Alves, M.J., Antunes, C.H., Costa, J.P. (2019). New concepts and an algorithm for multiobjective bilevel programming: optimistic, pessimistic and moderate solutions.
Operational Research,
https://doi.org/10.1007/s12351-019-00534-9
Amiri, M., Ekhtiari, M., Yazdani, M. (2011). Nadir compromise programming: A model for optimization of multi-objective portfolio problem. Expert Systems with Applications 38: 7222-7226.
Aviso, K.B., Tan, R.R., Culaba, A.B., Cruz Jr, J.B. (2010). Bi-level fuzzy optimization approach for water exchange in eco-industrial parks. Process Safety and Environmental Protection 88: 31–40.
Britz, W., Ferris, M., Kuhn, A. (2013). Modeling water allocating institutions based on Multiple Optimization Problems with Equilibrium Constraints. Environmental Modelling and Software 46: 196-207.
Cachon G, Netessine, S. (2004). Game theory in supply chain analysis. In: Simchi-Levi EbD, Wu SD, Shen M, editors. Supply chain analysis in the e-business era. Norwell, MA: Kluwer Academic.
Cai, Y., Yue, W., Xu, L., Yang, Z., & Rong, O. (2016). Sustainable urban water resources management considering life-cycle environmental impacts of water utilization under uncertainty. Resources, Conservation and Recycling 108: 21–40.
Carino DR, Kent T, Meyers DH, Stacy C, Sylvanus M, Turner AL, Watanabe K, Ziemba WT (1994) The Russell–Yasuda Kasai model: an asset/liability model for a Japanese insurance company using multistage stochastic programming. Interfaces 24(1): 29–49.
Chen, Y., He, L., Lu, H., Li, J., Ren, L. (2017). A leader-follower-interactive method for regional water resources management with considering multiple water demands and eco-environmental constraints. Journal of Hydrology 548: 121-134.
Charnes A, Cooper WW (1959) Chance constrained programming. Management Science 6:73–79.
Ekhtiari, M., Ghoseiri, K. (2013). Multi-objective stochastic programming to solve manpower allocation problem. International Journal of Advanced Manufacturing Technology 65:183-196.
Ertogral, K., Wu, S.D. (2000). Auction-theoretic coordination of production planning in the supply chain. IIE Transactions 32(10): 1154–1168.
FAO. (2017). AQUASTAT Database Query Results.
Grosso, J.M., Ocampo-Martínez, C., Puig, V., Joseph, B. (2014). Chance-constrained model predictive control for drinking water networks. Journal of Process Control 24: 504-516.
Gu, J.J., Huang, G.H., Guo, P., Shen, N. (2013). Interval multistage joint-probabilistic integer programming approach for water resources allocation and management. Journal of Environmental Management 128: 615-624.
Guo, Z., Yang, J., Leung, S.Y.S., Shi, L. (2016). A bi-level evolutionary optimization approach for integrated production and transportation scheduling. Applied Soft Computing 42: 215-228.
Holland, J. H., 1975, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
Ip WH, Fung R, Keung KW (1999) An investigation of stochastic analysis of flexible manufacturing systems simulation. International Journal of Advanced Manufacturing Technology 15: 244–250.
Kӧksoy, O., Yalcinoz, T. (2008). Robust design using pareto type optimization: A genetic algorithm with arithmetic crossover. Computers and Industrial Engineering 55(1): 208-2018.
Kucukmehmetoglu, M. (2012). An integrative case study approach between game theory and Pareto frontier concepts for the transboundary water resources allocations. Journal of Hydrology 450–451: 308-319.
Kuo, R.J., Han, Y.S. (2011). A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem-A case study on supply chain model. Applied Mathematical Modelling 35: 3905–3917.
Kuo, R.J., Lee, Y.H., Zulvia, F.E., Tien, F.C. (2015). Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm. Applied Mathematics and Computation 266: 1013–1026.
Lan, H., Li, R., Liu, Z., Wang, R. (2011). Study on the inventory control of deteriorating items under VMI model based on bi-level programming. Expert Systems with Applications 38: 9287–9295.
Lewis, A., Randall, M. (2017). Solving multi-objective water management problems using evolutionary computation. Journal of Environmental Management 204: 179-188.
Li, Y.P., Huang, G.H., Huang, Y.F., Zhou, H.D. (2009). A multistage fuzzy-stochastic programming model for supporting sustainable water-resources allocation and management. Environmental Modelling & Software 24(7): 786-797.
Li, M., Fu, Q., Singh, V.P., Liu, D. (2018). An interval multi-objective programming model for irrigation water allocation under uncertainty. Agricultural Water Management 196: 24–36.
Linton, J.D., Klassen, R., Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of Operations Management 25: 1075-1082.
Naimi Sadigh, A., Mozafari, M., Karimi, B. (2012). Manufacturer–retailer supply chain coordination: A bi-level programming approach. Advances in Engineering Software 45: 144–152.
Pérez-Uresti, S.I., Ponce-Ortega, J.M., Jiménez-Gutiérrez, A. (2019). A multi-objective optimization approach for sustainable water management for places with over-exploited water resources. Computers and Chemical Engineering 121: 158-173.
Prékopa, A. (1995). Stochastic programming. Dordrecht, the Netherlands: Springer.
Ren, C., Guo, P., Tan, Q., Zhang, L. (2017). A multi-objective fuzzy programming model for optimal use of irrigation water and land resources under uncertainty in Gansu Province, China. Journal of Cleaner Production 164: 85-94.
Roghanian, E., Sadjadi, S.J., Aryanezhad, M.B. (2007). A probabilistic bi-level linear multi-objective programming problem to supply chain planning. Applied Mathematics and Computation 188: 786–800.
Roozbahani, R., Schreider, S., Abbasi, B. (2015). Optimal water allocation through a multi-objective compromise between environmental, social, and economic preferences. Environmental Modelling and Software 64: 18-30.
Sen S (2001) In: Gass S, Harris C (eds) Stochastic programming: computational issues and challenges, encyclopedia of OR/MS. Kluwer Academic, Dordrecht, pp 784–789.
Sheikh Sajadieh M, AkbariJokar MR (2009) An integrated vendor–buyer cooperative model under stochastic supply lead-time. International Journal of Advanced Manufacturing Technology 41:1043–1050.
Sivanandam, S.N., Deepa, S.N. (2008). Introduction to Genetic Algorithms: Springer Berlin Heidelberg.
Sun, S.,
Wang, Y.,
Liu, J., Cai, H., Wu, P., Geng, Q., Xu, L. (2016). Sustainability assessment of regional water resources under the DPSIR framework.
Journal of Hydrology 532: 140-148.
Thi Bui, N.,
Kawamura, A., Du Bui, D., Amaguchi, H., Duc Bui, D., Tu Truong, N., Thi Do, H.H., Chung Thuy Nguyen, C. (2019). Groundwater sustainability assessment framework: A demonstration of environmental sustainability index for Hanoi, Vietnam.
Journal of Environmental Management 241: 479-487.
Uen, T-S., Chang, F-J., Zhou, Y., Tsai, W-P. (2018). Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes. Science of the Total Environment 633: 341–351.
Wee, H.M., Lee, M.C., Yang, P.C., Chung, R.L. (2013). Bi-level vendor–buyer strategies for a time-varying product price. Applied Mathematics and Computation 219: 9670–9680.
Xiong, W., Li, Y., Zhang, W., Ye, Q., Zhang, S., Hou, X. (2018). Integrated multi-objective optimization framework for urban water supply systems under alternative climates and future policy. Journal of Cleaner Production 195: 640-650.
Xie, Y.L., Xia, D.H. Huang, G.H., Ji, L. (2018). Inexact stochastic optimization model for industrial water resources allocation under considering pollution charges and revenue-risk control. Journal of Cleaner Production 203: 109-124.
Yao, L.,
Xu, Z., Chen, X. (2019). Sustainable Water Allocation Strategies under Various Climate Scenarios: A Case Study in China.
Journal of Hydrology 574: 529-543.
Zarghami, M., Hajykazemian, H. (2013). Urban water resources planning by using a modified particle swarm optimization algorithm. Resources, Conservation and Recycling 70: 1–8.
Zhang, X., Vesselinov, V.V. (2016). Energy-water nexus: Balancing the tradeoffs between two-level decision makers. Applied Energy 183: 77–87.