Online due date assignment in dynamic and stochastic scheduling environments with family setups under tardiness and quoted lead time considerations

Abstract

In this study, due date assignment problem is addressed in a single machine dynamic and stochastic environment with family setups. A due date is to be assigned for each new job immediately at the time of its arrival. While assigning a due date for a new arrival, non-complete jobs that have arrived before that job and whose due dates are already assigned, are also considered and rescheduled. While assigning a short due date for a new arrival as close as possible, compliance to assigned due dates of noncomplete jobs is necessary. These two objectives con ict with each other. In order to solve this problem, a two-phase solution methodology is proposed. In the rst phase, a capacity allocation takes place for families before observing any actual jobs arrivals, based on expected work load and arrival estimation, in a periodic and static manner. In this phase, families can be assigned to batches and a batching structure is formed. In the second phase, a due date is assigned for the new arrival immediately in an online fashion, based on the outputs of the rst phase. A mixed integer programming model and a heuristic algorithm are developed for each phase. Simultaneously, a discrete event simulation is carried out to imitate a real production system. The performance of the designed batching policy is measured through the developed simulation model and results are reported under di erent system parameters.