Document Type : Research Paper
Department of Industrial Engineering, Faculty of Malek-e-Ashtar University of Technology, Isfahan, Iran.
One of the most important decisions taken in a supply chain is the issue of aggregate production planning where a program-within a medium time-range-- is determined for optimum manufacturing of all products using shared equipment and resources. This research presents a multi-objective model that helps the decision makers to make such decisions.
The proposed model comprises four main objectives, the first one of which considers minimizing costs (including costs of manufacturing product, supplying, maintenance, inventory stock shortage, and expenditures related to man power). The second objective is defined as maximizing customers’ satisfaction. Minimizing suppliers’ satisfaction makes up the third objective and maximizing the quality of the manufactured products constitutes the fourth objective. In this model, the demand parameter is investigated under uncertain conditions; hence, other parameters influenced by this parameter are also presented under uncertain conditions occurring within three differing scenarios. This model is solved through LP- metric and the LINGO v126.96.36.199 software. At first the model is solved by means of numerical example; then it is solved by the actual data that are related to a military industry. Finally, process, variables like inventory level, overtime work hours etc, are valued with the help of closed-loop supply chain of the proposed model.
- Chen, C. L., & Lee, W. C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & chemical engineering, 28(6-7), 1131-1144.
- Pokharel, S., & Mutha, A. (2009). Perspectives in reverse logistics: a review. Resources, conservation and recycling, 53(4), 175-182.
- Ghahremani-Nahr, J., Nozari, H., & Najafi, S. E. (2020). Design a green closed loop supply chain network by considering discount under uncertainty. Journal of applied research on industrial engineering, 7(3), 238-266.
- Teimoori, E., Shafiian Bejestani, J., & Kalb Khani, K. (2002). Simultaneous selecting and developing of suppliers in fuzzy state. The first national conference of logistics and supply chains, Tehran, Iran. (In Persian). https://civilica.com/doc/8764/
- Hansen, Z. N. L., Larsen, S. B., Nielsen, A. P., Groth, A., Gregersen, N. G., & Ghosh, A. (2018). Combining or separating forward and reverse logistics. The international journal of logistics management, 29(1), 216-236. https://doi.org/10.1108/IJLM-12-2016-0299
- Zhang, R., Zhang, L., Xiao, Y., & Kaku, I. (2012). The activity-based aggregate production planning with capacity expansion in manufacturing systems. Computers & industrial engineering, 62(2), 491-503.
- Paduloh, P., Djatna, T., Sukardi, S., & Muslich, M. (2020). Uncertainty models in reverse supply chain: a review. j. supply chain manag, 9(2), 139-149.
- Jang, J., & Do Chung, B. (2020). Aggregate production planning considering implementation error: a robust optimization approach using bi-level particle swarm optimization. Computers & industrial engineering, 142, 106367. https://doi.org/10.1016/j.cie.2020.106367
- Hafezalkotob, A., Chaharbaghi, S., & Lakeh, T. M. (2019). Cooperative aggregate production planning: a game theory approach. Journal of industrial engineering international, 15, 19-37. https://doi.org/10.1007/s40092-019-0303-0
- Masud, A. S., & Hwang, C. L. (1980). An aggregate production planning model and application of three multiple objective decision methods. International journal of production research, 18(6), 741-752. https://doi.org/10.1080/00207548008919703
- Cheraghalikhani, A., Khoshalhan, F., & Mokhtari, H. (2019). Aggregate production planning: a literature review and future research directions. International journal of industrial engineering computations, 10(2), 309-330.
- Hatefi, S. M., Jolai, F., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2015). A credibility-constrained programming for reliable forward–reverse logistics network design under uncertainty and facility disruptions. International journal of computer integrated manufacturing, 28(6), 664-678. https://doi.org/10.1080/0951192X.2014.900863
- Ghorbani, M., Arabzad, S. M., & Tavakkoli-Moghaddam, R. (2014). A multi-objective fuzzy goal programming model for reverse supply chain design. International journal of operational research, 19(2), 141-153.
- Rivaz, S., Nasseri, S. H., & Ziaseraji, M. (2020). A fuzzy goal programming approach to multiobjective transportation problems. Fuzzy information and engineering, 12(2), 139-149. https://doi.org/10.1080/16168658.2020.1794498
- Khalifa, H. A. (2018). On solving fully fuzzy multi-criteria De Novo programming via fuzzy goal programming approach. Journal of applied research on industrial engineering, 5(3), 239-252. DOI: 22105/jarie.2018.148642.1054
- Baykasoglu, A. D. İ. L. (2001). MOAPPS 1.0: aggregate production planning using the multiple-objective tabu search. International journal of production research, 39(16), 3685-3702. https://doi.org/10.1080/00207540110061607
- Leung, S. C., Wu, Y., & Lai, K. K. (2003). Multi-site aggregate production planning with multiple objectives: a goal programming approach. Production planning & control, 14(5), 425-436. https://doi.org/10.1080/0953728031000154264
- Gholamian, N., Mahdavi, I., & Tavakkoli-Moghaddam, R. (2016). Multi-objective multi-product multi-site aggregate production planning in a supply chain under uncertainty: fuzzy multi-objective optimisation. International journal of computer integrated manufacturing, 29(2), 149-165. https://doi.org/10.1080/0951192X.2014.1002811
- Mirzapour Al-E-Hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International journal of production economics, 134(1), 28-42.
- Rahimi, E., Paydar, M. M., Mahdavi, I., Jouzdani, J., & Arabsheybani, A. (2018). A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts. Journal of industrial and production engineering, 35(4), 214-228. https://doi.org/10.1080/21681015.2018.1441195
- Zanjani, B., Amiri, M., Hanafizadeh, P., & Salahi, M. (2021). Robust multi-objective hybrid flow shop scheduling. Journal of applied research on industrial engineering, 8(1), 40-55.
- Mirzapour Al-e-hashem, S. M. J. M., Aryanezhad, M. B., & Sadjadi, S. J. (2012). An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. The international journal of advanced manufacturing technology, 58, 765-782. https://doi.org/10.1007/s00170-011-3396-1
- Mirzapour Al-e-hashem, S. M. J., Baboli, A., & Sazvar, Z. (2013). A stochastic aggregate production planning model in a green supply chain: considering flexible lead times, nonlinear purchase and shortage cost functions. European journal of operational research, 230(1), 26-41.
- Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
- Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & operations research, 37(4), 668-683.