Order-based genetic algorithm for flow shop scheduling

Flow shop scheduling is one of the most well-known production scheduling problems and a typical NP-hard combinatorial optimization problem with strong engineering background. This paper presents an order-based genetic algorithm for flow shop scheduling, which borrows the idea of ordinal optimization to reduce computation and ensure the quality of the solution found and enforces the evolutionary searching mechanism and learning capability of the genetic algorithm. With the guidance of ordinal comparison and by emphasizing the order-based search and elitist-based evolution in the proposed approach, a good enough solution can be guaranteed with high confidence level and reduced computation quantity, which is demonstrated by the numerical simulation based on some benchmarks. Moreover, some parameter sensitivities are presented and discussed.