FCVLP: A fuzzy random conditional value-at-risk-based linear programming model for municipal solid waste management

A fuzzy random conditional value-at-risk-based linear programming (FCVLP) model was proposed in this study for dealing with municipal solid waste (MSW) management problems under uncertainty. FCVLP improves upon the existing fuzzy linear programming and fuzzy random conditional value-at-risk methods by allowing analysis of the risks of violating constraints that contain fuzzy parameters. A long-term MSW management problem was used to illustrate the applicability of FCVLP. The optimal feasibility solutions under various significance risk levels could be generated in order to analysis the trade-offs among the system cost, the feasibility degree of capacity constraints, and the risk level of waste-disposal-demand constraints. The results demonstrated that (1) a lower system cost may lead to a lower feasibility of waste-facility-capacity constraint and a higher risk of waste-disposal-demand constraint; (2) effects on system cost from vague information in incinerator capacity inputs would be greater than those in landfill capacity inputs; (3) the total allowable waste allocation would vary significantly because of the variations of risk levels and feasibility degrees. The proposed FCVLP method could be used to identify optimal waste allocation scenarios associated with a variety of complexities in MSW management systems

Reasoning and querying bounds on differences with layered preferences

Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and calories. Recently, some approaches have extended the BoDs framework in a fuzzy, \u201cnoncrisp\u201d direction, considering probabilities or preferences. While previous approaches have mainly aimed at providing an optimal solution to the set of constraints, we propose an innovative class of approaches in which constraint propagation algorithms aim at identifying the \u201cspace of solutions\u201d (i.e., the minimal network) with their preferences, and query answering mechanisms are provided to explore the space of solutions as required, for example, in decision support tasks. Aiming at generality, we propose a class of approaches parametrized over user\u2010defined scales of qualitative preferences (e.g., Low, Medium, High, and Very High), utilizing the resume and extension operations to combine preferences, and considering different formalisms to associate preferences with BoDs. We consider both \u201cgeneral\u201d preferences and a form of layered preferences that we call \u201cpyramid\u201d preferences. The properties of the class of approaches are also analyzed. In particular, we show that, when the resume and extension operations are defined such that they constitute a closed semiring, a more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation of the constraint propagation algorithms

Preference approach to fuzzy linear inequalities and optimizations

[[abstract]]In this study we first present the preference structures in decision making as a generalized non-linear function. Then, we incorporate it into a fuzzy linear inequality of which the progressive and conservative manners are described by the concept of target hyperplanes. Thus, ones preference domain is said to be bounded by such soft constraint. When facing different situations, a membership function is defined as an evaluation function for incorporating ones optimistic or pessimistic attitude into this soft constraint. Once a goal is pursued, the problem is transformed into a symmetric fuzzy linear program. Based on max–min principle, an auxiliary crisp model in the form of a generalized fractional program is derived. Then, Dinkelbach-type-2 and the bisection algorithms are adopted for solution. Finally their simulation results of an uncertain production scheduling problem are reported.[[fileno]]2020402010003[[department]]工工