Heuristics for job shop scheduling with volatile machine constraints

An alteration of the job shop scheduling problem, concerning advertisement scheduling on digital advertisement spaces, is presented. Dispatching Rules (DR), Iterated Local Search (ILS) and Genetic Algorithms (GA) are discussed and applied to the problem space. The results show that ILS is the best performing heuristic, and surpasses the other heuristics especially in large problem spaces (≥ 100 machines, ≥ 100 jobs). The results match previously made findings, which indicates that effects on large-scale problems should be further researched in conjunction with amalgam algorithms between DR, GA and ILS.