MCFP: A Monte Carlo Simulation-based Fuzzy Programming Approach for Optimization under Dual Uncertainties of Possibility and Continuous Probability

Abstract

The efficiency and confidence of decision making much rely on accurate information and objective judgement, however, which are usually compromised by uncertainties existing in the system. Although in many studies uncertainties are reflected during optimization processes, few models considered the dual uncertainties of possibility and continuous probability. This study proposed a Monte Carlo simulation-based fuzzy programming (MCFP) approach to handle such dual uncertainties. The developed approach was tested by a municipal solid waste management (MSW) problem to demonstrate its feasibility and efficiency. The results indicated that the proposed approach could obtain a reliable solution and adequately support the decision making process in MSW management. It is significantly advantageous in handling the coexistence of various fuzzy sets and complex probability distributions when compared to the conventional fuzzy stochastic programming approaches. Furthermore, three levels of the optimal results to help decision makers effectively manage the composting facility: the entire distributions for general policy makers in long term policy making and trade-off, risk and reliability analyses of the system; the range of most frequent occurrences for project/plant managers in a medium arrangement; and the expected values for the plant operators for short term operating and adjusting the facility to minimize the system cost. Such different levels of decision supports could make the MCFP approach highly feasible, flexible and adaptable in real-work applications.

Safety Guarantee System of Drinking Water Source in Three Gorges Reservoir Area and Its Application in Huangjuedu Drinking Water Source Area

“…Here, 85,500 grid points are calculated, as shown in Figure 3. Hydrological data of the dry period was Q = 3480 m 3 /s; Z = 169.86 m. By comparing the simulated and measured values of ammonia concentration, the accuracy of the transverse and longitudinal diffusion coefficients was verified [32][33][34]. The result shows that the average absolute error of the algorithm is less than 6% in the calculation of pollutant emergencies in the water source area.…”

Development of a Maximum Entropy-Archimedean Copula-Based Bayesian Network Method for Streamflow Frequency Analysis—A Case Study of the Kaidu River Basin, China

“…However, extensive uncertainties in hydrological processes are among the major challenges to deriving reliable water system management strategies. Uncertainties in hydrological processes have been addressed with a large number of research works [6][7][8][9][10]. For hydrological processes, the variation of natural circumstances, the lack of historical records, and the limitation of measurement may cause uncertainties in input information, i.e., randomness in the streamflow inputs [11].…”

A Multi-Objective Energy and Environmental Systems Planning Model: Management of Uncertainties and Risks for Shanxi Province, China

“…Among them, inexact programming methods are widely used to provide comprehensive management solutions under specific system conditions, which were generally based on fuzzy mathematical programming (FMP) and stochastic mathematical programming (SMP). FMP can effectively address the imprecise/vague information in decision-making problems, which is formulated by possibility distribution with the subjective experience/knowledge of specialists, stakeholders, and decision makers [2][3][4][5][6][7]. For example, Zhang et al [8] proposed a fuzzy credibility programming method for regional EPS to address the imprecision of the constrained left-hand parameters.…”