Dynamic control of a closed two-stage queueing network for outfitting process in shipbuilding

The U.S. Naval shipbuilding industry faces significant challenges to build ships on-time and within budgeted cost. To achieve greater efficiency and timeliness in shipbuilding, we developed a flexible two-stage queueing model under a CONWIP job release policy to enhance the planning and control of the outfitting process, one of the key processes in shipbuilding. The model is formulated using Markov Decision Processes which can provide (1) the optimal dynamic control policy and (2) the optimal cost. The numerical results showed that the optimal control policy is a state dependent threshold type policy and very complex to analyze. Therefore, we developed a static model to simplify the dynamic model and used Mean Value Analysis to gain insights. Using both data from the dynamic model and the static model, we developed a regression model to calculate a threshold policy heuristic. Testing reveals that the performance of this heuristic is very close to the optimal. •An innovative model for outfitting processes in shipbuilding.•Queueing networks models capture the variation of the process.•System control is based on an analytical congestion model.•A new MDP model captures the essential features of the problem.•A regression-based heuristic benefits from an exact MDP and a simpler MVA model.•A pre-processing stage yields a fast real time heuristic that performs very well.