f8ca842c-69ad-435d-8847-3866a3cfbadd20210107115744404wseamdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTER RESEARCH1991-875510.37394/232018http://wseas.org/wseas/cms.action?id=13372352020352020810.37394/232018.2020.8http://www.wseas.org/wseas/cms.action?id=23207Application of Integer Programming in Project InvestmentChunxueZhaoAnyang Normal University, School of Mathematics and Statistics, Xiange Street 436, Anyang, P.R.CHINAInteger programming is widely used to solve optimization problems in economy, management, communication and engineering.In this paper, we use the integer programming to solve the project investment problem, which provides a solution to this type of problem.71320207132020111114https://www.wseas.org/multimedia/journals/computerresearch/2020/a285105-1398.pdf10.37394/232018.2020.8.14https://www.wseas.org/multimedia/journals/computerresearch/2020/a285105-1398.pdfXiao Zhi, Zhong Bo, Li Yingbing, The application of assignment problems in the supplier selection in supply chain, Operations Research and Management Science, 11, 3, 2002, pp. 63-68Liu Jiaxue, Chen Shiguo, Generalized assignment problem and its generalization application within the armaments transportation, Mathematics in Practice and Theory, 36, 1, 2006, pp. 199-203Liu Jiaxue, The multiple attribute group decision making based on the optimal line arassignment, Systems Engineering, 19, 4, 2001, pp. 32-36Goldberg D. E., Korb B, Deb K., Messy genetic algorithms: motivation, analysis and first results,Complex Systems, 3, 1989, pp. 493-530Cantu-Paz E A,A summary of research on parallel genetic algorithms, IlliGAL Report No. 95007, 1995.10.1016/b978-0-08-050684-5.50020-3Eshelman L J,The CHC adaptive search algorithm: How to have safe search when engaging in noon-traditional genetic recombination, In: Foundations of Genetic Algorithms , Morgan Kaufmann Publishers,1991, pp. 265-283.Srinivas M, Patnaik L M, Adaptive probabilities of crossover and mutations in GAs, In: IEEE Trans. on SMC, 24, 4, 1994, pp. 656-667.Houck C. R., Joines J. A., A genetic algorithm for function optimization: A MATLAB implementation, NC-SU-IE TR95-09, 1995.Houck C. R., Joines J. A., A genetic algorithm for function optimization: A MATLAB implementation, NC-SU-IE TR95-09, 1995.Tsujimura Y,Gen M, Genetic algorithms for solving multi-processor scheduling problems,In: Simulated Evolution and Learning, First Asia-Pacific Conference, SEALâ€™96, Taejon, Korea, Springer, 1996, pp. 106-115Nakano R., Conventional genetic algorithm for job shop problems, Proceeding of the Fourth International Conference on Genetic Algorithms, 1991, pp. 474-479.Li Y., Ng K. C., Uniform approach to model-based fuzzy control system design and structural optimization, Genetic Algorithms and Soft Computing, Herrera F and VerdegayJ(ed), Physica Verlag, 1996, pp. 251-278