Olfat, L. Total Tardiness Minimization in Flow Shop with Intermediate Due Dates, Journal of Modern Researches in Decision Making. Vol. 2 No. 3 (in Persian), 2017, pp. 25-47,
 Naderi, B., Ruiz, R. The distributed permutation flowshop problem, Computers and Operations Research, 2009, Volume 37, Issue 4, pp 754-768, doi.10.1016/j.cor.2009.06. 019.
 Ounniche, J, Boctor, F.F., Martel, A. The impact of sequencing decisions on multi-item lot sizing and scheduling in flow shops, International Journal of Production Research, 37(10), 1999, 2253–2270.
 He, H. Minimization of maximum lateness in an m-machine permutation flow shop with a general exponential learning effect, Computers & Industrial Engineering, 2016, Volume 97, Pages 73-83, http://dx.doi.org/10.1016/j.cie. 2016.04.010
 Li, X., Yin, M. A discrete artificial bee colony algorithm with composite mutation strategies for permutation flow shop scheduling problem, Scientia Iranica E, 2012, Volume 19, pp.1921–1935, doi:10.1016/j. scient.2012.10.034
 Behnamian, J., Fatemi Ghomi, S.M.T. The heterogeneous multi-factory production network scheduling with adaptive communication policy and parallel machine, Information Sciences, 219, 2013, 181–196.
 G hafoori S. and M. taghizadeh yazdi, "Proposing a Multi- Objective Mathematical Model for RCPSP and Solving it with Firefly and Simulated Annealing algorithms," Journal of Modern Researches in Decision Making, vol. vol 1 no4 (in Persian), pp.117-142,2017.
 Wang, S., Wang, L., Liu, M., Xu, Y. An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem, Int. J. Production Economics 145, 2013, 387–396. http://dx.doi.org/10.1016/j.ijpe. 2013.05.004
 Lin, J., Zhang. S. An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem, Computers & Industrial Engineering, Volume 97, Pages 128-136, 2016, http://dx.doi.org/ 10.1016/j.cie. 2016.05.005
 Deng, J., Wang, L. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem, Swarm and Evolutionary Computation, 2016, http://dx.doi. org/10.1016/j.swevo.2016.06.002
 Rodriguez, M.A., Vecchietti, A.R., Harjunkoski, I., Grossmann, I.E. Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models, Computers and Chemical Engineering 62, 2014, 194–210.
 You, F., Grossmann, I.E. Integrated Multi-Echelon Supply Chain Design with Inventories under Uncertainty: MINLP Models, Computational Strategies, Carnegie Mellon University, USA, 2008.
 Pakzad-Moghaddam, S.H., Tavakkoli-Moghaddam, R., Mina, H. An approach for modeling a new single machine scheduling problem with deteriorating and learning effects, Computers & Industrial Engineering, 2014, http://dx.DOI.org/10.1016/j
 Holland, J.H. Adaptive in Natural and Artificial Systems, Ann Arbor: University of Michigan Press, 1975.
 Mehdizadeh, E., Tavakkoli-Moghaddam, R., Yazdani, M. A Vibration Damping Optimization Algorithm for a Parallel Machines Scheduling Problem with Sequence-independent Family Setup Times, Applied mathematical modeling, 2015, Volume 39, Issue 22, Pages 6845-6859, Doi: http://dx.doi.org/10.1016/j.apm
 Alaghebandha, M., Hajipour, V. A soft computing-based approach to optimise queuing inventory control problem. International Journal of Systems Science, 2013, DOI:10.1080/00207721.2013.809614.
 Pasandideh, SHR., Akhavan Niaki, ST., Hajipour, V. A multi objective facility location model with batch arrivals: two parameter tuned meta heuristic algorithms, J Intell Manuf, 24, 2013, 331-348.
Choi, S.-W., Kim, Y.-D. Minimizing total tardiness on a two-machine re-entrantflowshop, Eur. J. Oper. Res. 199, 2009, 375–384.