Uysal, F., İşleyen, S. K., & Çetinkaya, C. (2018). Resource constrained project scheduling with stochastic resources. Journal of applied research on industrial engineering, 5(1), 39-49.
 Rafiei, A., Homayouni, S. M., & Shafiei Alavijeh, A. (2015). A Mathematical Model for the Single Machine Scheduling Considering Sequence Dependent Setup Costs and Idle Times. Journal of applied research on industrial engineering, 2(2), 77-85.
 Ebrahimi, M., Ghomi, S. F., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied mathematical modelling
(9-10), 2490-2504. https://doi.org/10.1016/j.apm.2013.10.061
 Ribas, I., Leisten, R., & Framiñan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers and operations research
(8), 1439-1454. https://doi.org/10.1016/j.cor.2009.11.001
 Shiau, D. F., Cheng, S. C., & Huang, Y. M. (2008). Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm. Expert systems with applications
(2), 1133-1143. https://doi.org/10.1016/j.eswa.2006.12.002
 Wang, H., Jacob, V., & Rolland, E. (2003). Design of efficient hybrid neural networks for flexible flow shop scheduling. Expert systems
(4), 208-231. https://doi.org/10.1111/1468-0394.00245
 Choi, S. W., Kim*, Y. D., & Lee, G. C. (2005). Minimizing total tardiness of orders with reentrant lots in a hybrid flowshop. International journal of production research, 43(11), 2149-2167.
 Choi, S. H., & Wang, K. (2012). Flexible flow shop scheduling with stochastic processing times: a decomposition-based approach. Computers and industrial engineering
(2), 362-373. https://doi.org/10.1016/j.cie.2012.04.001
 Luo, H., Du, B., Huang, G. Q., Chen, H., & Li, X. (2013). Hybrid flow shop scheduling considering machine electricity consumption cost. International journal of production economics
(2), 423-439. https://doi.org/10.1016/j.ijpe.2013.01.028
 Naderi, B., Gohari, S., & Yazdani, M. (2014). Hybrid flexible flowshop problems: Models and solution methods. Applied mathematical modelling, 38(24), 5767-5780.
 Tang, D., Dai, M., Salido, M. A., & Giret, A. (2016). Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in industry
, 82-95. https://doi.org/10.1016/j.compind.2015.10.001
 Rahmani, D., & Heydari, M. (2014). Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times. Journal of manufacturing systems, 33(1), 84-92.
 Lin, J. T., & Chen, C. M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation modelling practice and theory
, 100-114. https://doi.org/10.1016/j.simpat.2014.10.008
 Wagner, H. M. (1959). An integer linear‐programming model for machine scheduling. Naval research logistics quarterly, 6(2), 131-140.
 Manne, A. S. (1960). On the job-shop scheduling problem. Operations research, 8(2), 219-223.
 Guinet, A., Solomon, M. M., Kedia, P. K., & Dussauchoy, A. (1996). A computational study of heuristics for two-stage flexible flowshops. International journal of production research, 34(5), 1399-1415.
 Bowman, E. H. (1959). The schedule-sequencing problem. Operations research, 7(5), 621-624.
 Meng, L., Zhang, C., Shao, X., Zhang, B., Ren, Y., & Lin, W. (2020). More MILP models for hybrid flow shop scheduling problem and its extended problems. International journal of production research, 58(13), 3905-3930.
 Elyasi, A., & Salmasi, N. (2013). Stochastic flow-shop scheduling with minimizing the expected number of tardy jobs. The international journal of advanced manufacturing technology, 66(1-4), 337-346.
 Wang, Y., & Li, L. (2014). Time-of-use based electricity cost of manufacturing systems: Modeling and monotonicity analysis. International journal of production economics, 156, 246-259.
 Mirmohseni, S. M., Nasseri, S. H., & Khaviari, M. H. (2017). A new fuzzy hybrid dynamic programming for scheduling weighted jobs on single machine. Journal of applied research on industrial engineering, 4(2), 97-115.
 Li, Z., & Ierapetritou, M. G. (2008). Robust optimization for process scheduling under uncertainty. Industrial and engineering chemistry research, 47(12), 4148-4157.
 Nagasawa, K., Ikeda, Y., & Irohara, T. (2015). Robust flow shop scheduling with random processing times for reduction of peak power consumption. Simulation modelling practice and theory
, 102-113. https://doi.org/10.1016/j.simpat.2015.08.001
 Shahnaghi, K., Shahmoradi-Moghadam, H., Noroozi, A., & Mokhtari, H. (2016). A robust modelling and optimisation framework for a batch processing flow shop production system in the presence of uncertainties. International journal of computer integrated manufacturing, 29(1), 92-106.
 Emami, S., Moslehi, G., & Sabbagh, M. (2017). A Benders decomposition approach for order acceptance and scheduling problem: a robust optimization approach. Computational and applied mathematics, 36(4), 1471-1515.
 Hamaz, I., Houssin, L., & Cafieri, S. (2018). A robust basic cyclic scheduling problem. EURO journal on computational optimization, 6(3), 291-313.
 Ding, H., Fan, Y., & Zhong, W. (2018). Robust optimization of performance scheduling problem under accepting strategy. Open journal of optimization, 7(4), 65-78.
 Jamili, A. (2019). Job shop scheduling with consideration of floating breaking times under uncertainty. Engineering applications of artificial intelligence, 78, 28-36.
 Goli, A., Babaee Tirkolaee, E., & Soltani, M. (2019). A robust just-in-time flow shop scheduling problem with outsourcing option on subcontractors. Production and manufacturing research, 7(1), 294-315.
 Sangaiah, A. K., Tirkolaee, E. B., Goli, A., & Dehnavi-Arani, S. (2020). Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. Soft computing, 24(11), 7885-7905.
 Babaee Tirkolaee, E., Goli, A., Pahlevan, M., & Malekalipour Kordestanizadeh, R. (2019). A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization. Waste management and research, 37(11), 1089-1101.
 Sadjadi, S. J., & Omrani, H. (2008). Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy policy, 36(11), 4247-4254.
 Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations research, 21(5), 1154-1157.
 Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations research letters, 25(1), 1-13.
 Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.
 Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision sciences, 11(2), 325-336.
 Hu, C. F., Teng, C. J., & Li, S. Y. (2007). A fuzzy goal programming approach to multi-objective optimization problem with priorities. European journal of operational research, 176(3), 1319-1333.
 Baky, I. A. (2009). Fuzzy goal programming algorithm for solving decentralized bi-level multi-objective programming problems. Fuzzy sets and systems, 160(18), 2701-2713.
 Hossain, M. S., & Hossain, M. M. (2018). Application of interactive fuzzy goal programming for multi-objective integrated production and distribution planning. International journal of process management and benchmarking, 8(1), 35-58.
 Gupta, S., Ali, I., & Ahmed, A. (2018). Efficient fuzzy goal programming model for multi-objective production distribution problem. International journal of applied and computational mathematics, 4(2), 76.
 Masoud, M., Khalifa, H. A., Liu, S. Q., Elhenawy, M., & Wu, P. (2019, September). A fuzzy goal programming approach for solving fuzzy multi-objective stochastic linear programming problem. 2019 international conference on industrial engineering and systems management (IESM) (pp. 1-6). IEEE.