Pan Quan-ke

Optimization of Fuzzy Job-shop Scheduling with Multi-process Routes and its Co-evolutionary Algorithm

This paper studies the job-shop scheduling problems of multi-process routes with fuzzy processing time and fuzzy due date, and establishes a fuzzy scheduling model to reach the optimization goals of maximum average satisfaction index. Then a co-evolutionary algorithm combined with genetic algorithm and discrete particle swarm optimization algorithm is presented, which improves the chromosome encoding scheme based on working procedure, with the idea of collaboration and the mechanism of feedback, this algorithm guides the evolutionary process of two populations which respectively use genetic algorithm and discrete particle swarm optimization algorithm in an effective and complementary way. The simulation results show that the co-evolutionary algorithm is a feasible method.

A novel grouping harmony search algorithm for the no-wait flow shop scheduling problems with total flow time criteria

This paper presents a novel grouping harmony search algorithm for solving the no-wait flow shop scheduling problems with total flow time criteria. Flow-shop scheduling problem (FSSP) is a typical NP-hard combinational optimization. Firstly, the HS optimization mechanism and FSSP is presented and the total flow time is calculated by a novel method. Secondly, a largest-order-value rule is used to transform harmony in harmony memory from real vectors to job sequence so that the harmony search can be applied for FSSP. Thirdly, the main contribution of this study is due to the fact that harmony memory is divided into groups and groups share information reciprocally in the process of optimization. At last, simulations demonstrate the efficiency, effectiveness and robustness of harmony search algorithm for no-wait FSSP with total flow time criteria.