Integrated transportation system and workcenter scheduling

Timely and cost-effective production of assembled products concerns diverse scheduling and logistic activities on the factory shop-floor. For improved schedules and better coordination among these activities, simultaneous scheduling of material handling transporters (such as Automatic Guided Vehicles or AGVs) and manufacturing equipment (such as machines and workcenters) is considered in this study. Given the shipping schedule for the end-items, the objective of the integrated problem is to minimize the cumulative lead time of the overall production schedule (i.e., total makespan) for on-time shipment, and to reduce material handling and inventory holding costs on the shop-floor. To reduce Work-In-Process (WIP) costs, a Just-In-Time (JIT) production strategy is implemented by ‘back-flushing’ all operations as late as possible, but without backlogging the end-items. Minimizing the makespan is then formulated as a transportation integrated scheduling problem. This problem is NP-hard. For industrial sized problems, an effective heuristic is developed to simultaneously schedule manufacturing and material handling operations by exploiting the critical path of an integrated operations network. The performance of the proposed heuristic TIPSA is evaluated via extensive numerical studies and compared with the traditional, sequential scheduling approach. The superiority of the integrated heuristic is well demonstrated.; To further improve on TIPSA a column-generation based solution approach is developed. The column-generation procedure consists of two problems: (1) The master problem selects from a set of choices, the sequence of operations and their assignment to multiple replicates such that the makespan is minimized. (2) The subproblem generates new sequences of operations for the workcenters with an attempt to minimize reduced cost with respect to a subset of the constraints of the master problem. The column-generation procedure has shown significant improvement over the TIPSA heuristic solution. Furthermore, the solutions generated by the column-generation on the average are within 15% of the lower bound obtained by solving the LP relaxation of the original problem. We also develop improved lower bound schemes based on Lagrangean Relaxation.