Gupta, P. K., & Mohan, M. (2006). Problems in operations research. Sultan Chand & Sons, New Delhi.
 Kapoor, V. K. (2006). Operations research: techniques for management. Sultan Chand.
 Haley, K. B. (1962). New methods in mathematical programming—the solid transportation problem. Operations research, 10(4), 448-463.
 Junginger, W. (1993). On representatives of multi-index transportation problems. European journal of operational research, 66(3), 353-371.
 Rautman, C. A., Reid, R. A., & Ryder, E. E. (1993). Scheduling the disposal of nuclear waste material in a geologic repository using the transportation model. Operations research, 459-469.
 Ahuja, A., & Arora, S. R. (2001). Multi index fixed charge bicriterion transportation problem. Indian journal of pure and applied mathematics, 32(5), 739-746.
 Sakawa, M. (2013). Fuzzy sets and interactive multi-objective optimization. Springer science & business media.
 Edalatpanah, S. A. (2019). A nonlinear approach for neutrosophic linear programming. J. Appl. Res. Ind. Eng. 6(4), 367-373.
 Mousa, A. A. (2010). Using genetic algorithm and TOPSIS technique for multi-objective transportation problem: a hybrid approach. International journal of computer mathematics, 87(13), 3017-3029.
 Mousa, A. A., Geneedy, H. M., & Elmekawy, A. Y. (2010, May). Efficient evolutionary algorithm for solving multiobjective transportation problem. The international conference on mathematics and engineering physics (ICMEP-5) (pp. 1-11). Military Technical College Kobry Elkobbah, Cairo, Egypt.
 Zaki, S. A., Mousa, A. A. A., Geneedi, H. M., & Elmekawy, A. Y. (2012). Efficient multi-objective genetic algorithm for solving transportation, assignment, and transshipment problems. Applied mathematics, 3, 92-99.
 Jafari, H., & Hajikhani, A. (2016). Multi objective decision making for impregnability of needle mat using design of experiment technique and respond surface methodology. Journal of applied research on industrial engineering, 3(1 (4)), 30-38.
 Kasana, H. S., & Kumar, K. D. (2013). Introductory operations research: theory and applications. Springer science & business media.
 Haley, K. B. (1963). The multi-index problem. Operations research, 11(3), 368-379.
 Badrloo, S., & Kashan, A. H. (2019). Combinatorial optimization of permutation-based quadratic assignment problem using optics inspired optimization. J. Appl. Res. Ind. Eng. 6(4), 314-332.
 El-Wahed, W. F. A. (2001). A multi-objective transportation problem under fuzziness. Fuzzy sets and systems, 117(1), 27-33.
 Zimmerman, H. J. (1983). Using fuzzy sets in operational research. European journal of operational research, 13(3), 201-216.
 Deb, K. (1999). An introduction to genetic algorithms. Sadhana, 24(4-5), 293-315.
 Gen, M., & Cheng, R. (2000). Genetic algorithms and engineering optimization. John wiley & sons. Inc.
 Michalewicz, Z. (1996). Binary or float? In Genetic algorithms+ data structures= evolution programs (pp. 97-106). Berlin, Heidelberg: Springer.
 El-Shorbagy, M. A., Ayoub, A. Y., Mousa, A. A., & El-Desoky, I. M. (2019). An enhanced genetic algorithm with new mutation for cluster analysis. Computational statistics, 34(3), 1355-1392.
 El-Shorbagy, M. A., Mousa, A. A., & Farag, M. A. (2019). An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem. OPSEARCH, 56(3), 911-944.
 Abdelsalam, A. M., & El-Shorbagy, M. A. (2018). Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renewable energy, 123, 748-755.
 Osman, M. S., Abo-Sinna, M. A., & Mousa, A. A. (2006). IT-CEMOP: An iterative co-evolutionary algorithm for multi-objective optimization problem with nonlinear constraints. Applied mathematics and computation, 183(1), 373-389.
 El-Shorbagy, M. A., Mousa, A. A., & Farag, M. (2017). Solving nonlinear single-unit commitment problem by genetic algorithm based clustering technique. Review of computer engineering research, 4(1), 11-29.