Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r ...Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r Aided Process Planning (CAPP) is a step in this direction. Most of the existin g CAPP systems do not consider scheduling while generating a process plan. Sched uling is done separately after the process plan has been generated and therefore , it is possible that a process plan so generated is either not optimal or feasi ble from scheduling point of view. As process plans are generated without consid eration of job shop status, many problems arise within the manufacturing environ ment. Investigations have shown that 20%～30% of all process plans generated are not valid and have to be altered or suffer production delays when production sta rts. There is thus a major need for integration of scheduling with computer aide d process planning for generating more realistic process plans. In doing so, eff iciency of the manufacturing system as a whole is expected to improve. Decision support system performs many functions such as selection of machine too ls, cutting tools, sequencing of operations, determination of optimum cutting pa rameters and checking availability of machine tool before allocating any operati on to a machine tool. The process of transforming component data, process capabi lity and decision rules into computer readable format is still a major obstacle. This paper proposes architecture of a system, which integrates computer aided p rocess-planning system with scheduling using decision support system. A decisio n support system can be defined as " an interactive system that provides the use rs with easy access to decision models in order to support semi-structured or u nstructured decision making tasks".展开更多
In this paper, the design, customization and implem en tation of an integrated Advanced Planning and Scheduling (APS) system for a Semi conductor Backend Assembly environment is described. The company is one of the w ...In this paper, the design, customization and implem en tation of an integrated Advanced Planning and Scheduling (APS) system for a Semi conductor Backend Assembly environment is described. The company is one of the w orldwide market leaders in semiconductor packaging technology. The project was d riven by the company’s quest to achieve a competitive edge as a manufacturing po werhouse by providing the shortest possible cycle time with a high degree of fle xibility through the application of Computer Integrated Manufacturing (CIM) tech nology. Gintic was responsible for the Planning & Scheduling functions through o ur APS tool kit, which is called Gintic Scheduling System (GSS). Our APS system is to be integrated with the other two key software systems, namely, the Enterpr ise Resource Planning (ERP) and Manufacturing Execution System (MES), with the C IM framework. The project was divided into four major execution phases. Phase One activities w ere focused on the gathering and analysis of the end users requirements in order to establish the ’As-Is’ situation and the wish list & the expectation of the ’To-Be’ system. Planning and Scheduling prototypes were built using GSS to iden tify the functionality gap between the existing GSS system and the ’To-Be’ mode l, in order to determine the customization effort needed. The project team perfo rmed detailed system analysis, design and development of the ’To-Be’ system dur ing Phase Two of the project. There are a total of four planning and scheduling modules, including Capacity Planning (CP), Daily Lot Release (DLR), Daily Produc tion Scheduling (DPS) and Dynamic Operation Scheduling (DOS). The detailed desig n specifications of each of the features and functionality were confirmed and ac cepted by the end users before the commencement of the development effort. The c ompleted and tested modules were delivered in stages for testing and acceptance by the end user during the Phase Three of the project. Pilot product line was se lected for live testing of the developed planning and scheduling modules, before they are proliferated to the rest of the product lines. System fine-tuning req uests were raised during the last phase of the project; the Planning & Schedulin g modules were fine-tuned to satisfy the end user requirements. This paper will conclude by highlighting the actual benefits achieved by the suc cessful deployment of the GSS system. The company has expressed their deep s atisfaction and has requested Gintic to look into the automation of the Plan ning and Scheduling functions in the Pre-Assembly and Test operations.展开更多
This paper presents an integration methodology for ma chining and measuring processes using OMM (On-Machine Measurement) technology b ased on CAD/CAM/CAI integration concept. OMM uses a CNC machining center as a me as...This paper presents an integration methodology for ma chining and measuring processes using OMM (On-Machine Measurement) technology b ased on CAD/CAM/CAI integration concept. OMM uses a CNC machining center as a me asuring station by changing the tools into measuring probes such as touch-type, laser and vision. Although the measurement accuracy is not good compared to tha t of the CMM (Coordinate Measuring Machine), there are distinctive advantages us ing OMM in real situation. In this paper, two topics are handled to show the eff ectiveness of the machining and measuring process integration: (1) inspection pl anning strategy for sculptured surface machining and (2) tool path compensation for profile milling process. For the first topic, as a first step, effective mea suring point locations are determined to obtain optimum results for given sampli ng numbers. Two measuring point selection methods are suggested based on the CAD /CAM/CAI integration concept: (1) by the prediction of cutting errors and (2) by considering cutter contact points to avoid the measurement errors caused by cus ps. As a next step, the TSP (Traveling Salesman Problem) algorithm is applied to minimize the probe moving distance. Appropriate simulations and experiments are performed to verify the proposed inspection planning strategy, and the results are analyzed. For the second topic, a methodology for profile milling error comp ensation is presented by using an ANN (Artificial Neural Network) model trained by the inspection database of OMM system. First, geometric and thermal errors of the machining center are compensated using a closed-loop configuration for the improvement of machining and inspection accuracy. The probing errors are also t aken into account. Then, a specimen workpiece is machined and then the machi ning surface error distribution is measured on the machine using touch-type pro be. In order to efficiently analyze the machining errors, two characteristic err or parameters (W err and D err) are defined. Subsequently, these param eters are modeled by applying the RFB (Radial Basis Function) network approach a s an ANN model. Based on the RBF network model, the tool paths are compensated i n order to effectively reduce the errors by employing an iterative algorithm. In order to validate the approaches proposed in this paper, a concrete case of the machining process is taken into account and about 90% of machining error reduction is successfully accomplished through the proposed approaches.展开更多
针对可再生能源出力和多能负荷的不确定性,提出一种综合能源系统多目标鲁棒优化规划方法。结合综合能源系统结构,构建其规划优化模型,在碳中和背景下将全寿期年化总成本最小和碳排放量最小作为两个规划目标。通过对源荷两侧不确定性因...针对可再生能源出力和多能负荷的不确定性,提出一种综合能源系统多目标鲁棒优化规划方法。结合综合能源系统结构,构建其规划优化模型,在碳中和背景下将全寿期年化总成本最小和碳排放量最小作为两个规划目标。通过对源荷两侧不确定性因素的刻画和对偶转换,建立综合能源系统多目标鲁棒优化规划模型,并融合启发式搜索和约束法的优点,提出基于拥挤度的约束生成算法,求解IES(Integrated Energy System)最优规划方案。算例结果证明了该规划方法的正确性和有效性。展开更多
针对动态不确定战场环境下多无人机对多区域、多目标的协同察打任务规划过程中存在的信息不确定、任务多约束及航迹强耦合的多目标优化与决策问题,结合Dubins航迹规划算法,提出了一种融合多种改进策略的灰狼优化算法(grey wolf optimiza...针对动态不确定战场环境下多无人机对多区域、多目标的协同察打任务规划过程中存在的信息不确定、任务多约束及航迹强耦合的多目标优化与决策问题,结合Dubins航迹规划算法,提出了一种融合多种改进策略的灰狼优化算法(grey wolf optimization algorithm incorporating multiple improvement strategies,IMISGWO).首先,针对动态环境带来的无人机巡航速度及察打任务消失时间的不确定性,基于可信性理论建立了以最大化任务收益为指标的任务规划数学模型;其次,为实现该问题的快速求解,设计了初始解均匀分布、个体通信机制调整、动态权重更新和跳出局部最优等策略,提升算法解搜索能力;最后,构建了多无人机察打一体典型任务仿真场景,通过数字仿真以及虚实结合半实物仿真试验验证了算法的可行性和有效性.仿真结果表明:算法在求解不确定环境下耦合航迹的多无人机察打一体任务规划问题时,能够生成多机高效的任务执行序列和满足无人机飞行性能约束的飞行轨迹,且能够适用于无人机数量增加导致问题复杂度增加情形下此类问题的求解.展开更多
随着可再生能源占比的提升,气电联合配网(integrated electricity-gas distribution systems,IEGDS)发展迅速,其研究的必要性也随之凸显。为解决IEGDS考虑可再生能源接入后的多设施规划及可靠性评估问题,提出了考虑可靠性约束的IEGDS分...随着可再生能源占比的提升,气电联合配网(integrated electricity-gas distribution systems,IEGDS)发展迅速,其研究的必要性也随之凸显。为解决IEGDS考虑可再生能源接入后的多设施规划及可靠性评估问题,提出了考虑可靠性约束的IEGDS分布鲁棒优化(distributionally robust optimization,DRO)规划模型。可靠性评估通过系统平均中断频率指标(system average interruption frequency index,SAIFI)与预期能源未供应指标(expectation energy not supplied,EENS)量化,给出一种显式可靠性评估模型。利用Wasserstein距离衡量的DRO规划模型来处理可再生能源出力、电与气负荷的随机性。最后经过案例分析得出,投建设施种类的增加不仅能降低总成本,还能提高系统运行可靠性与灵活性,可再生能源出力与气电负荷的不确定性导致总成本提升60%以上。随着可靠性要求提升总成本也随之提高,但得到的方案在实际应用中更具价值与参考性。展开更多
文摘Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r Aided Process Planning (CAPP) is a step in this direction. Most of the existin g CAPP systems do not consider scheduling while generating a process plan. Sched uling is done separately after the process plan has been generated and therefore , it is possible that a process plan so generated is either not optimal or feasi ble from scheduling point of view. As process plans are generated without consid eration of job shop status, many problems arise within the manufacturing environ ment. Investigations have shown that 20%～30% of all process plans generated are not valid and have to be altered or suffer production delays when production sta rts. There is thus a major need for integration of scheduling with computer aide d process planning for generating more realistic process plans. In doing so, eff iciency of the manufacturing system as a whole is expected to improve. Decision support system performs many functions such as selection of machine too ls, cutting tools, sequencing of operations, determination of optimum cutting pa rameters and checking availability of machine tool before allocating any operati on to a machine tool. The process of transforming component data, process capabi lity and decision rules into computer readable format is still a major obstacle. This paper proposes architecture of a system, which integrates computer aided p rocess-planning system with scheduling using decision support system. A decisio n support system can be defined as " an interactive system that provides the use rs with easy access to decision models in order to support semi-structured or u nstructured decision making tasks".
文摘In this paper, the design, customization and implem en tation of an integrated Advanced Planning and Scheduling (APS) system for a Semi conductor Backend Assembly environment is described. The company is one of the w orldwide market leaders in semiconductor packaging technology. The project was d riven by the company’s quest to achieve a competitive edge as a manufacturing po werhouse by providing the shortest possible cycle time with a high degree of fle xibility through the application of Computer Integrated Manufacturing (CIM) tech nology. Gintic was responsible for the Planning & Scheduling functions through o ur APS tool kit, which is called Gintic Scheduling System (GSS). Our APS system is to be integrated with the other two key software systems, namely, the Enterpr ise Resource Planning (ERP) and Manufacturing Execution System (MES), with the C IM framework. The project was divided into four major execution phases. Phase One activities w ere focused on the gathering and analysis of the end users requirements in order to establish the ’As-Is’ situation and the wish list & the expectation of the ’To-Be’ system. Planning and Scheduling prototypes were built using GSS to iden tify the functionality gap between the existing GSS system and the ’To-Be’ mode l, in order to determine the customization effort needed. The project team perfo rmed detailed system analysis, design and development of the ’To-Be’ system dur ing Phase Two of the project. There are a total of four planning and scheduling modules, including Capacity Planning (CP), Daily Lot Release (DLR), Daily Produc tion Scheduling (DPS) and Dynamic Operation Scheduling (DOS). The detailed desig n specifications of each of the features and functionality were confirmed and ac cepted by the end users before the commencement of the development effort. The c ompleted and tested modules were delivered in stages for testing and acceptance by the end user during the Phase Three of the project. Pilot product line was se lected for live testing of the developed planning and scheduling modules, before they are proliferated to the rest of the product lines. System fine-tuning req uests were raised during the last phase of the project; the Planning & Schedulin g modules were fine-tuned to satisfy the end user requirements. This paper will conclude by highlighting the actual benefits achieved by the suc cessful deployment of the GSS system. The company has expressed their deep s atisfaction and has requested Gintic to look into the automation of the Plan ning and Scheduling functions in the Pre-Assembly and Test operations.
文摘This paper presents an integration methodology for ma chining and measuring processes using OMM (On-Machine Measurement) technology b ased on CAD/CAM/CAI integration concept. OMM uses a CNC machining center as a me asuring station by changing the tools into measuring probes such as touch-type, laser and vision. Although the measurement accuracy is not good compared to tha t of the CMM (Coordinate Measuring Machine), there are distinctive advantages us ing OMM in real situation. In this paper, two topics are handled to show the eff ectiveness of the machining and measuring process integration: (1) inspection pl anning strategy for sculptured surface machining and (2) tool path compensation for profile milling process. For the first topic, as a first step, effective mea suring point locations are determined to obtain optimum results for given sampli ng numbers. Two measuring point selection methods are suggested based on the CAD /CAM/CAI integration concept: (1) by the prediction of cutting errors and (2) by considering cutter contact points to avoid the measurement errors caused by cus ps. As a next step, the TSP (Traveling Salesman Problem) algorithm is applied to minimize the probe moving distance. Appropriate simulations and experiments are performed to verify the proposed inspection planning strategy, and the results are analyzed. For the second topic, a methodology for profile milling error comp ensation is presented by using an ANN (Artificial Neural Network) model trained by the inspection database of OMM system. First, geometric and thermal errors of the machining center are compensated using a closed-loop configuration for the improvement of machining and inspection accuracy. The probing errors are also t aken into account. Then, a specimen workpiece is machined and then the machi ning surface error distribution is measured on the machine using touch-type pro be. In order to efficiently analyze the machining errors, two characteristic err or parameters (W err and D err) are defined. Subsequently, these param eters are modeled by applying the RFB (Radial Basis Function) network approach a s an ANN model. Based on the RBF network model, the tool paths are compensated i n order to effectively reduce the errors by employing an iterative algorithm. In order to validate the approaches proposed in this paper, a concrete case of the machining process is taken into account and about 90% of machining error reduction is successfully accomplished through the proposed approaches.
文摘针对可再生能源出力和多能负荷的不确定性,提出一种综合能源系统多目标鲁棒优化规划方法。结合综合能源系统结构,构建其规划优化模型,在碳中和背景下将全寿期年化总成本最小和碳排放量最小作为两个规划目标。通过对源荷两侧不确定性因素的刻画和对偶转换,建立综合能源系统多目标鲁棒优化规划模型,并融合启发式搜索和约束法的优点,提出基于拥挤度的约束生成算法,求解IES(Integrated Energy System)最优规划方案。算例结果证明了该规划方法的正确性和有效性。
文摘随着可再生能源占比的提升,气电联合配网(integrated electricity-gas distribution systems,IEGDS)发展迅速,其研究的必要性也随之凸显。为解决IEGDS考虑可再生能源接入后的多设施规划及可靠性评估问题,提出了考虑可靠性约束的IEGDS分布鲁棒优化(distributionally robust optimization,DRO)规划模型。可靠性评估通过系统平均中断频率指标(system average interruption frequency index,SAIFI)与预期能源未供应指标(expectation energy not supplied,EENS)量化,给出一种显式可靠性评估模型。利用Wasserstein距离衡量的DRO规划模型来处理可再生能源出力、电与气负荷的随机性。最后经过案例分析得出,投建设施种类的增加不仅能降低总成本,还能提高系统运行可靠性与灵活性,可再生能源出力与气电负荷的不确定性导致总成本提升60%以上。随着可靠性要求提升总成本也随之提高,但得到的方案在实际应用中更具价值与参考性。
