This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling (based on the work of Terrazas-Moreno & Grossmann (2011) [1]) under products prices uncertainty. Wit...This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling (based on the work of Terrazas-Moreno & Grossmann (2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral set. The genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.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
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
Distributed Integrated Modular Avionics(DIMA)develops from Integrated Modular Avionics(IMA)and realizes distributed integration of multiple sub-function areas.Timetriggered network provides effective support for time ...Distributed Integrated Modular Avionics(DIMA)develops from Integrated Modular Avionics(IMA)and realizes distributed integration of multiple sub-function areas.Timetriggered network provides effective support for time synchronization and information coordination in DIMA systems.However,inconsistency between processing resources and communication network destroys the time determinism benefiting from partitions and time-triggered mechanism.To ensure such time determinism and achieve guaranteed real-time performance,system design should collectively provide a global communication scheme for messages in network domain and a corresponding execution scheme for partitions in processing domain.This paper firstly establishes a general DIMA model which coordinates partitioned processing and time-triggered communication,and then proposes a hybrid scheduling algorithm using Mixed Integer Programming to produce feasible system schemes.Furthermore,incrementally integrating new functions causes upgrades or reconfigurations of DIMA systems and will generate integration cost.To control such cost,this paper further develops an optimization algorithm based on Maximum Satisfiability Problem and guarantees that the scheduling design for upgraded DIMA systems inherit their original schemes as much as possible.Finally,two typical cases,including a simple fully connected DIMA system case and an industrial DIMA system case,are constructed to illustrate our DIMA model and validate the effectiveness of our hybrid scheduling algorithms.展开更多
Quantum search has emerged as one of the most promising fields in quantum computing.Stateof-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the d...Quantum search has emerged as one of the most promising fields in quantum computing.Stateof-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution.These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces.Unfortunately,the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure,rendering this kind of approach not exploitable in real industrial problems.In contrast,this work proposes exploiting specific constraints of an outage planning problem,consisting in setting outage dates of production units under specific fuel management constraints and resource constraints limiting the number of outages in parallel,to build an initial superposition of states with size almost quadratically increasing as a function of the problem size.This state space reduction,inspired by the quantum walk algorithm,constructs a state superposition corresponding to all paths in a state-graph,embedding spacing constraints between outages.Our numerical results on quantum emulators highlight the potential of the statespace reduction approach.In our simplified use case,the number of iterations required to reach a 90% probability of measuring a feasible solution is reduced by a factor between 2 and 4.More importantly,the squared ratio between the number of possible configurations and the number of valid solutions shifts from exponential to linear behavior,demonstrating that the quadratic speedup offered by Grover-based algorithms becomes sufficient in this setting.While these results are based on a simplified scenario and further investigation is needed to generalize them to large-scale industrial problems,they illustrate the promise of structure-aware initialization in significantly improving the efficiency of quantum search by focusing on a smaller,more relevant solution space.展开更多
For increasing the overall performance of modem manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the com...For increasing the overall performance of modem manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling （IPPS） problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS （MOIPPS） problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatch- ing rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.展开更多
Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the...Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.展开更多
The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy ...The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy resources management from a centralized planning perspective. Thus, a combined modeling of the reservoirs involved in electric power and gas systems and their locations on both networks are essential features to be considered in the operational planning of energy resources.This paper presents a modeling and optimization approach to the operational planning of electric power and natural gas systems, taking into account different energy storage facilities, such as water reservoirs, natural gas storages and line packs of pipelines. The proposed model takes advantage of captures both energy systems synergy and their associated networks. This approach identifies the interactions between the energy storage facilities and their economic impact over their optimal scheduling. The results show the benefits of an integrated operational planning of electric power and natural gas systems, the close interdependency between the energy resources stored in both systems, and the effects of a combined scheduling.展开更多
AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexami...AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexamined.In particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline constraints.To cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two observations.First,resource planning for AI workloadsis a complicated search problem that requires much time for optimization.Second,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in advance.Based on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of resources.Instead of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of tasks.Thus,in any case,the workload isimmediately executed according to the resource plan maintained.Specifically,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload changes.The proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its effectiveness.Simulationexperiments show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.展开更多
Operating on volatile profit margins,it is imperative for the airline industry to utilize its capacity and resources via sophisticated schedules for efficient operation.While the airline scheduling problem consisting ...Operating on volatile profit margins,it is imperative for the airline industry to utilize its capacity and resources via sophisticated schedules for efficient operation.While the airline scheduling problem consisting of schedule design,fleet assignment,aircraft routing,and crew scheduling subproblems were extensively studied and successfully applied in real practice,the recent decade has witnessed a growing interest in integrated scheduling problems that capture the interdependencies between various decisions across different subproblems and airline resources with significantly reduced operation costs.This paper aims to provide insights for modelling the airline integrated scheduling problem,indicating a roadmap for future research in this domain.Multiple perspectives concerning demand-supply interaction,operational consistency,and schedule robustness are first provided.State-of-the-art mathematical formulations and coupling relationships for representative integrated problems are summarized additionally.In the end,we conclude with a series of themes that can be further addressed in the future.展开更多
针对多目标工艺规划与车间调度集成问题(multi-objective integrated process planning and scheduling,MOIPPS),以最小化完工时间和生产能耗最低为优化目标,提出了一种考虑全局和局部最优的改进混合优化算法。通过分析集成系统工艺设...针对多目标工艺规划与车间调度集成问题(multi-objective integrated process planning and scheduling,MOIPPS),以最小化完工时间和生产能耗最低为优化目标,提出了一种考虑全局和局部最优的改进混合优化算法。通过分析集成系统工艺设计和生产调度两个问题的区别与联系,搭建了多目标问题模型和解决框架。针对两阶段集成问题提出混合优化算法,对工艺阶段采用全局搜索算法,为集成系统提供多种工艺加工方案,保证集成算法的全局搜索性能;针对调度阶段设计一种改进禁忌搜索算法,通过交叉与随机抽样扩大解的分布范围,使用邻域禁忌搜索使得算法快速收敛,并采用Pareto非支配排序获得全局最优解。实验对比分析,验证了所提算法在求解多目标工艺规划与车间调度集成问题的高效性和稳定性。展开更多
儿童注意缺陷多动障碍(attention deficit and hyperactivity disorder,ADHD)往往伴随情绪问题或情绪障碍,这增加了诊疗的复杂性。西医(即现代医学)依据《精神障碍诊断与统计手册(第5版)》(DSM-5)或《国际疾病分类(第11版)》(ICD-11)的...儿童注意缺陷多动障碍(attention deficit and hyperactivity disorder,ADHD)往往伴随情绪问题或情绪障碍,这增加了诊疗的复杂性。西医(即现代医学)依据《精神障碍诊断与统计手册(第5版)》(DSM-5)或《国际疾病分类(第11版)》(ICD-11)的标准,对ADHD及其伴随的情绪问题(如焦虑、抑郁)进行诊断,并采用心理治疗或药物治疗;中医(即中国传统医学)则根据辨证分型(如心肝火旺证、痰火内扰证等),运用中药(如丹栀逍遥散、黄连温胆汤)及外治法(针灸、推拿)开展治疗。该文基于中西医协同理念,提出了ADHD伴情绪问题的诊治方案。该方案强调多学科评估,动态调整治疗方案,结合患儿年龄、病情及家庭需求,实现个体化干预。同时,方案涵盖轻度至重度情绪问题的分级治疗策略,并注重疗效评价、安全性监测及家庭社会支持,以此提升ADHD的整体疗效,改善患儿的生活质量。展开更多
Large-scale centralized exploitation of intermittent wind energy resources has become popular in many countries.However,as a result of the frequent occurrence of largescale wind curtailment,expansion of corresponding ...Large-scale centralized exploitation of intermittent wind energy resources has become popular in many countries.However,as a result of the frequent occurrence of largescale wind curtailment,expansion of corresponding transmission projects has fallen behind the speed at which installed wind capacity can be developed.In this paper,a coordinated planning approach for a large-scale wind farm integration system and its related regional transmission network is proposed.A bilevel programming model is formulated with the objective of minimizing cost.To reach the global optimum of the bi-level model,this work proposes that the upper-level wind farm integration system planning problem needs to be solved jointly with the lower-level regional transmission planning problem.The bi-level model is expressed in terms of a linearized mathematical problem with equilibrium constraints(MPEC)by Karush-KuhnTucker conditions.It is then solved using mixed integer linear programming solvers.Numerical simulations are conducted to show the validity of the proposed coordinated planning method.展开更多
The airline industry is a representative industry with high cost and low profitability.Therefore,airlines should carefully plan their schedules to ensure that overall profit is maximized.We review the literature on ai...The airline industry is a representative industry with high cost and low profitability.Therefore,airlines should carefully plan their schedules to ensure that overall profit is maximized.We review the literature on airline planning and scheduling and focus on mathematical formulations and solution methodologies.Our research framework is anchored on three major problems in the airline scheduling,namely,fleet assignment,aircraft routing,and crew scheduling.General formulation,widely used solution approaches,and important extensions are presented for each problem and integrated problems.We conclude the review by identifying promising areas for further research.展开更多
文摘This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling (based on the work of Terrazas-Moreno & Grossmann (2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral set. The genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.
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".
基金co-supported by the National Natural Science Foundation of China(No.71701020)the Defense Research Field Foundation of China(No.61403120404)the Civil Aircraft Airworthiness and Maintenance Key Laboratory Fund of Civil Aviation University of China(No.2017SW02).
文摘Distributed Integrated Modular Avionics(DIMA)develops from Integrated Modular Avionics(IMA)and realizes distributed integration of multiple sub-function areas.Timetriggered network provides effective support for time synchronization and information coordination in DIMA systems.However,inconsistency between processing resources and communication network destroys the time determinism benefiting from partitions and time-triggered mechanism.To ensure such time determinism and achieve guaranteed real-time performance,system design should collectively provide a global communication scheme for messages in network domain and a corresponding execution scheme for partitions in processing domain.This paper firstly establishes a general DIMA model which coordinates partitioned processing and time-triggered communication,and then proposes a hybrid scheduling algorithm using Mixed Integer Programming to produce feasible system schemes.Furthermore,incrementally integrating new functions causes upgrades or reconfigurations of DIMA systems and will generate integration cost.To control such cost,this paper further develops an optimization algorithm based on Maximum Satisfiability Problem and guarantees that the scheduling design for upgraded DIMA systems inherit their original schemes as much as possible.Finally,two typical cases,including a simple fully connected DIMA system case and an industrial DIMA system case,are constructed to illustrate our DIMA model and validate the effectiveness of our hybrid scheduling algorithms.
文摘Quantum search has emerged as one of the most promising fields in quantum computing.Stateof-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution.These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces.Unfortunately,the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure,rendering this kind of approach not exploitable in real industrial problems.In contrast,this work proposes exploiting specific constraints of an outage planning problem,consisting in setting outage dates of production units under specific fuel management constraints and resource constraints limiting the number of outages in parallel,to build an initial superposition of states with size almost quadratically increasing as a function of the problem size.This state space reduction,inspired by the quantum walk algorithm,constructs a state superposition corresponding to all paths in a state-graph,embedding spacing constraints between outages.Our numerical results on quantum emulators highlight the potential of the statespace reduction approach.In our simplified use case,the number of iterations required to reach a 90% probability of measuring a feasible solution is reduced by a factor between 2 and 4.More importantly,the squared ratio between the number of possible configurations and the number of valid solutions shifts from exponential to linear behavior,demonstrating that the quadratic speedup offered by Grover-based algorithms becomes sufficient in this setting.While these results are based on a simplified scenario and further investigation is needed to generalize them to large-scale industrial problems,they illustrate the promise of structure-aware initialization in significantly improving the efficiency of quantum search by focusing on a smaller,more relevant solution space.
文摘For increasing the overall performance of modem manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling （IPPS） problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS （MOIPPS） problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatch- ing rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.
文摘Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.
基金supported by the Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET)the Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT)
文摘The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy resources management from a centralized planning perspective. Thus, a combined modeling of the reservoirs involved in electric power and gas systems and their locations on both networks are essential features to be considered in the operational planning of energy resources.This paper presents a modeling and optimization approach to the operational planning of electric power and natural gas systems, taking into account different energy storage facilities, such as water reservoirs, natural gas storages and line packs of pipelines. The proposed model takes advantage of captures both energy systems synergy and their associated networks. This approach identifies the interactions between the energy storage facilities and their economic impact over their optimal scheduling. The results show the benefits of an integrated operational planning of electric power and natural gas systems, the close interdependency between the energy resources stored in both systems, and the effects of a combined scheduling.
基金This work was partly supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by theKorean government(MSIT)(No.2021-0-02068,Artificial Intelligence Innovation Hub)(No.RS-2022-00155966,Artificial Intelligence Convergence Innovation Human Resources Development(Ewha University)).
文摘AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexamined.In particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline constraints.To cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two observations.First,resource planning for AI workloadsis a complicated search problem that requires much time for optimization.Second,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in advance.Based on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of resources.Instead of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of tasks.Thus,in any case,the workload isimmediately executed according to the resource plan maintained.Specifically,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload changes.The proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its effectiveness.Simulationexperiments show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.
基金supported by the fellowship from the China Postdoc-toral Science Foundation(Certificate Number:2023M741686).
文摘Operating on volatile profit margins,it is imperative for the airline industry to utilize its capacity and resources via sophisticated schedules for efficient operation.While the airline scheduling problem consisting of schedule design,fleet assignment,aircraft routing,and crew scheduling subproblems were extensively studied and successfully applied in real practice,the recent decade has witnessed a growing interest in integrated scheduling problems that capture the interdependencies between various decisions across different subproblems and airline resources with significantly reduced operation costs.This paper aims to provide insights for modelling the airline integrated scheduling problem,indicating a roadmap for future research in this domain.Multiple perspectives concerning demand-supply interaction,operational consistency,and schedule robustness are first provided.State-of-the-art mathematical formulations and coupling relationships for representative integrated problems are summarized additionally.In the end,we conclude with a series of themes that can be further addressed in the future.
文摘针对多目标工艺规划与车间调度集成问题(multi-objective integrated process planning and scheduling,MOIPPS),以最小化完工时间和生产能耗最低为优化目标,提出了一种考虑全局和局部最优的改进混合优化算法。通过分析集成系统工艺设计和生产调度两个问题的区别与联系,搭建了多目标问题模型和解决框架。针对两阶段集成问题提出混合优化算法,对工艺阶段采用全局搜索算法,为集成系统提供多种工艺加工方案,保证集成算法的全局搜索性能;针对调度阶段设计一种改进禁忌搜索算法,通过交叉与随机抽样扩大解的分布范围,使用邻域禁忌搜索使得算法快速收敛,并采用Pareto非支配排序获得全局最优解。实验对比分析,验证了所提算法在求解多目标工艺规划与车间调度集成问题的高效性和稳定性。
文摘儿童注意缺陷多动障碍(attention deficit and hyperactivity disorder,ADHD)往往伴随情绪问题或情绪障碍,这增加了诊疗的复杂性。西医(即现代医学)依据《精神障碍诊断与统计手册(第5版)》(DSM-5)或《国际疾病分类(第11版)》(ICD-11)的标准,对ADHD及其伴随的情绪问题(如焦虑、抑郁)进行诊断,并采用心理治疗或药物治疗;中医(即中国传统医学)则根据辨证分型(如心肝火旺证、痰火内扰证等),运用中药(如丹栀逍遥散、黄连温胆汤)及外治法(针灸、推拿)开展治疗。该文基于中西医协同理念,提出了ADHD伴情绪问题的诊治方案。该方案强调多学科评估,动态调整治疗方案,结合患儿年龄、病情及家庭需求,实现个体化干预。同时,方案涵盖轻度至重度情绪问题的分级治疗策略,并注重疗效评价、安全性监测及家庭社会支持,以此提升ADHD的整体疗效,改善患儿的生活质量。
基金supported in part by the National High Technology Research and Development Program of China(No.2012AA050208)National Natural Science Foundation of China(No.51177043)111 Project(No.B08013).
文摘Large-scale centralized exploitation of intermittent wind energy resources has become popular in many countries.However,as a result of the frequent occurrence of largescale wind curtailment,expansion of corresponding transmission projects has fallen behind the speed at which installed wind capacity can be developed.In this paper,a coordinated planning approach for a large-scale wind farm integration system and its related regional transmission network is proposed.A bilevel programming model is formulated with the objective of minimizing cost.To reach the global optimum of the bi-level model,this work proposes that the upper-level wind farm integration system planning problem needs to be solved jointly with the lower-level regional transmission planning problem.The bi-level model is expressed in terms of a linearized mathematical problem with equilibrium constraints(MPEC)by Karush-KuhnTucker conditions.It is then solved using mixed integer linear programming solvers.Numerical simulations are conducted to show the validity of the proposed coordinated planning method.
基金the National Natural Science Foundation of China under Grant No.71825001.
文摘The airline industry is a representative industry with high cost and low profitability.Therefore,airlines should carefully plan their schedules to ensure that overall profit is maximized.We review the literature on airline planning and scheduling and focus on mathematical formulations and solution methodologies.Our research framework is anchored on three major problems in the airline scheduling,namely,fleet assignment,aircraft routing,and crew scheduling.General formulation,widely used solution approaches,and important extensions are presented for each problem and integrated problems.We conclude the review by identifying promising areas for further research.
