scholarly journals Wetland Restoration Planning Approach Based on Interval Fuzzy Linear Programming under Uncertainty

2021 ◽  
Vol 18 (18) ◽  
pp. 9549
Author(s):  
Yang Zhang ◽  
Jing Shen
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Wetland Restoration ◽  
Fuzzy Linear Programming ◽  
Decision Makers ◽  
Ecological Environment ◽  
Planning Problem ◽  
Restoration Planning ◽  
Trade Offs ◽  
Comparison Results

When planning wetland restoration projects, the planting area allocation and the costs of the restoration measures are two major issues faced by decision makers. In this study, a framework based on the interval fuzzy linear programming (IFLP) method is introduced for the first time to plan wetland restoration projects. The proposed framework can not only effectively deal with interval and fuzzy uncertainties that exist in the planning process of wetland restorations but also handle trade-offs between ecological environment benefits and economic cost. This framework was applied to a real-world wetland restoration planning problem in the northeast of China to verify its validity and examine the credibility of the constraints. The optimized results obtained from the framework that we have developed indicate that higher ecological and social benefits can be obtained with optimal restoration costs after using the wetland restoration decision-making framework. The optimal restoration measure allocation schemes obtained by IFLP under different credibility levels can help decision makers generate a range of alternatives, which can also provide decision suggestions to local managers to generate a satisfactory decision-making plan. Furthermore, a comparison was made between the IFLP model and ILP model in this study. The comparison results indicate that the IFLP model provides more information regarding ecological environment and economic trade-offs between the system objective, certainty, and reliability. This framework provides managers with an effective way to plan wetland restoration projects, while transference of the model may help solve similar problems.

Evaluating Risk-Propensity Assessment Methods: Patterns in Repeated Exposure to Emotional Stimuli

2020 ◽  
Vol 64 (1) ◽  
pp. 283-287
Author(s):  
Sahinya Susindar ◽  
Harrison Wissel-Littmann ◽  
Terry Ho ◽  
Thomas K. Ferris
Keyword(s):  
Decision Making ◽  
Emotion Regulation ◽  
Assessment Method ◽  
Decision Makers ◽  
Decision Task ◽  
Emotional States ◽  
Affective States ◽  
Risk Propensity ◽  
Emotional Stimuli ◽  
Comparison Results

In studying naturalistic human decision-making, it is important to understand how emotional states shape decision-making processes and outcomes. Emotion regulation techniques can improve the quality of decisions, but there are several challenges to evaluating these techniques in a controlled research context. Determining the effectiveness of emotion regulation techniques requires methodology that can: 1) reliably elicit desired emotions in decision-makers; 2) include decision tasks with response measures that are sensitive to emotional loading; and 3) support repeated exposures/trials with relatively-consistent emotional loading and response sensitivity. The current study investigates one common method, the Balloon Analog Risk Task (BART), for its consistency and reliability in measuring the risk-propensity of decision-makers, and specifically how the method’s effectiveness might change over the course of repeated exposures. With the PANASX subjective assessment serving for comparison, results suggest the BART assessment method, when applied over repeated exposures, is reduced in its sensitivity to emotional stimuli and exhibits decision task-related learning effects which influence the observed trends in response data in complex ways. This work is valuable for researchers in decision-making and to guide design for humans with consideration for their affective states.

scholarly journals Using Deliberative Democracy for Better Urban Decision-Making through Integrative Thinking

Urban Science ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 3
Author(s):  
Janette Hartz-Karp ◽  
Dora Marinova
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Deliberative Democracy ◽  
Decision Makers ◽  
Capital City ◽  
City Region ◽  
Multiple Sources ◽  
Trade Offs ◽  
Urban Issues ◽  
Sustainability Challenges

This article expands the evidence about integrative thinking by analyzing two case studies that applied the collaborative decision-making method of deliberative democracy which encourages representative, deliberative and influential public participation. The four-year case studies took place in Western Australia, (1) in the capital city Perth and surrounds, and (2) in the city-region of Greater Geraldton. Both aimed at resolving complex and wicked urban sustainability challenges as they arose. The analysis suggests that a new way of thinking, namely integrative thinking, emerged during the deliberations to produce operative outcomes for decision-makers. Building on theory and research demonstrating that deliberative designs lead to improved reasoning about complex issues, the two case studies show that through discourse based on deliberative norms, participants developed different mindsets, remaining open-minded, intuitive and representative of ordinary people’s basic common sense. This spontaneous appearance of integrative thinking enabled sound decision-making about complex and wicked sustainability-related urban issues. In both case studies, the participants exhibited all characteristics of integrative thinking to produce outcomes for decision-makers: salience—grasping the problems’ multiple aspects; causality—identifying multiple sources of impacts; sequencing—keeping the whole in view while focusing on specific aspects; and resolution—discovering novel ways that avoided bad choice trade-offs.

scholarly journals A dual-inexact fuzzy stochastic model for water resources management and non-point source pollution mitigation under multiple uncertainties

2014 ◽  
Vol 18 (5) ◽  
pp. 1793-1803 ◽  
Author(s):  
C. Dong ◽  
Q. Tan ◽  
G.-H. Huang ◽  
Y.-P. Cai
Keyword(s):  
Linear Programming ◽  
Point Source ◽  
Upper Bounds ◽  
Fuzzy Linear Programming ◽  
Chance Constrained Programming ◽  
Lower And Upper Bounds ◽  
Point Source Pollution ◽  
Pollution Mitigation ◽  
Trade Offs ◽  
Non Point Source Pollution

Abstract. In this research, a dual-inexact fuzzy stochastic programming (DIFSP) method was developed for supporting the planning of water and farmland use management system considering the non-point source pollution mitigation under uncertainty. The random boundary interval (RBI) was incorporated into DIFSP through integrating fuzzy linear programming (FLP) and chance-constrained programming (CCP) approaches within an interval linear programming (ILP) framework. This developed method could effectively tackle the uncertainties expressed as intervals and fuzzy sets. Moreover, the lower and upper bounds of RBI are continuous random variables, and the correlation existing between the lower and upper bounds can be tackled in RBI through the joint probability distribution function. And thus the subjectivity of decision making is greatly reduced, enhancing the stability and robustness of obtained solutions. The proposed method was then applied to solve a water and farmland use planning model (WFUPM) with non-point source pollution mitigation. The generated results could provide decision makers with detailed water supply–demand schemes involving diversified water-related activities under preferred satisfaction degrees. These useful solutions could allow more in-depth analyses of the trade-offs between humans and environment, as well as those between system optimality and reliability. In addition, comparative analyses on the solutions obtained from ICCP (Interval chance-constraints programming) and DIFSP demonstrated the higher application of this developed approach for supporting the water and farmland use system planning.

Synthesizing Different Preference Information by Fuzzy Programming Method

2011 ◽  
Vol 328-330 ◽  
pp. 2352-2357
Author(s):  
Jing Yang
Keyword(s):  
Decision Making ◽  
Group Decision ◽  
Fuzzy Programming ◽  
Fuzzy Linear Programming ◽  
Decision Makers ◽  
Preference Ordering ◽  
Utility Value ◽  
Preference Information ◽  
Preference Strength ◽  
Judgment Matrix

Group decision making problems with different forms of preference information are discussed. Firstly, four forms of preference information ( i.e. preference ordering, utility value, AHP judgment matrix and fuzzy judgment matrix) are introduced and the computing formulas are given to transform different forms of preference information into the form of fuzzy judgment matrix. A new method that involves in different preference strength of experts is studied. Then, the assessment of the group priorities is formulated as a fuzzy linear programming problem, maximizing the group’s overall satisfaction to get the group solution. The method can easily deal with missing judgments and different partiality intensity by decision makers. At the end, the feasibility and effectiveness of method is explained by an example.

scholarly journals A Comparative Study between GLP and GBWM

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Reda M. S. Abdulaal ◽  
Omer A. Bafail
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Comparative Study ◽  
Simplex Method ◽  
Multicriteria Decision Making ◽  
Decision Makers ◽  
The Other ◽  
Ideal Solution ◽  
Linguistic Variables ◽  
Best Worst Method

When decision-makers’ judgments are uncertain, they often express their opinions using grey linguistic variables. Once used, the data often retains its grey nature throughout all subsequent decision-making iterations. Multicriteria decision-making (MCDM) is a tool used when making complicated decisions and in circumstances where several criteria require evaluation to choose the most desirable option. Grey data serves as the basis for several MCDM methods. This paper compares two MCDM methods, Grey-Linear-Programming (GLP) and Grey-Best-Worst-Method (GBWM), in terms of the weights of decision criteria and their rankings. Moreover, Grey-The Technique for Order of Preference by Similarity to Ideal Solution (GTOPSIS) was used to rank the weights of the two methods. Study findings demonstrated that GBWM requires more mathematical calculations than GLP, based on linear programming's classic simplex method. On the other hand, when GTOPSIS follows GLP, the alternative rank does not change compared to when GTOPSIS followed GBWM. For the applications used in this comparison, GLP procedure is considered simpler than GBWM procedure.

Fuzzy Linear Programming: A Modern Tool for Decision Making

2012 ◽  
Author(s):  
Pandian M. Vasant ◽  
R. Nagarajan ◽  
Sazali Yaacob
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Production Planning ◽  
Membership Function ◽  
Industrial Production ◽  
Real Life ◽  
Fuzzy Linear Programming ◽  
Programming Approach ◽  
Modern Trend ◽  
Application Problem

The modern trend in industrial application problem deserves modeling of all relevant vague or fuzzy information involved in a real decision making problem. In the first part of the paper, some explanations on tri partite fuzzy linear programming approach and its importance have been given. In the second part, the usefulness of the proposed S-curve membership function is established using a real life industrial production planning of a chocolate manufacturing unit. The unit produces 8 products using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraints. A solution to this problem establishes the usefulness of the suggested membership function for decision making in industrial production planning. Key words: Fuzzy linear programming, Satisfactory solution; Decision maker; Implementer; Analyst; Fuzzy constraint; Vagueness.

A new approach to solve fully intuitionistic fuzzy linear programming problem with unrestricted decision variables

2021 ◽  
pp. 1-14
Author(s):  
Manisha Malik ◽  
S. K. Gupta ◽  
I. Ahmad
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Fuzzy Set ◽  
Linear Programming Problem ◽  
Fuzzy Numbers ◽  
Fuzzy Linear Programming ◽  
Planning Problem ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Decision Variables

In many real-world problems, one may encounter uncertainty in the input data. The fuzzy set theory fits well to handle such situations. However, it is not always possible to determine with full satisfaction the membership and non-membership degrees associated with an element of the fuzzy set. The intuitionistic fuzzy sets play a key role in dealing with the hesitation factor along-with the uncertainity involved in the problem and hence, provides more flexibility in the decision-making process. In this article, we introduce a new ordering on the set of intuitionistic fuzzy numbers and propose a simple approach for solving the fully intuitionistic fuzzy linear programming problems with mixed constraints and unrestricted variables where the parameters and decision variables of the problem are represented by intuitionistic fuzzy numbers. The proposed method converts the problem into a crisp non-linear programming problem and further finds the intuitionistic fuzzy optimal solution to the problem. Some of the key significance of the proposed study are also pointed out along-with the limitations of the existing studies. The approach is illustrated step-by-step with the help of a numerical example and further, a production planning problem is also demonstrated to show the applicability of the study in practical situations. Finally, the efficiency of the proposed algorithm is analyzed with the existing studies based on various computational parameters.

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