Application of Value Stream Mapping Based Lean Production System. International Journal of Business and Management, 5(6), 203-209.
Chizari, E., Bonyadi Naeini, A., & Nouralizadeh, H. (2018). A new model for Physical flow optimization in the global automotive value chain (Case study: SIBA MOTOR Company). Journal of Industrial and Systems Engineering, 11(4), 34-55.
da-Silva, C. G., Figueira, J., Lisboa, J., & Barman, S. (2006). An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming. Omega, 34(2), 167-177.
De Figueiredo, J. N., Mayerle, S. F., & Donato, F. S. (2011). Optimal product/customer mix selection as a strategic tool for cross-functional integration. Journal of Operations and Supply Chain Management, 4(1), 51-70.
Entezaminia, A., Heydari, M., & Rahmani, D. (2016). A multi-objective model for multi-product multi-site aggregate production planning in a green supply chain: Considering collection and recycling centers. Journal of Manufacturing Systems, 40, 63-75.
Fallahi, A., Azimi-Dastgerdi, M., & Mokhtari, H. (2021). A Sustainable Production-Inventory Model Joint with Preventive Maintenance and Multiple Shipments for Imperfect Quality Items. Scientia Iranica, Article in press.
Gansterer, M. (2015). Aggregate planning and forecasting in make-to-order production systems. International Journal of Production Economics, 170, 521-528.
Georgiadis, G. P., Elekidis, A. P., & Georgiadis, M. C. (2021). Optimal production planning and scheduling in breweries. Food and Bioproducts Processing, 125, 204-221.
Gholamian, N., Mahdavi, I., Tavakkoli-Moghaddam, R., & 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, 37, 585-607.
Hahn, G. J., & Brandenburg, M. (2018). A sustainable aggregate production planning model for -the chemical process industry. Computers & operations research, 94, 154-168.
Ilangkumaran, M., Avenash, A., Balakrishnan, V., Kumar, S. B., & Raja, M. B. (2013). Material selection using hybrid MCDM approach for automobile bumper. International Journal of Industrial and Systems Engineering, 14(1), 20-39.
Khoshalhan, F., & Cheraghali-Khani, A. (2012). An Integrated Model of Aggregate Production Planning with Maintenance Costs. International Journal of Industrial Engineering & Production Management, 23(1), 67-77. (in Persian)
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
Modarres, M., & Izadpanahi, E. (2016). Aggregate production planning by focusing on energy saving: A robust optimization approach. Journal of Cleaner Production, 133, 1074-1085.
Oubahman, L., & Duleba, S. (2021). Review of Promethee method in transportation. Production Engineering Archives, 27.
Rasmi, S. A. B., Kazan, C., & Türkay, M. (2019). A multi-criteria decision analysis to include environmental, social, and cultural issues in the sustainable aggregate production plans. Computers & Industrial Engineering, 132, 348-360.
Rezaie, K., Gereie, A., Ostadi, B., & Shakhseniaee, M. (2009). Safety interval analysis: A risk-based approach to specify low-risk quantities of uncertainties for contractor’s bid proposals. Computers & Industrial Engineering, 56(1), 152-156.
Saaty, T.L. (1989) Group decision making and the AHP. In: Golden BL, Wasil EA, Harker PT (eds) The analytic hierarchy process—applications and studies. Springer, Berlin, pp 59-67.
Silva Filho, O.S., Cezarino, W., & Ratto, J. (2010). Aggregate production planning: Modeling and solution via Excel spreadsheet and solver. IFAC Proceedings Volumes, 43(17), 89-94.
Su, T. S. (2017). A fuzzy multi-objective linear programming model for solving remanufacturing planning problems with multiple products and joint components. Computers & Industrial Engineering, 110, 242-254.
Tsai, W. H., & Lu, Y. H. (2018). A framework of production planning and control with carbon tax under industry 4.0. Sustainability, 10(9), 3221.
Hosseini, S.M.H. (2020). Modelling and solving the multi objective aggregate production planning with maintenance costs and dissatisfaction reduction approach. Industrial Management Studies, 18(56), 129-169. [In Persian]
Khatami-Firoozabadi, S.M.A., Makuee, A. , & Piri, V. (2013). Aggregate Production Planning Of L.M.I Corporation Using Grey Goal Programming Approach, Modiriat-e-Farda Journal, 35(12), 51-64. [In Persian]
Industrial Management Studies
Volume 19, Issue 63
January 2022
Pages 163-192
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