【正文】 management of the overall project ? workshop: an instance of the pa class – scheduling and resource allocation on a department or CNC machine ? database agent: an instance of the pa class – an integration agent, integrates ExPlanTech with factory ERP ? material agent: an instance of the pa class – integrates an MRP material resource planning system ? Special visualization and user manipulation metaagent 仿真模擬 ? 仿真模擬提供對全部任務，次序 ,先后和時間選擇決定的結果的直接觀察，能夠以較低的計算成本對方案進行快速而詳細的分析。 分解 Basic Deposition Idea ? Compared to “ manufacturing” problems: 1. Unknown type and number of batches (tasks)。 ? 在事件數目遠小于時間段數目時， NUDM的性能明顯優(yōu)于UDM。 StateTask Network (STN) Representation BsA2=BsA4=20 S1 40% 25% S3 S2 60% S4 75% BIA,S1,2=8 BOA,S3,4=5 BIA,S2,2=12 BOA,S4,4=15 StateTask Network (STN) Representation Inventory S2 S3 0 1 2 3 4 5 6 Time (h) Reactor 1 Reactor 2 Reactor 3 Column Heating Reaction 1 Reaction 2 Reaction 3 Separation 0 1 2 3 4 5 6 Time (h) 優(yōu)化調度模型－ 時間表示方式 Kondili, Pantelides Sargent (1993)。 中間貯罐并不能夠完全地解決時間與能力瓶頸。 ? 動態(tài)調度依據生產計劃和實際工況響應進行調度，與靜態(tài)調度不同，需要考慮實時性。 向后順序計劃固定結束時間 ， 決定開始時間 ， 產生一個不會延遲的計劃 ， 然而 ， 計劃也許有不可行的開始時間 。 ? 目的在于尋找最優(yōu)的設備加工任務次序，使得等待時間與切換時間最小。 n個工件在 m臺機器上的加工順序相同，工件在機器上的加工時間是給定。 啟發(fā)式規(guī)則 ? 主要的優(yōu)點是啟發(fā)式規(guī)則往往利用與該問題相關的知識，因此，在通常情況下能夠在較短的時間內得到較好方案。 使得： 目標函數最優(yōu) 。 連續(xù)型流程工業(yè)生產調度 ? 連續(xù)型生產過程適合于固定的大批量產品的生產，其特點是生產過程工藝流程基本不變，物料流是連續(xù)的。 Rodriguez et al. (2023)。 對于凸問題能夠得到全局最優(yōu)解。 some scheduling problems ? Special “ constructs” and constraints for classes of problems ? Constructs: activity X, unary resource Y ? Constraints: X requires Y (GLOBAL) A ? B, A ? B (LOGIC) ? Highly Expressive ? Effective local search ? Search is based on constraint propagation Mathematical vs. Constraint Programming Constraint Programming ? Fast algorithms for special problems Computationally effective for highly constrained, feasibility and machine sequencing problems Not effective for optimization problems with plex structure and many feasible solutions Mathematical Programming ? Intelligent search strategy but putationally expensive for large problems Computationally effective for optimization problems with many feasible solutions Not effective for feasibility problems and machine sequencing problems MAIN IDEA Depose problem into two parts ? Use MP for highlevel optimization decisions ? Use CP for lowlevel sequencing decisions Proposed Strategy Production Z* Upper bound Feasible solution 0 2 4 6 8 10 Iterations Fix no/type of tasks and assignment decisions Problem is highly constrained: suitable for CP If feasible, obtain lower bound Add integer cut and continue until bounds converge ? Express problem in an aggregated MP form ? Use MP to identify potentially good solutions ? Fix no/type of tasks, assignment of tasks to units ? Fix no/type of tasks and assignment decisions ? Problem is highly constrained: suitable for CP ? If feasible, obtain lower bound ? Add integer cut and continue until bounds converge Solve MIP Master Problem max production . RELAXATION Obtain UB Solve CP Sub problem max production . ALL CONSTRAINTS w/ fixed no/type of tasks Obtain LB Solve MIP Master Problem max production . RELAXATION Obtain UB Fix no/type of tasks,assignment to units Add integer cuts Fix no/type of tasks and assignment decisions Problem is highly constrained: suitable for CP If feasible, obtain lower bound Add integer cut and continue until bounds converge Proposed Formulation Solve MIP Master Problem max production . RELAXATION Obtain UB CP Sub problem(CP) max production . ALL CONSTRAINTS w/ fixed no/type of tasks Obtain LB MIP Master Problem(MP) max production . SOME CONSTRAINTS Obtain UB Fix no/type of tasks,assignment to units Add integer cuts Tasks ? Activities Units ? Unary Resources Utilities ? Discrete Resources States ? Reservoirs Zic = 1 if batch c of task i is carried out Integer Cuts INTsCSCciZZsBBSSciZBBZBjMSZDssicici cicIiti cicOitsicMAXiicicMINijIi cicic???????????????????? ?? ?? ? ||, , 10)(?? Generalization of Deposition Framework I Multipurpose Batch Plant S3 10% 90% Heat S4 60% Reaction 3 S7 40% Separation Reaction1 Reaction3 Reaction2 70% 30% S5 S6 S2 S1 Heat Master MIP Problem CP Sub problem Zic = 1 if batch c of task i is carried out Bic = batch size of batch c of task i Ss = inventory level of state s ? ? ? ?? ? ? ?? ? ? ?? ? ? ?? ? ciMSendciTaskscisSta teBcons umesciTaskscisSta teBcons umesciTaskcirUtilit yRrequ iresciTaskcjIijjUnitrequ iresciTaskFPsdBciBRciBBBicspisiscisici csOicsiciiicMAXiicMINi??????????????????????????? ?, ., , , ,),(, , , , ????INTsCSFPsdSsBBSSciZBBZBjESTSTMSZDssssi cicIiti cicOitsicMAXiicicMINijIi cjjicic??????????????????? ?? ?? ?? , 0)(?? Generalization of Deposition Framework General Multistage Plant Master MIP Problem CP Sub problem Zic = 1 if batch c of task i is carried out Bic = batch size of batch c of task i Ss = inventory level of state s ? ? ? ?? ? ? ?? ? ? ?? ? ciMSendciTaskscisSta teBcons