Bayesian Belief Networks for the metamodeling of simulation-optimization model to identify optimum water allocation scenario, Application in Miyandoab plain, Urmia Lake basin, Iran

2021 ◽  
Author(s):  
Amirhossein Dehghanipour ◽  
Gerrit Schoups ◽  
Hossein Babazadeh ◽  
Majid Ehtiat ◽  
Bagher Zahabiyoun
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Water Demand ◽  
Simulation Optimization ◽  
Bayesian Belief Networks ◽  
Environmental Water ◽  
Lake Basin ◽  
Decision Making Models ◽  
Validation Tests ◽  
Urmia Lake Basin

<p>In this study, decision-making models in uncertain conditions are developed to identify optimal strategies for reducing competition between agricultural and environmental water demand. The decision-making models are applied to the irrigated Miyandoab Plain, located upstream of endorheic Lake Urmia in Northwestern Iran. Decision-making models are conceptualized based on static and dynamic Bayesian Belief Networks (BBN). The static BBN evaluates the effects of management strategies and drought conditions on environmental flow and agricultural profit at the annual scale, while the dynamic BBN accounts for monthly dynamics of water demand and conjunctive use. The reliability and performance of BBNs depend on the quantity and quality of data used to train the BBN and create conditional probability tables (CPTs). In this study, simulated outputs from a multi-period simulation-optimization model (Dehganipour et al., 2020) are used to populate the CPTs in each BBN and reduce the BBN training error. Cross-validation tests and sensitivity analysis are used to evaluate the effectiveness of the resulting BBNs. Sensitivity analysis shows that drought conditions have the most significant impact on environmental flow compared to other variables. Cross-validation tests show that the BBNs are able to reproduce outputs of the complex simulation-optimization model used for training, and therefore provide a computationally fast alternative for decision-making under uncertainty.</p><p><strong>Reference:</strong> Dehghanipour, A. H., Schoups, G., Zahabiyoun, B., & Babazadeh, H. (2020). Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management, 241, 106353.</p>

Artikel Strategi tentang Pengambilan Keputusan

2019 ◽  
Author(s):  
Winda Safitri Caniago ◽  
Hade Afriansyah
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Heuristic Model ◽  
Selection Of

Decision making is an action with determine the result in solving problem with choose a rule action between alternative through a mental of process, logic of process and etc. This purpose article is to help make it easier to solve a problem. This article explain some strategy decision making such as optimization model, satisfying model, mixed scanning model, heuristic model, and last the selection of certain model.

Information support for expert evaluation of the states of the external environment

2020 ◽  
pp. 71-76
Author(s):  
Rasol Murtadha Najah
Keyword(s):  
Decision Making ◽  
Environmental Conditions ◽  
Fuzzy Inference ◽  
Environmental Parameters ◽  
Information Support ◽  
Expert Assessment ◽  
Fuzzy Variables ◽  
Decision Making Models ◽  
The Difference ◽  
State Of The Environment

This article discusses the application of methods to enhance the knowledge of experts to build a decision-making model based on the processing of physical data on the real state of the environment. Environmental parameters determine its ecological state. To carry out research in the field of expert assessment of environmental conditions, the analysis of known works in this field is carried out. The results of the analysis made it possible to justify the relevance of the application of analytical, stochastic models and models based on methods of enhancing the knowledge of experts — experts. It is concluded that the results of using analytical and stochastic objects are inaccurate, due to the complexity and poor mathematical description of the objects. The relevance of developing information support for an expert assessment of environmental conditions is substantiated. The difference of this article is that based on the analysis of the application of expert methods for assessing the state of the environment, a fuzzy logic adoption model and information support for assessing the environmental state of the environment are proposed. The formalization of the parameters of decision-making models using linguistic and fuzzy variables is considered. The formalization of parameters of decision-making models using linguistic and fuzzy variables was considered. The model’s description of fuzzy inference is given. The use of information support for environment state assessment is shown on the example of experts assessing of the land desertification stage.

Expected consistency-based model and multiplicative DEA cross-efficiency for group decision-making with incomplete distribution linguistic preference relations

2021 ◽  
pp. 1-21
Author(s):  
Jinpei Liu ◽  
Longlong Shao ◽  
Ligang Zhou ◽  
Feifei Jin
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Optimization Model ◽  
Group Decision ◽  
Preference Relation ◽  
Preference Relations ◽  
Priority Vector ◽  
Linguistic Preference Relation ◽  
Multiplicative Preference Relations ◽  
High Consistency

Faced with complex decision problems, Distribution linguistic preference relation (DLPR) is an effective way for decision-makers (DMs) to express preference information. However, due to the complexity of the decision-making environment, DMs may not be able to provide complete linguistic distribution for all linguistic terms in DLPRs, which results in incomplete DLPRs. Therefore, in order to solve group decision-making (GDM) with incomplete DLPRs, this paper proposes expected consistency-based model and multiplicative DEA cross-efficiency. For a given incomplete DLPRs, we first propose an optimization model to obtain complete DLPR. This optimization model can evaluate the missing linguistic distribution and ensure that the obtained DLPR has a high consistency level. And then, we develop a transformation function that can transform DLPRs into multiplicative preference relations (MPRs). Furthermore, we design an improved multiplicative DEA model to obtain the priority vector of MPR for ranking all alternatives. Finally, a numerical example is provided to show the rationality and applicability of the proposed GDM method.

scholarly journals Amount and location of tectonic uplift in the Urmia region of northwest Iran from the Permian to the Neogene

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Akram Alizadeh
Keyword(s):  
Hypersaline Lake ◽  
Time Interval ◽  
Lake Basin ◽  
Lake Urmia ◽  
Qom Formation ◽  
Northwest Of Iran ◽  
The Subject ◽  
Orogenic Uplift ◽  
East Azerbaijan ◽  
Urmia Lake Basin

AbstractThe Urmia Lake Basin is located between the West and East Azerbaijan provinces in the northwest of Iran. Lake Urmia is the twentieth largest lake and second largest hypersaline lake in the world. Stratigraphic columns have been constructed, using published information, to compare the sedimentary units deposited from the Permian to the Neogene on the east and west sides of the lake, and to use these to quantity subsidence and uplift. East of the lake, the sedimentary section is more complete and has been the subject of detailed stratigraphic studies, including the compilation of measured sections for some units. West of the lake, the section is incomplete and less work has been done; three columns illustrate variations in the preserved stratigraphy for the time interval. In all cases, the columns are capped by the Oligocene–Miocene Qom Formation, which was deposited during a post-orogenic marine transgression and unconformably overlies units ranging from Precambrian to Cretaceous. Permian to Cretaceous stratigraphy is used to measure subsidence in the Lake Urmia basin up to the end of the Cretaceous, and then, the subsequent orogenic uplift, which was followed by further subsidence recorded by the deposition of the Qom Formation in the Oligocene–Miocene.

Information technology support for integrated supply chain modeling

2008 ◽  
Vol 27 (1) ◽  
pp. 3-13
Author(s):  
Charu Chandra ◽  
Jānis Grabis
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Supply Chain ◽  
Process Modeling ◽  
Service Oriented Architecture ◽  
Model Development ◽  
Integrated Models ◽  
Supply Chain Modeling ◽  
Decision Making Models ◽  
Integrated Supply Chain

Multiple interrelated decision-making models are frequently used in supply chain modeling. Model integration is a precondition for efficient development and utilization of these models. This paper discusses use of modern information technology (IT) techniques and methods for integration of supply chain decision-making models. The overall approach to using IT at various stages of model development is presented. Data and process modeling techniques are used to developed semi-formalized representation of integrated models. These models support integration of decision-making components with other parts of supply chain information system. Process modeling is also used to describe interrelationships among multiple decision-making models. This representation is used as the basis for implementation of integrated models. The service-oriented architecture is proposed as an implementation platform. The presented discussion serves as the basis for further developments in developing integrated supply chain decision-making models.

scholarly journals Approaches for multicriteria decision-making based on the hesitant fuzzy best–worst method

2021 ◽  
Author(s):  
Jian Li ◽  
Li-li Niu ◽  
Qiongxia Chen ◽  
Zhong-xing Wang
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Programming Model ◽  
Multicriteria Decision Making ◽  
Decision Makers ◽  
Multicriteria Decision ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Decision Making Models ◽  
Best Worst Method

AbstractHesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

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