A sequential genetic programming method to learn forward construction heuristics for order acceptance and scheduling

Abstract

Order acceptance and scheduling (OAS) is a hard optimisation problem in which both acceptance decisions and scheduling decisions must be considered simultaneously. Designing effective solution methods or heuristics for OAS is not a trivial task, especially to deal with different problem configurations and sizes. This paper proposes a new heuristic framework called forward construction heuristic (FCH) for OAS and develops a new sequential genetic programming (SGPOAS) method for automatic design of FCHs. The key idea of the new GP method is to learn priority rules directly from optimal scheduling decisions at different decision moments and evolve a set of rules for FCHs instead of a single rule as shown in previous studies. The results show that evolved FCHs are significantly better than evolved single priority rules. The evolved FCHs are also competitive with the existing meta-heuristics in the literature and very effective for large problem instances.