EVALUATION OF SWINE ODOR MANAGEMENT STRATEGIES IN A FUZZY MULTI-CRITERIA DECISION ENVIRONMENT

The paper evaluates swine odor management strategies using the fuzzy extension of the Analytical Hierarchy Process (AHP), which is a multiple criteria decision making approach based on fuzzy scales. The evaluation is conducted using data from our cost effectiveness study of odor management strategies and our on farm studies relating odor to various management practices. These strategies include manual oil sprinkling, automatic oil sprinkling, wet scrubber, diffusion-coagulation-separation (DCS) deduster, pelleting feed, and draining shallow pit weekly. The criteria employed to evaluate the strategies are odor reduction efficiency, costs, nutrients in manure, and other benefits. Two producer profiles are considered: (a) producers who are pressured to achieve maximum reduction in odor emissions; and (b) producers who are constrained with limited financial resources. Both of these profiles are reflective of current situations for some producers. The results show that, as the scale fuzziness decreases, the preference of the first producer profile over the strategies from high to low is DCS deduster, pelleting feed, automatic oil sprinkling, manual oil sprinkling, draining pit weekly, and wet scrubber while the preference of the second producer profile is draining pit weekly, DCS dedusters, automatic oil sprinkling, wet scrubbers, pelleting feed, and manual oil sprinkling.Livestock Production/Industries,

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization

Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması

Mean-variance portfolio optimization model, introduced by Markowitz, provides a fundamental answer to the problem of portfolio management. This model seeks an efficient frontier with the best trade-offs between two conflicting objectives of maximizing return and minimizing risk. The problem of determining an efficient frontier is known to be NP-hard. Due to the complexity of the problem, genetic algorithms have been widely employed by a growing number of researchers to solve this problem. In this study, a literature review of genetic algorithms implementations on mean-variance portfolio optimization is examined from the recent published literature. Main specifications of the problems studied and the specifications of suggested genetic algorithms have been summarized

Project scheduling under uncertainty using fuzzy modelling and solving techniques

In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance

QML-Morven : A Novel Framework for Learning Qualitative Models

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