In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-in...In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.展开更多
An improved differential evolution(IDE)algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem(RCPSP)with the obj...An improved differential evolution(IDE)algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem(RCPSP)with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated.To investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm(HGA),particle swarm optimization(PSO) and several well selected heuristics.The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.展开更多
In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select...In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.展开更多
A memetic algorithm (MA) for a multi-mode resourceconstrained project scheduling problem (MRCPSP) is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The f...A memetic algorithm (MA) for a multi-mode resourceconstrained project scheduling problem (MRCPSP) is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called "strategic oscillation" to make the search process have a higher probability to visit solutions around a "feasible boundary". One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.展开更多
Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition t...Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of goals.Project scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of goals.Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation process.The concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations.Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us.We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment features.In this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained projects.Five projects are considered test problems for the PHR.The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems.The results in many cases of the proposed priority rules are very promising,where they achieve better scheduling dates in many test case problems and the same results for the others.The proposed model is based on the dynamic features for project topography.展开更多
This paper presents a new genetic algorithm for the resource-constrained project scheduling problem(RCPSP).The algorithm employs a standardized random key(SRK) vector representation with an additional gene that determ...This paper presents a new genetic algorithm for the resource-constrained project scheduling problem(RCPSP).The algorithm employs a standardized random key(SRK) vector representation with an additional gene that determines whether the serial or parallel schedule generation scheme(SGS) is to be used as the decoding procedure.The iterative forward-backward improvement as the local search procedure is applied upon all generated solutions to schedule the project three times and obtain an SRK vector,which is reserved into population.Several evolutionary strategies are implemented including the elitist selection(the high quality solution set),and the selection of parents used in crossover operator.The computational experiments on 1 560 standard instances show that the proposed algorithm outperforms the current state-of-the-art heuristic algorithms for J30 and J60,and ranks the third for J120 with 50 000 schedules;it ranks the second for J30 and J60,and ranks the fifth for J120 with 5 000 schedules;it ranks the third,second,and fifth for J30,J60 and J120 with 1 000 schedules,respectively.It is demonstrated that the proposed algorithm is competitive for RCPSP,especially for larger number of schedules.展开更多
This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable dec...This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable decoding scheme. Then a multi-pass biased sampling method followed up by a multi-local search is used to generate a diverse and good quality initial population. The population then evolves through modified order-based recombination and mutation operators to perform exploration for promising solutions within the entire region. Mutation is performed only if the current population has converged or the produced offspring by recombination operator is too similar to one of his parents. Finally the algorithm performs an intensified local search on the best solution found in the evolutionary stage. Computational experiments using standard instances indicate that the proposed algorithm works well in both computational time and solution quality.展开更多
The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocatio...The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.展开更多
This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations amo...This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem (RACP) by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion (PPSO) combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.展开更多
To determine the reasonable resource dependent relations between activities for the purpose of exactly computing the total floats and the free floats of activities, correctly identifying critical activities and critic...To determine the reasonable resource dependent relations between activities for the purpose of exactly computing the total floats and the free floats of activities, correctly identifying critical activities and critical sequences in a project schedule with variable resource constraints, the concept of the minimal feasible set (MFS) is proposed and the properties of MFS are discussed. The methods to identify optimal MFSs and resource links are then studied. Furthermore, MFS is generalized to the situation that the preconditions of MFS are not satisfied. Contrastive results show that in establishing resource links and resolving floats, MFS is at least not inferior to other methods in all cases and is superior in most situations.展开更多
The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a ki...The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a kind of software development and management strategy, is defined as a series of activities implemented by software life cycle and provides a set of rules for various phases of the software engineering to achieve the desired objectives. With the current software development cycle getting shorter, facing more frequent needs change and fierce competition, a new resource management pattern is proposed to respond to these issues agilely by introducing the crowdsourcing service to agile software development for pushing the agility of software process. Then, a user-oriented resource scheduling method is proposed for rational use of various resources in the process and maximizing the benefits of all parties. From the experimental results, the proposed pattern and resources scheduling method reduces greatly the resource of project resource manager and increases the team resource utilization rate, which greatly improves the agility of software process and delivers software products quickly in crowdsourcing pattern.展开更多
To solve the resource-constrained multiple project scheduling problem(RCMPSP) more effectively,a method based on timed colored Petri net(TCPN) was proposed.In this methodology,firstly a novel mapping mechanism between...To solve the resource-constrained multiple project scheduling problem(RCMPSP) more effectively,a method based on timed colored Petri net(TCPN) was proposed.In this methodology,firstly a novel mapping mechanism between traditional network diagram such as CPM(critical path method)/PERT(program evaluation and review technique) and TCPN was presented.Then a primary TCPN(PTCPN) for solving RCMPSP was modeled based on the proposed mapping mechanism.Meanwhile,the object PTCPN was used to simulate the multiple projects scheduling and to find the approximately optimal value of RCMPSP.Finally,the performance of the proposed approach for solving RCMPSP was validated by executing a mould manufacturing example.展开更多
In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm forsolving multiple resource-constrained project scheduling problems. The heuristic solves problems intwo phases. In the pre-p...In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm forsolving multiple resource-constrained project scheduling problems. The heuristic solves problems intwo phases. In the pre-processing phase, the algorithm estimates distance between a state and the goalstate and measures complexity of problem instances. In the search phase, the algorithm uses estimatesof the pre-processing phase to further estimate distances to the goal state. The search continues in astepwise generation of a series of intermediate states through search path evaluation process withbacktracking. Developments of intermediate states are exclusively based on a bi-criteria new stateselection technique where we consider resource utilization and duration estimate to the goal state. Wealso propose a variable weighting technique based on initial problem complexity measures.Introducing this technique allows the algorithm to efficiently solve complex project schedulingproblems. A numerical example illustra展开更多
It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analy...It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.展开更多
基金supported by the Sichuan Science and Technology Program(grant number 2022YFG0123).
文摘In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.
基金supported by the National Natural Science Foundation of China(6083500460775047+4 种基金60974048)the National High Technology Research and Development Program of China(863 Program)(2007AA0422442008AA04Z214)the Natural Science Foundation of Hunan Province(09JJ9012)Scientific Research Fund of Hunan Provincial Education Department(08C337)
文摘An improved differential evolution(IDE)algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem(RCPSP)with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated.To investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm(HGA),particle swarm optimization(PSO) and several well selected heuristics.The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.
基金The Spring Plan of Ministry of Education,China(No.Z2012017)
文摘In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.
基金supported by the National Natural Science Foundation of China(71171038)
文摘A memetic algorithm (MA) for a multi-mode resourceconstrained project scheduling problem (MRCPSP) is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called "strategic oscillation" to make the search process have a higher probability to visit solutions around a "feasible boundary". One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.
文摘Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of goals.Project scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of goals.Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation process.The concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations.Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us.We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment features.In this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained projects.Five projects are considered test problems for the PHR.The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems.The results in many cases of the proposed priority rules are very promising,where they achieve better scheduling dates in many test case problems and the same results for the others.The proposed model is based on the dynamic features for project topography.
文摘This paper presents a new genetic algorithm for the resource-constrained project scheduling problem(RCPSP).The algorithm employs a standardized random key(SRK) vector representation with an additional gene that determines whether the serial or parallel schedule generation scheme(SGS) is to be used as the decoding procedure.The iterative forward-backward improvement as the local search procedure is applied upon all generated solutions to schedule the project three times and obtain an SRK vector,which is reserved into population.Several evolutionary strategies are implemented including the elitist selection(the high quality solution set),and the selection of parents used in crossover operator.The computational experiments on 1 560 standard instances show that the proposed algorithm outperforms the current state-of-the-art heuristic algorithms for J30 and J60,and ranks the third for J120 with 50 000 schedules;it ranks the second for J30 and J60,and ranks the fifth for J120 with 5 000 schedules;it ranks the third,second,and fifth for J30,J60 and J120 with 1 000 schedules,respectively.It is demonstrated that the proposed algorithm is competitive for RCPSP,especially for larger number of schedules.
文摘This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable decoding scheme. Then a multi-pass biased sampling method followed up by a multi-local search is used to generate a diverse and good quality initial population. The population then evolves through modified order-based recombination and mutation operators to perform exploration for promising solutions within the entire region. Mutation is performed only if the current population has converged or the produced offspring by recombination operator is too similar to one of his parents. Finally the algorithm performs an intensified local search on the best solution found in the evolutionary stage. Computational experiments using standard instances indicate that the proposed algorithm works well in both computational time and solution quality.
文摘The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.
基金supported by the National Natural Science Foundation of China(7120116671201170)
文摘This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem (RACP) by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion (PPSO) combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.
基金supported partly by the Postdoctoral Science Foundation of China(2007042-0922)the Program of Educational Commission of Guangxi Zhuang Minority Autonomous Region(200712LX128)the Scientific Research Foundation of Guangxi University for Nationalities for Talent Introduction(200702YZ01).
文摘To determine the reasonable resource dependent relations between activities for the purpose of exactly computing the total floats and the free floats of activities, correctly identifying critical activities and critical sequences in a project schedule with variable resource constraints, the concept of the minimal feasible set (MFS) is proposed and the properties of MFS are discussed. The methods to identify optimal MFSs and resource links are then studied. Furthermore, MFS is generalized to the situation that the preconditions of MFS are not satisfied. Contrastive results show that in establishing resource links and resolving floats, MFS is at least not inferior to other methods in all cases and is superior in most situations.
基金Projects(61304184,61672221)supported by the National Natural Science Foundation of ChinaProject(2016JJ6010)supported by the Hunan Provincial Natural Science Foundation of China
文摘The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a kind of software development and management strategy, is defined as a series of activities implemented by software life cycle and provides a set of rules for various phases of the software engineering to achieve the desired objectives. With the current software development cycle getting shorter, facing more frequent needs change and fierce competition, a new resource management pattern is proposed to respond to these issues agilely by introducing the crowdsourcing service to agile software development for pushing the agility of software process. Then, a user-oriented resource scheduling method is proposed for rational use of various resources in the process and maximizing the benefits of all parties. From the experimental results, the proposed pattern and resources scheduling method reduces greatly the resource of project resource manager and increases the team resource utilization rate, which greatly improves the agility of software process and delivers software products quickly in crowdsourcing pattern.
文摘To solve the resource-constrained multiple project scheduling problem(RCMPSP) more effectively,a method based on timed colored Petri net(TCPN) was proposed.In this methodology,firstly a novel mapping mechanism between traditional network diagram such as CPM(critical path method)/PERT(program evaluation and review technique) and TCPN was presented.Then a primary TCPN(PTCPN) for solving RCMPSP was modeled based on the proposed mapping mechanism.Meanwhile,the object PTCPN was used to simulate the multiple projects scheduling and to find the approximately optimal value of RCMPSP.Finally,the performance of the proposed approach for solving RCMPSP was validated by executing a mould manufacturing example.
文摘In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm forsolving multiple resource-constrained project scheduling problems. The heuristic solves problems intwo phases. In the pre-processing phase, the algorithm estimates distance between a state and the goalstate and measures complexity of problem instances. In the search phase, the algorithm uses estimatesof the pre-processing phase to further estimate distances to the goal state. The search continues in astepwise generation of a series of intermediate states through search path evaluation process withbacktracking. Developments of intermediate states are exclusively based on a bi-criteria new stateselection technique where we consider resource utilization and duration estimate to the goal state. Wealso propose a variable weighting technique based on initial problem complexity measures.Introducing this technique allows the algorithm to efficiently solve complex project schedulingproblems. A numerical example illustra
基金supported by the National Natural Science Foundation of China(61671462).
文摘It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.