Sustainable Irrigation Planning with Imprecise Parameters under Fuzzy Environment

The present paper proposes a model of Multi Objective Fuzzy Linear Programming (MOFLP) based on Fuzzy Parametric Programming (FPP) to solve the problem of optimal cropping pattern in an irrigation system. It has been found that in order to solve the problem of uncertainty in the planning of sustainable irrigation, the concept of fuzzy logic has been in practice for long and was being systematically applied either to the case of fuzzy objectives alone or to case of fuzzy objectives with fuzzy resources. There has not been reported a single case of either formulation and application of MOFLP model for the planning of irrigation making use of fuzzy objective function coefficients, fuzzy technological coefficients and fuzzy resources. The approach presented in the MOFLP model attempts to consider the fuzziness of all the coefficients of a mathematical model, as they present themselves in the real life situations. The present model takes into account the experience, information and expectations of the Decision Maker (DM). The objective of the model is to maximize simultaneously four objective functions viz. the Net Benefits (NB), Crop Production (CP), Employment Generation (EG) and Manure Utilization (MU). The model proposed takes into consideration the fuzziness involved in the coefficient of objective functions, technological coefficients and stipulations. The model intends to develop a program of sustainable irrigation planning for the Jayakwadi Project, Stage-I, located in the State of Maharashtra, India. The optimal cropping pattern has been obtained for five different strategies. The results finally obtained through the fifth strategy appear realistic, promising and effective as they involve the consideration of the uncertainty contained in coefficient of objective functions, technological coefficients and stipulations simultaneously. The model may be applied to any irrigation project with a view to utilize the resources available optimally and deal with the problem of uncertainty in realistic ways in solving real life problems.