良好的拌合廠作業排程規劃，可以提升澆置效率，並降低成本。目前國內拌合廠業者大多依照以往經驗，估算平均的旅行時間，以進行拌合廠作業排程規劃。此作法忽略了實務營運上旅行時間之隨機特性，若此隨機性擾動過大時，則可能使原規劃的結果失去其優越性，亦即最佳化拌合廠作業排程結果可能不為實際最佳排程。此等最佳化拌合廠作業排程在營運中受到隨機因素擾動的影響，在過去未曾發現有文獻進行探討。有鑑於此，為規劃較符合實務情況的拌合廠作業排程，本研究考量實際營運時，旅行時間之隨機變動狀況及實務營運的相關限制，以總營運成本最小化為目標，構建一隨機性模式，針對拌合廠整體澆置作業的排程規劃進行最佳化的分析，以幫助業者在有限的澆置資源下作出最佳的混凝土生產作業及拌合車的調派決策，並提高澆置作業效率。 本研究參考國內實際的拌合廠作業方式，利用時空網路流動技巧，以系統最佳化的觀點，構建一隨機性拌合廠作業排程模式。之後，本研究修正隨機性旅行時間為一固定平均旅行時間，發展一確定性拌合廠作業排程模式。另外，本研究並發展一模擬評估方法，以評估實務排程、確定性與隨機性排程規劃的結果於實際營運環境中之績效優劣。在求解上，本研究預期可定式為含額外限制式之整數規劃網路流動問題，屬NP-hard問題，當問題規模變大時，可能難以在有限的時間內利用數學規劃軟體求得一最佳解。緣此，本研究發展一隨機最佳化啟發解法，以求解隨機性旅行時間之拌合廠作業排程模式。最後，為測試本研究模式與評估方法的實用績效，本研究以台灣一拌合廠之實際營運資料為範例進行測試與分析，以了解啟發式求解演算法之績效及特性，進而提出結論與建議。 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.
