A Steady-State Genetic Algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability

This paper presents an evolutionary algorithm-based steady-state grouping genetic algorithm (SSGGA) for the single-machine scheduling problem with periodic machine availability (SinMSPMA problem) whose objective is to minimize the makespan. This problem is NP -hard which arises in several real production scenarios, where industries are giving importance of maintenance activities in their production scheduling systems due to not only improving the efficiency and safety of production, but also increasing the productivity. The SinMSPMA problem belongs to a class of grouping problems. Due to its grouping-aspect structure, the proposed SSGGA encodes each chromosome as a set of periods (groups) and relies on combining specialized genetic operators with a problem-specific repair operator in order to generate an offspring. On available benchmark instances, computational results of SSGGA indicate that SSGGA outperforms the best three approaches out of 19 existing approaches. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.