Metaheuristics for the flow shop scheduling problem with maintenance activities integrated

This paper deals with a flow shop scheduling problem in which machines are not available during the whole planning horizon and the periods of unavailability are due to random faults. Since they are subject to failures, both corrective maintenance activities and scheduled maintenance activities are performed to increase their availability. Hence, jobs and maintenance tasks are jointly considered to find the optimal schedule. The objective is to find the optimal integrated job-planned maintenance sequence that minimises the makespan and the earliness-tardiness penalty. To this aim, we propose two novel meta-heuristic algorithms obtained modifying a standard Genetic Algorithm (GA) and Harmony Search (HS). Numerical results obtained from experiments considering different problem sizes and configurations show that the proposed Harmony Search and Genetic Algorithm are efficient to approach the integrated job-maintenance scheduling problem.

Automated summary

This paper addresses a flow shop scheduling problem where machines are randomly unavailable due to faults, requiring corrective and scheduled maintenance activities to improve availability. Two novel meta-heuristic algorithms, derived from a standard Genetic Algorithm and Harmony Search, are proposed to optimize the integrated job-maintenance schedule for minimizing makespan and earliness-tardiness penalties. Numerical results show that the proposed algorithms are efficient in approaching this integrated scheduling problem.