Scheduling Jobs in a Two-Stage Hybrid Flow Shop with a Simulation-Based Genetic Algorithm and Standard Dispatching Rules

The paper proposes a simulation-based hyperheuristics approach to generate schedules for a two-stage hybrid flow shop scheduling problem with sequence-dependent setup times. The scheduling problem is derived from a company that is assembling printed circuit boards. A genetic algorithm determines sequences of standard dispatching rules that are evaluated by a discrete-event simulation model minimizing a multi-criteria objective composed of makespan and total tardiness. To reduce the computation time of the algorithm a dispatching rule-based chromosome representation is used containing a sequence of dispatching rules and time intervals in which the rules are applied. Different experiment configurations and their impact on solution quality and computation time are analyzed. The optimization model generates efficient schedules for multiple real-world data sets.