Water scarcity causes conflicts among natural resources, society and economy and reinforces the need for optimal allocation of irrigation water resources in a sustainable way. Uncertainties caused by natural condition...
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Water scarcity causes conflicts among natural resources, society and economy and reinforces the need for optimal allocation of irrigation water resources in a sustainable way. Uncertainties caused by natural conditions and human activities make optimal allocation more complex. An intuitionistic fuzzy multiobjectivenon-linearprogramming (IFMONLP) model for irrigation water allocation under the combination of dry and wet conditions is developed to help decision makers mitigate water scarcity. The model is capable of quantitatively solving multiple problems including crop yield increase, blue water saving, and water supply cost reduction to obtain a balanced water allocation scheme using a multi-objectivenonlinearprogramming technique. Moreover, it can deal with uncertainty as well as hesitation based on the introduction of intuitionistic fuzzy numbers. Consideration of the combination of dry and wet conditions for water availability and precipitation makes it possible to gain insights into the various irrigation water allocations, and joint probabilities based on copula functions provide decision makers an average standard for irrigation. A case study on optimally allocating both surface water and groundwater to different growth periods of rice in different subareas in Heping irrigation area, Qing'an County, northeast China shows the potential and applicability of the developed model. Results show that the crop yield increase target especially in tillering and elongation stages is a prevailing concern when more water is available, and trading schemes can mitigate water supply cost and save water with an increased grain output. Results also reveal that the water allocation schemes are sensitive to the variation of water availability and precipitation with uncertain characteristics. The IFMONLP model is applicable for most irrigation areas with limited water supplies to determine irrigation water strategies under a fuzzy environment. (C) 2017 Elsevier B.V. All righ
作者：
Li, MoFu, QiangSingh, Vijay P.Liu, DongLi, TianxiaoZhou, YanNortheast Agr Univ
Sch Water Conservancy & Civil Engn Changjiang St 600 Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ
Key Lab Effect Utilizat Agr Water Resources Minist Agr Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ
Heilongiiagn Prov Key Lab Water Resources & Water Harbin 150030 Heilongjiang Peoples R China Texas A&M Univ
Dept Biol & Agr Engn 321 Scoates Hall2117 TAMU College Stn TX 77843 USA Texas A&M Univ
Zachry Dept Civil Engn 321 Scoates Hall2117 TAMU College Stn TX 77843 USA UAE Univ
Natl Water Ctr Al Ain U Arab Emirates
This study presents an optimization model for the allocation of agricultural water and land resources under uncertainty. The model incorporates intuitionistic fuzzy numbers, fuzzy credibility-constrained programming, ...
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This study presents an optimization model for the allocation of agricultural water and land resources under uncertainty. The model incorporates intuitionistic fuzzy numbers, fuzzy credibility-constrained programming, mixed-integer non-linearprogramming, and multi-objectiveprogramming into a general framework. The model is capable of (1) balancing the trade-off among economic, environmental, and social considerations in an irrigated agricultural system;(2) optimally allocating limited agricultural water and land resources simultaneously;and (3) dealing with the complexities of non-linearity and fuzzy uncertainties concurrently occurring in both parameters and constraints to objectively reflect practical issues in agricultural water and land resources allocations. The developed model is applied to a real case study in northeast China. The net system benefits, global warming potential, water pollution, resource allocation equity, and agricultural water and land allocation schemes among different subareas in different crop growth periods are obtained under various scenarios. The performance of the model is assessed with alternative schemes. The model can help decision makers realize how much confidence one can have in the optimal solutions and manage agricultural water and land resources in a more efficient and environment-friendly way.
In this study, multi-objective inventory model of deteriorating and perishable items is developed under space and budget constraints. Demand is stock dependent and power function of time. This model is completely a ne...
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In this paper, a closed-loop supply chain (CLSC) network model consisting of various conflicting decisions of forward and reverse facilities is considered. The proposed model integrates the strategic and tactical deci...
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In this paper, a closed-loop supply chain (CLSC) network model consisting of various conflicting decisions of forward and reverse facilities is considered. The proposed model integrates the strategic and tactical decisions to avoid the sub-optimalities led from separated design in both chain networks. The strategic-level decisions relate to the amounts of goods flowing on the forward and reverse chains whereas the tactical-level decisions concern balancing disassembly lines, collection and refurbishing activities in the reverse chain. First, a fuzzy multi-objective mixed-integer non-linearprogramming model that considers the imprecise nature of critical parameters such as cost coefficients, capacity levels, market demands and reverse rates is proposed. Then, proposed fuzzy model is converted into an auxiliary crisp multi-objective mixed-integer non-linearprogramming (MOMINP) model by applying two different approaches. Finally, different fuzzy interactive programming approaches are applied to solve this MOMINP model to find a satisfactory solution for the network that is considered. The proposed model with the solution approaches is validated through a realistic numerical example. Computational results indicate that our proposed model and solution approaches can effectively be used in CLSC network problems.
The aim of this study is to develop a multi-objective decision-making method, named Simultaneous Evaluation of Criteria and Alternatives (SECA) for optimal ranking of wastewater allocation alternatives. This multi-obj...
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The aim of this study is to develop a multi-objective decision-making method, named Simultaneous Evaluation of Criteria and Alternatives (SECA) for optimal ranking of wastewater allocation alternatives. This multi-objective decision-making method is for the continuous environment, meaning that experts can choose the best solution among the solutions obtained. The SECA multi-objective non-linear programming method has three objective functions: (i) maximization of the overall performance of alternatives, (ii) minimization of the deviation of criterion weights from reference point based on the within-criteria variation information, and (iii) minimization of the deviation of criterion weights based on the between-criteria variation information as well as two limitations for the weights of criteria. First, 15 criteria from different fields, including economic, socio-cultural, technological, and environmental, and six alternatives were determined for reusing wastewater, including reuse in the industrial sector, for recreational consumption, supplying environmental demand, artificial aquifer recharge, agricultural irrigation, and landscape irrigation. Then, decision-making matrix was constructed and reference points were calculated. By programming the SECA code in Lingo software and considering different beta values, different weights for criteria and alternatives were ranked. In the other words, determining the best value for beta was the most important step, so different values for beta from 0.1 to 10 were examined. Results showed that in the value of beta equal to 4, the maximum value of the objective function was obtained as 0.6926. Therefore, it was the best value for beta and the weight of the effects on water resources criterion was equal to 0.0957 and was the most important criterion, and the allocation alternative in the environmental sector with a score of 0.8575 was the best wastewater allocation alternative. Followed by the environmental sector alternative, la
In multi-Criteria Decision Making (MCDM), alternatives are evaluated by considering different criteria. In MCDM, there is a requirement to integrate the objective and subjective weights, since the objective weighting ...
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In multi-Criteria Decision Making (MCDM), alternatives are evaluated by considering different criteria. In MCDM, there is a requirement to integrate the objective and subjective weights, since the objective weighting methods ignore the decision-maker's (DM's) experiences and the subjective weighting methods ignore the performance ratings of the alternatives with respect to different criteria. To integrate the two types of weights and evaluate the best alternative, three well-established methods, namely "CRiteria Importance Through Intercriteria Correlation (CRITIC)", "Best Worst Method (BWM)", and "linearprogramming techniques for multidimensional Analysis of Preferences (LINMAP)" are considered in our study. Based on these methods, we have proposed a new method, namely "objective-Subjective Weighted method for Minimizing Inconsistency (OSWMI)" which considers both pairwise comparisons of the criteria and alternatives along with their corresponding performance ratings. We have first improved both the methods, CRITIC (named as improved CRITIC) and LINMAP (named as LINMAP II). Finally, the proposed OSWMI method is developed by integrating the improved CRITIC method, BWM, and LINMAP II using a multi-objective non-linear programming (MONLP) model. The OSWMI method may reduce the problem of strategic weight manipulation, since the integrated weights and the two ideal solutions are priori unknown and obtained simultaneously for selecting the best alternative. A case study of the web service selection is used to demonstrate the implementation of the OSWMI method. From the analysis, the proposed OSWMI method reveals a promising result. Further, sensitivity of the OSWMI method is checked by using the standard regression coefficients obtained by multiple linear regression.
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