A Greedy Cooperative Co-Evolutionary Algorithm With Problem-Specific Knowledge for Multiobjective Flowshop Group Scheduling Problems
研究了同时最小化完工时间、总流动时间和总能耗的流水车间序列依赖分组调度问题，提出混合整数线性规划模型和贪婪协同进化算法，实验表明算法优于现有方法。
The flowshop sequence-dependent group scheduling problem (FSDGSP) with the production efficiency measures has been extensively studied due to its wide industrial applications. However, energy efficiency indicators are often ignored in the literature. This article considers the FSDGSP to minimize makespan, total flow time, and total energy consumption, simultaneously. After the problem-specific knowledge is extracted, a mixed-integer linear programming model and a critical path-based accelerated evaluation method are proposed. Since the FSDGSP includes multiple coupled subproblems, a greedy cooperative co-evolutionary algorithm (GCCEA) is designed to explore the solution space in depth. Meanwhile, a random mutation operator and a greedy energy-saving strategy are employed to adjust the processing speeds of machines to obtain a potential nondominated solution. A large number of experimental results show that the proposed algorithm significantly outperforms the existing classic multiobjective optimization algorithms, which is due to the usage of problem-related knowledge.