| dc.description.abstract | A good plant work schedule can improve the effectiveness of the RMC placement and thus reduce the operating costs. In current practices, the plant work schedule is typically designed by the staff’s experience, in accordance with the projected(or average) fleet travel times, meaning that stochastic disturbances arising from variations in vehicle travel times in actual operations are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during daily operations, the planned plant work schedule could be disturbed enough to lose its optimality. Since there has been no research on plant work scheduling problems that can account for stochastic fleet travel times, in this research stochastic disturbance of daily travel times that occur in actual operations are considered from the basis of the carrier’s perspective. We develop a stochastic plant work scheduling model, with the objective of minimizing the total operating costs. The model is expected to be useful planning tool for carriers to decide on their optimal plant work schedules in their operations.
We employ network flow technique with the system optimization perspective, to construct a stochastic plant work scheduling model. Then, we modified the variational travel time parameters in the stochastic plant work scheduling model as fixed variable to develop a deterministic scheduling model. In addition, we still also develop a simulation-based evaluation method to evaluate the actual operation, deterministic and stochastic scheduling models in real world. The model is formulated as integer network flow problem with side constraints, which is characterized as NP-hard. Since the problem size is expected to be huge, the model is difficult to solve in a reasonable time. Therefore, we develop a heuristic, for solving stochastic plant work scheduling problems. Numerical tests based on real operating data from RMC plant were performed to evaluate the proposed solution algorithm. Conclusions and suggestions were given finally. | en_US |