Evolutionary Multitasking Memetic Algorithm for Distributed Hybrid Flow-Shop Scheduling Problem With Deterioration Effect

In the production enterprises, the distributed hybrid flow-shop scheduling problems widely exist in the actual production controlling and decision, especially in the production of steel and aluminum. Considering the uncertain processing time in the actual production environment, the constraint of deteriorated variable processing time is added in some problems. In this paper, the distributed hybrid flow-shop scheduling problem with deterioration effect (DHFSP-DE) is investigated. The framework of evolutionary multitasking memetic algorithm (MTMA) is designed to address the proposed model. In the proposed method, evolutionary transfer learning is utilized to communicate between two independent DHFSP-DE solvers. The implicit knowledge of the scheduling scheme can be transferred into other solvers to guide the evolution of the population. The memetic algorithm combines a strategy of local intensification with a population-based paradigm. These strategies can capture the implicit knowledge of DHFSP-DE. This paper makes the first attempt to work on the framework of evolutionary multitasking learning for DHFSP-DE problems. The experimental results on the different instances show the effectiveness and efficiency of the proposed MTMA algorithm. Note to Practitioners —The distributed hybrid flow-shop scheduling problem with deterioration effect (DHFSP-DE) is modeled based on the production process of aluminum. The DHFSP-DE is also widely used in the production process of various industries. As the processing time of the job is varied with time, the determined scheduling problem is transformed into a scheduling problem with deterioration effect. This transformation makes the problem even more complicated. The problems are more complex than static problems because they require greater computational dimensionality for evolutionary computation, resulting in the use of computational resources. An evolutionary multitasking memetic algorithm is designed to solve DHFSP-DEs cooperatively. The solutions of the solver can be transferred to other solvers through the mapping of DHFSP-DEs. The transferred solution can affect the evolution process. The efficiency and effectiveness of the proposed MTMA are verified by the comparison experiments. In addition, the MTMA framework can be applied to other scheduling problems.