Acknowledgement
이 논문은 2022학년도 조선대학교 학술연구비의 지원을 받아 연구되었음.
References
- Bai, X., Yan, W., Ge, S. S., & Cao, M. (2018). An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field. Information Sciences, 453, 227-238. https://doi.org/10.1016/j.ins.2018.04.044
- Fisher, M. L., Nemhauser, G. L., & Wolsey, L. A. (1978). An analysis of approximations for maximizing submodular set functions-II. Berlin, Heidelberg. Polyhedral combinatorics, pp. 73-87.
- Huang, L., Qu, H., & Zuo, L. (2018). Multi-type UAVs cooperative task allocation under resource constraints. IEEE Access, 6, 17841-17850.
- Kim, G. (2022). Multi agent-multi tasks assignment problem using hybrid cross-entropy algorithm. Journal of the Korea Industrial Information Systems Research, 27(4), 37-45.
- Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T. (2002). The sample average approximation method for stochastic discrete optimization. SIAM J ournal on Optimization, 12(2), 479-502. https://doi.org/10.1137/S1052623499363220
- Le Thi, H. A., Nguyen, D. M., & Dinh, T. P. (2012). Globally solving a nonlinear UAV task assignment problem by stochastic and deterministic optimization approaches. Optimization Letters, 6(2), 315-329. https://doi.org/10.1007/s11590-010-0259-x
- Lee, J.H. & Shin M.I (2016), Stochastic Weapon Target Assignment Problem under Uncertainty in Targeting Accuracy, The Korean Operations Research and Management Science Society, 41(3), 23-36.
- Lee, J., Kim, G., & Moon, I. (2021). A mobile multi-agent sensing problem with submodular functions under a partition matroid. Computers & Operations Research, 132, 105265.
- Li, X., & Zhang, K. (2018). A sample average approximation approach for supply chain network design with facility disruptions. Computers & Industrial Engineering, 126, 243-251. https://doi.org/10.1016/j.cie.2018.09.039
- Li, J. J., Zhang, R. B., & Yang, Y. (2015). Meta-heuristic ant colony algorithm for multi-tasking assignment on collaborative AUVs. International J ournal of Grid and Distributed Computing, 8(3), 135-144.
- Mancilla, C., & Storer, R. (2012). A sample average approximation approach to stochastic appointment sequencing and scheduling. I IE Transactions, 44(8), 655-670. https://doi.org/10.1080/0740817X.2011.635174
- Nemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978). An analysis of approximations for maximizing submodular set functions-I. Mathematical programming, 14(1), 265-294. https://doi.org/10.1007/BF01588971
- Qu, G., Brown, D., & Li, N. (2019). Distributed greedy algorithm for multi-agent task assignment problem with submodular utility functions. Automatica, 105, 206-215. https://doi.org/10.1016/j.automatica.2019.03.007
- Ruszczynski, A., & Shapiro, A. (2003). Stochastic programming models. H andbooks in operations research and management science, 10, 1-64.
- Schutz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European journal of operational research, 199(2), 409-419. https://doi.org/10.1016/j.ejor.2008.11.040
- Sun, X., Cassandras, C. G., & Meng, X. (2017, December). A submodularity-based approach for multi-agent optimal coverage problems. 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 4082-4087.
- Verweij, B., Ahmed, S., Kleywegt, A. J., Nemhauser, G., & Shapiro, A. (2003). The sample average approximation method applied to stochastic routing problems: a computational study. Computational optimization and applications, 24(2), 289-333. https://doi.org/10.1023/A:1021814225969
- Yun, Y.S. & Chuluunsukh, A. (2019). Green Supply Chain Network Model: Genetic Algorithm Approach. J ournal of the Korea Industrial Information Systems Research, 24(3), 31-38.