A multi-surrogate genetic programming hyper-heuristic algorithm for the manufacturing project scheduling problem with setup times under dynamic and interference environments

Abstract

In this study, in order to cope with the uncertain environments and space interference scenarios encountered in the production process for a class of manufacturing projects, we propose a novel manufacturing project scheduling problem with setup times under dynamic and interference environments (MPSPST-DIE), and design a novel multi-surrogate genetic programming hyper-heuristic (HH-MGP) algorithm to address it. Firstly, MPSPST-DIE is required to make decisions on the activity schedule, resource setup and space allocation. Therefore, we modify the traditional resource based policy class that only contains the activity schedule and simulate the entire scheduling process. Secondly, a new hyper-heuristic genetic programming algorithm is designed to automatically evolve activity rules, resource setup rules and space allocation rules simultaneously. Moreover, the multi-surrogate is devised to improve the performance of the basic genetic programming (GP) algorithm. In addition, a new evolutionary learning mechanism is embedded in the multi-surrogate. Different surrogates learn from each other to complement each other’s strengths. Finally, numerical instances of the MPSPST-DIE are generated by configuring specific parameters of spatial resources and extensive numerical experiments are performed. At the same time, we implement the Taguchi experiment for the sensitivity analysis of parameters. The comparative analysis between the HH-MGP and traditional rules is performed. Further, the performance comparison between the multi-surrogate and other surrogates is also conducted. The experimental results show that the evolved rule of the HH-MGP performs better than the traditional rules for the MPSPST-DIE. The performance of the multi-surrogate model added to the GP algorithm are generally better than the single-surrogate model and no-surrogate.