Enhancing the best-first-search F with incremental search and restarts for large-scale single machine scheduling with release dates and deadlines

This paper presents a new heuristic search \(\hbox {F}_{IR}\) ${\text{F}}_{\mathit{IR}}$ that determines the feasibility of scheduling a set of jobs on a single machine where each job is characterized by its release date, processing time, and deadline. \(\hbox {F}_{IR}\) ${\text{F}}_{\mathit{IR}}$ uses incremental search and restart strategies to scale the best-first-search heuristic F (Laalaoui and M’Hallah, in: IEEE symposium on evolutionary scheduling and combinatorial optimisation (SSCI) 2022, IEEE, Singapore, 2022). Experimental results provide computational evidence of the superiority of \(\hbox {F}_{IR}\) ${\text{F}}_{\mathit{IR}}$ for large-scale instances. In fact, \(\hbox {F}_{IR}\) ${\text{F}}_{\mathit{IR}}$ outperforms the IBM ILOG CP constraint programming solver, the CPLEX mixed integer programming solver, and existing heuristics. It finds feasible solutions for many instances with 100,000 jobs in less than one minute.