Multi-perspective collaborative scheduling using extended genetic algorithm with interval-valued intuitionistic fuzzy entropy weight method

Abstract

Scheduling multiple heterogeneous tasks in a manufacturing system to satisfy customized requirements becomes challenging, especially in uncertain manufacturing environment. In cloud manufacturing, a serious problem is to schedule multiple heterogeneous tasks to balance the benefit conflicts among customers, manufacturing enterprises, and manufacturing platform comprehensively. Therefore, this study formulates the multi-task scheduling problem mathematically as a new fuzzy mixed-integer linear programming (FMILP) model based on multi-perspective collaborative optimization and fuzzy set theory. To solve the FMILP model, an extended genetic algorithm (EGA) with the interval-valued intuitionistic fuzzy entropy weight (IVIFEW) method is proposed. The IVIFEW method is adapted to obtain the preference of QoS attributes and task priority. In addition, the basic genetic algorithm is improved by integrating a migration operator, local search, and restart strategy to maintain the diversity of population and enhance the exploitation ability. A suitable parameter combination of EGA is found in a series of experiments based on the Taguchi method. The experimental results demonstrate that the proposed EGA solves the FMILP model effectively, providing better optimal solutions compared with the baseline algorithms.