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

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 conflict with each other. In order to solve this problem, a two-phase solution methodology is proposed. In the first 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 first 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 different system parameters.