Solving multi-scenario hybrid flow shop scheduling problem based on an improved probe machine model

Abstract

The hybrid flow-shop scheduling problem is widely present and applied in industries such as production, manufacturing, transportation, and aerospace. In recent years, due to the advantages of nonlinear access and fully parallel processing, the probe machine has shown powerful computing capabilities and promising applications in solving various combinatorial optimization problems. This work firstly proposes an Improved Probe Machine with Multi-Level Probe Operations (IPMMPO) and ingeniously designs general data libraries and probe libraries tailored for multi-scenario HFS problems, including HFS with identical parallel machines and HFS with unrelated parallel machines, no-wait scenario, and standard scenario. Secondly, based on the data libraries of the IPMMPO, two tuple sets suitable for constraint programming modeling are further designed as data preprocessing. Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. The results demonstrate that the proposed IPMMPO-CP outperforms the compared algorithms and models.