Fuzzy model of choosing an operation combination when identifying the best available technology

Objective: modeling the mechanism of minimizing the enterprise’s integrated operating costs by combining the technological operation, permissible for environmental limitations. Methods: fuzzy mathematical programming methods were used for the model construction. Results: the modern principles of regulation of the industrial enterprises having a significant negative impact on the environment are based on the best available technologies conception, which stimulates the innovative “green” technologies development. In view of the complexity of identifying the best of competing alternative technologies, the development of models and methods for evaluating alternative options is an urgent task. The article presents a model for choosing the combination of various technologies admissible on ecological restrictions for minimizing the total operational costs of an enterprise when ensuring the admissible level of negative impact on the environment. In some cases it enables to make a more effective ecologically and economically choice. Scientific novelty: in contrast to the existing models of choice, which a priori identify a single technology, the proposed model allows to expand the set of available alternatives to include not only the basic one, but also a combination of operations from different technologies.Practical relevance: the model allows enterprises to implement the optimal choice of the best available technology in environmentally and economically conflict situations

Solving Fuzzy Job-Shop Scheduling Problems with a Multiobjective Optimizer

International audienceIn real-world manufacturing environments, it is common to face a job-shop scheduling problem (JSP) with uncertainty. Among different sources of uncertainty, processing times uncertainty is the most common. In this paper, we investigate the use of a multiobjective genetic algorithm to address JSPs with uncertain durations. Uncertain durations in a JSP are expressed by means of triangular fuzzy numbers (TFNs). Instead of using expected values as in other work, we consider all vertices of the TFN representing the overall completion time. As a consequence, the proposed approach tries to obtain a schedule that optimizes the three component scheduling problems (corresponding to the lowest, most probable, and largest durations) all at the same time. In order to verify the quality of solutions found by the proposed approach, an experimental study was carried out across different benchmark instances. In all experiments, comparisons with previous approaches that are based on a single-objective genetic algorithm were also performed