[1] Gansterer, M. (2015). Aggregate planning and forecasting in make-to-order production systems. International journal of production economics, 170, 521-528.
[2] Kumar, G. M., & Haq, A. N. (2005). Hybrid genetic—ant colony algorithms for solving aggregate production plan. Journal of advanced manufacturing systems, 4(01), 103-111.
[3] Al-e, 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(5-8), 765-782.
[4] Dakka, F, Aswin, M., & Siswojo, B. (2017). Multi-Plant multi-product aggregate production planning using genetic algorithm. International journal of engineering research and management, 4, 2349- 2058.
[5] Swinney, R. (2011). Selling to strategic consumers when product value is uncertain: The value of matching supply and demand. Management science, 57(10), 1737-1751.
[6] Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A review and critique on integrated production–distribution planning models and techniques. Journal of manufacturing systems, 32(1), 1-19.
[7] 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.
[8] Modarres, M., & Izadpanahi, E. (2016). Aggregate production planning by focusing on energy saving: A robust optimization approach. Journal of cleaner production, 133, 1074-1085.
[9] Makui, A., Heydari, M., Aazami, A., & Dehghani, E. (2016). Accelerating benders decomposition approach for robust aggregate production planning of products with a very limited expiration date. Computers & industrial engineering, 100, 34-51.
[10] Hsieh, S., & Wu, M. S. (2000). Demand and cost forecast error sensitivity analyses in aggregate production planning by possibilistic linear programming models. Journal of intelligent manufacturing, 11(4), 355-364.
[11] Wang, R. C., & Fang, H. H. (2001). Aggregate production planning with multiple objectives in a fuzzy environment. European journal of operational research, 133(3), 521-536.
[12] Wang, R. C., & Liang, T. F. (2005). Aggregate production planning with multiple fuzzy goals. The international journal of advanced manufacturing technology, 25(5-6), 589-597.
[13] Gulsun, B., Tuzkaya, G., Tuzkaya, U. R., & Onut, S. (2009). An aggregate production planning strategy selection methodology based on linear physical programming. International journal of industrial engineering, 16(2), 135-146.
[14] Nowak, M. (2013). An interactive procedure for aggregate production planning. Croatian operational research review, 4(1), 247-257.
[15] Chakrabortty, R. K., Hasin, M. A. A., Sarker, R. A., & Essam, D. L. (2015). A possibilistic environment based particle swarm optimization for aggregate production planning. Computers & industrial engineering, 88, 366-377.
[16] Shyu, S. J., Lin, B. M., & Yin, P. Y. (2004). Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time. Computers & industrial engineering, 47(2-3), 181-193.
[17] Montgomery, J., Fayad, C., & Petrovic, S. (2006). Solution representation for job shop scheduling problems in ant colony optimisation. International workshop on ant colony optimization and swarm intelligence (pp. 484-491). Berlin, Heidelberg: Springer.
[18] Pal, A., Chan, F. T. S., Mahanty, B., & Tiwari, M. K. (2011). Aggregate procurement, production, and shipment planning decision problem for a three-echelon supply chain using swarm-based heuristics. International journal of production research, 49(10), 2873-2905.
[19] Bremermann, H. J., Oehme, R., & Taylor, J. G. (1958). Proof of dispersion relations in quantized field theories. Physical review, 109(6), 2178.
[20] Ramezanian, R., Rahmani, D., & Barzinpour, F. (2012). An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search. Expert systems with applications, 39(1), 1256-1263.
[21] Chakrabortty, R. K., & Hasin, M. A. A. (2013). Solving an aggregate production planning problem by fuzzy based genetic algorithm approach. International journal of fuzzy logic systems, 3(1), 1-15.
[22] Hossain, M. M., Nahar, K., Reza, S., & Shaifullah, K. M. (2016). Multi-period, multi-product, aggregate production planning under demand uncertainty by considering wastage cost and incentives. World review of business research, 6(2), 170-185.
[23] Savsani, P., Banthia, G., Gupta, J., & Ronak, V. (2016). Optimal aggregate production planning by using genetic algorithm. Proceedings of the international conference on industrial engineering and operations management, (IEOM) (pp. 863-874).
[24] Mahmud, S., Hossain, M. S., & Hossain, M. M. (2018). Application of multi-objective genetic algorithm to aggregate production planning in a possibilistic environment. International journal of industrial and systems engineering, 30(1), 40-59.
[25] Jamalnia, A., Yang, J. B., Feili, A., Xu, D. L., & Jamali, G. (2019). Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions. The international journal of advanced manufacturing technology, 102(1-4), 159-181.
[26] Al Aziz, R., Paul, H. K., Karim, T. M., Ahmed, I., & Azeem, A. (2018). Modeling and optimization of multi-layer aggregate production planning. Journal of operations and supply chain management, 11(2), 1-15.
[27] Mehdizadeh, E., Niaki, S. T. A., & Hemati, M. (2018). A bi-objective aggregate production planning problem with learning effect and machine deterioration: Modeling and solution. Computers & operations research, 91, 21-36.
[28] Malhotra, R., Singh, N., & Singh, Y. (2011). Genetic algorithms: Concepts, design for optimization of process controllers. Computer and information science, 4(2), 39.
[29] Chakrabortty, R. K., & Hasin, M. A. A. (2013). Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach. International journal of industrial engineering computations, 4, 1-12.
[30] Mohammadi-Andargoli, H., Tavakkoli-Moghaddam, R., Shahsavari Pour, N., & Abolhasani-Ashkezari, M. H. (2012). Duplicate genetic algorithm for scheduling a bi-objective flexible job shop problem. International journal of research in industrial engineering, 1(2), 10-26.
[31] Moradi, N., & Shadrokh, S. (2019). A simulated annealing optimization algorithm for equal and un-equal area construction site layout problem. International journal of research in industrial engineering, 8(2), 89-104.
[32] Ali, S. M., & Nakade, K. (2015). A mathematical optimization approach to supply chain disruptions management considering disruptions to suppliers and distribution centers. Operations and supply chain management, 8(2), 57-66.