Offshore engineering construction projects are large and complex,having the characteristics of multiple execution modes andmultiple resource constraints.Their complex internal scheduling processes can be regarded as r...Offshore engineering construction projects are large and complex,having the characteristics of multiple execution modes andmultiple resource constraints.Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems(RCPSPs).To solve RCPSP problems in offshore engineering construction more rapidly,a hybrid genetic algorithmwas established.To solve the defects of genetic algorithms,which easily fall into the local optimal solution,a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation.Then,an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions,reduce the computation time and avoid premature convergence.A calibrated function method was used to cater to the roulette rules,and appropriate rules for encoding,decoding and crossover/mutation were designed.Finally,a simple network was designed and validated using the case study of a real offshore project.The performance of the genetic algorithmand a simulated annealing algorithmwas compared to validate the feasibility and effectiveness of the approach.展开更多
Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately u...Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.展开更多
Remote and Hybrid work has been a common practice for many organizations in recent years. It has many advantages such as offering a better work-life balance but it might also negatively affect productivity and teamwor...Remote and Hybrid work has been a common practice for many organizations in recent years. It has many advantages such as offering a better work-life balance but it might also negatively affect productivity and teamwork. While an organization would like to satisfy the remote/hybrid preferences of its employees, it also must ensure that there are enough people working in the office to satisfy certain professional needs. Finding the right balance between in-office and remote work is not an easy task. We develop three optimization models to give solutions to the problem. The most comprehensive model allows employees to work remotely some days of the week and flexible hours for those weekdays when employees work in the office. Our computational results show that the models are very time-efficient in practice. The computational results also include a sensitivity analysis of the most comprehensive model.展开更多
Aiming at the hybrid flow-shop (HFS) scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution (DE) is proposed. By using a...Aiming at the hybrid flow-shop (HFS) scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution (DE) is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.展开更多
Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity...Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm (POA) in combination with the genetic algorithm (GA). After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.展开更多
The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies ha...The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem(IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem(HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles(AGVs)is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation(including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
In view of the problem that the packet information preempted netw ork resources in the process of transmission in the CAN bus,w hich leads to the low utilization of netw ork resources and the low accuracy of informati...In view of the problem that the packet information preempted netw ork resources in the process of transmission in the CAN bus,w hich leads to the low utilization of netw ork resources and the low accuracy of information transmission. Thus,a hybrid scheduling algorithm NM TS based on CAN bus is proposed,in the NM TS hybrid scheduling algorithm,the dynamic scheduling algorithm EDF is used to schedule hard real-time messages to solve the problem of low utilization of netw ork resources; the static scheduling algorithm RM S is used to schedule soft real-time messages and non real-time messages,so as to solve the problem of low accuracy of information transmission. By using M ATLAB softw are,the CAN netw ork model can be built,the EDF algorithm,RM S algorithm and NM TS algorithm are simulated. The experimental results show that the netw ork resources utilization is 90%,the packet loss rate is 0% of the NM TS algorithm. Therefore,The hybrid scheduling algorithm based on CAN bus NM TS has the characteristics of high netw ork resource utilization and high accuracy of information transmission,w hich w ill be very helpful for further research of CAN bus.展开更多
Aiming at the flexible flowshop group scheduling problem,taking sequence dependent setup time and machine skipping into account, a mathematical model for minimizing makespan is established,and a hybrid differential ev...Aiming at the flexible flowshop group scheduling problem,taking sequence dependent setup time and machine skipping into account, a mathematical model for minimizing makespan is established,and a hybrid differential evolution( HDE) algorithm based on greedy constructive procedure( GCP) is proposed,which combines differential evolution( DE) with tabu search( TS). DE is applied to generating the elite individuals of population,while TS is used for finding the optimal value by making perturbation in selected elite individuals. A lower bounding technique is developed to evaluate the quality of proposed algorithm. Experimental results verify the effectiveness and feasibility of proposed algorithm.展开更多
A new local search method with hybrid neighborhood for Job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while sea...A new local search method with hybrid neighborhood for Job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while search procedure get trapped at local optima and carry the search to areas of the feasible set with better prospect. New strategies used for breaking out of entrapments are presented and they are helpful for the procedure to improve local optima. A performance comparison of the proposed method with some best-performing algorithms on all 10-job, 10-machine benchmark problems and the other two problems generated by Fisher and Thompson (ie., FT6 and FT20)is made. The experiment results show the better optimal performance of the proposed algorithm.展开更多
Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal s...Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.展开更多
In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, l...In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.展开更多
A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system (SLHS). An algorithm was proposed to select one (or a group of) sensor at each time from a set of s...A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system (SLHS). An algorithm was proposed to select one (or a group of) sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finally, the algorithms are validated through an illustrative target tracking example.展开更多
In hard real-time systems, schedulability analysis is not only one of the important means of guaranteeing the timelines of embedded software but also one of the fundamental theories of applying other new techniques, s...In hard real-time systems, schedulability analysis is not only one of the important means of guaranteeing the timelines of embedded software but also one of the fundamental theories of applying other new techniques, such as energy savings and fault tolerance. However, most of the existing schedulability analysis methods assume that schedulers use preemptive scheduling or non-preemptive scheduling. In this paper, we present a schedulability analysis method, i.e., the worst-case hybrid scheduling (WCHS) algorithm, which considers the influence of release jitters of transactions and extends schedulability analysis theory to timing analysis of linear transactions under fixed priority hybrid scheduling. To the best of our knowledge, this method is the first one on timing analysis of linear transactions under hybrid scheduling. An example is employed to demonstrate the use of this method. Experiments show that this method has lower computational complexity while keeping correctness, and that hybrid scheduling has little influence on the average worst-case response time (WCRT), but a negative impact on the schedulability of systems.展开更多
Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or n...Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.展开更多
A new concept of multi-shop (M ) is put forward which contains all basic shops including open shop (O), job shop (J ), flow shop (F ) and hybrid flow shop (H ) so that these basic shop can be scheduled toget...A new concept of multi-shop (M ) is put forward which contains all basic shops including open shop (O), job shop (J ), flow shop (F ) and hybrid flow shop (H ) so that these basic shop can be scheduled together. Several algorithms including ant colony optimization (ACO), most work remaining (MWR), least work remaining (LWR), longest processing time (LPT) and shortest processing time (SPT) are used for scheduling the M. Numerical experiments of the M adopting data of some car and reC series benchmark instances are tested. The results show that the ACO algorithm has better performance for scheduling the M than the other algorithms, if minimizing the makespan ( Cmax^*) is taken as the objective function. As a comparison, the separate shops contained in the M are also scheduled by the ACO algorithm for the same objective function, when the completing time of the jobs in the previous shop is taken as the ready time of these jobs in the following shop. The results show that the M has the advantage of shortening the makespan upon separate shops.展开更多
Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. Thi...Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony(IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability,the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.展开更多
An integrated model is presented to schedule the container handling system. The objective is to improve the coop- eration between different types of equipments, and to increase the productivity of the terminal. The pr...An integrated model is presented to schedule the container handling system. The objective is to improve the coop- eration between different types of equipments, and to increase the productivity of the terminal. The problem is formulated as a Hybrid Flow Shop Scheduling problem with precedence constraint, setup times and blocking (HFSS-B). A tabu search algorithm is proposed to solve this problem. The quality and efficiency of the proposed algorithm is analyzed from the computational point of view.展开更多
The qualification run(qual-run) as a technique to avoid possible quality problems of products inevitably leads to much longer cycle time. To effectively balance the trade-off between the qual-run and setup times,a sch...The qualification run(qual-run) as a technique to avoid possible quality problems of products inevitably leads to much longer cycle time. To effectively balance the trade-off between the qual-run and setup times,a scheduling model of a single machine with multiple families was developed and an adaptive differential evolution algorithm based on catastrophe with depth neighborhood search was applied to resolve the problem. First,a scheduling problem domain was described,and a mathematical programming model was set up with an objective of minimizing makespan. Further,several theorems were developed to construct feasible solutions. On the basis of differential evolution,the depth neighborhood search operator was adopted to search a wide range of solutions. In addition,the adaptive process and catastrophe theory were combined to improve the performance of the algorithm. Finally,simulation experiments were carried out and the results indicated that the proposed algorithm was effective and efficient.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
基金funded by the Ministry of Industry and Information Technology of the People’s Republic of China(Nos.[2018]473,[2019]331).
文摘Offshore engineering construction projects are large and complex,having the characteristics of multiple execution modes andmultiple resource constraints.Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems(RCPSPs).To solve RCPSP problems in offshore engineering construction more rapidly,a hybrid genetic algorithmwas established.To solve the defects of genetic algorithms,which easily fall into the local optimal solution,a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation.Then,an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions,reduce the computation time and avoid premature convergence.A calibrated function method was used to cater to the roulette rules,and appropriate rules for encoding,decoding and crossover/mutation were designed.Finally,a simple network was designed and validated using the case study of a real offshore project.The performance of the genetic algorithmand a simulated annealing algorithmwas compared to validate the feasibility and effectiveness of the approach.
基金supported by National Natural Science Foundation of China(Grant No.61806138)the Central Government Guides Local Science and Technology Development Funds(Grant No.YDZJSX2021A038)+2 种基金Key RD Program of Shanxi Province(International Cooperation)under Grant No.201903D421048Outstanding Innovation Project for Graduate Students of Taiyuan University of Science and Technology(Project No.XCX211004)China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)(Grant 2021FNA04014).
文摘Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.
文摘Remote and Hybrid work has been a common practice for many organizations in recent years. It has many advantages such as offering a better work-life balance but it might also negatively affect productivity and teamwork. While an organization would like to satisfy the remote/hybrid preferences of its employees, it also must ensure that there are enough people working in the office to satisfy certain professional needs. Finding the right balance between in-office and remote work is not an easy task. We develop three optimization models to give solutions to the problem. The most comprehensive model allows employees to work remotely some days of the week and flexible hours for those weekdays when employees work in the office. Our computational results show that the models are very time-efficient in practice. The computational results also include a sensitivity analysis of the most comprehensive model.
基金supported by the National Natural Science Fundation of China (60774082 70871065+2 种基金 60834004)the Program for New Century Excellent Talents in University (NCET-10-0505)the Doctoral Program Foundation of Institutions of Higher Education of China(20100002110014)
文摘Aiming at the hybrid flow-shop (HFS) scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution (DE) is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.
基金supported by the National Key Basic Research Development Program of China (Grant No. 2002CCA00700)
文摘Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm (POA) in combination with the genetic algorithm (GA). After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.
基金Supported by National Key R&D Program of China (Grant No. 2019YFB1704603)National Natural Science Foundation of China (Grant Nos. U21B2029 and 51825502)。
文摘The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem(IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem(HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles(AGVs)is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation(including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
文摘In view of the problem that the packet information preempted netw ork resources in the process of transmission in the CAN bus,w hich leads to the low utilization of netw ork resources and the low accuracy of information transmission. Thus,a hybrid scheduling algorithm NM TS based on CAN bus is proposed,in the NM TS hybrid scheduling algorithm,the dynamic scheduling algorithm EDF is used to schedule hard real-time messages to solve the problem of low utilization of netw ork resources; the static scheduling algorithm RM S is used to schedule soft real-time messages and non real-time messages,so as to solve the problem of low accuracy of information transmission. By using M ATLAB softw are,the CAN netw ork model can be built,the EDF algorithm,RM S algorithm and NM TS algorithm are simulated. The experimental results show that the netw ork resources utilization is 90%,the packet loss rate is 0% of the NM TS algorithm. Therefore,The hybrid scheduling algorithm based on CAN bus NM TS has the characteristics of high netw ork resource utilization and high accuracy of information transmission,w hich w ill be very helpful for further research of CAN bus.
基金Shanghai Municipal Natural Science Foundation of China(No.10ZR1431700)
文摘Aiming at the flexible flowshop group scheduling problem,taking sequence dependent setup time and machine skipping into account, a mathematical model for minimizing makespan is established,and a hybrid differential evolution( HDE) algorithm based on greedy constructive procedure( GCP) is proposed,which combines differential evolution( DE) with tabu search( TS). DE is applied to generating the elite individuals of population,while TS is used for finding the optimal value by making perturbation in selected elite individuals. A lower bounding technique is developed to evaluate the quality of proposed algorithm. Experimental results verify the effectiveness and feasibility of proposed algorithm.
基金TheNationalGrandFundamentalResearch973ProgramofChina (No .G19980 30 6 0 0 )
文摘A new local search method with hybrid neighborhood for Job shop scheduling problem is developed. The proposed hybrid neighborhood is not only efficient in local search, but also can help overcome entrapments while search procedure get trapped at local optima and carry the search to areas of the feasible set with better prospect. New strategies used for breaking out of entrapments are presented and they are helpful for the procedure to improve local optima. A performance comparison of the proposed method with some best-performing algorithms on all 10-job, 10-machine benchmark problems and the other two problems generated by Fisher and Thompson (ie., FT6 and FT20)is made. The experiment results show the better optimal performance of the proposed algorithm.
基金This work was supported by the National Key R&D Program of China(2018AAA0101400)the National Natural Science Foundation of China(62173251,61921004,U1713209)the Natural Science Foundation of Jiangsu Province of China(BK20202006).
文摘Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.
文摘In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.
基金Foundation item: Project(2012AA051603) supported by the National High Technology Research and Development Program 863 Plan of China
文摘A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system (SLHS). An algorithm was proposed to select one (or a group of) sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finally, the algorithms are validated through an illustrative target tracking example.
基金the National Natural Science Foundation of China (No. 60533040)the Hi-Tech Research and Development Program (863) of China (Nos. 2007AA010304 and 2007AA01Z129)the Key Scientific and Technological Project of Hangzhou Tech-nology Bureau, China (No. 20062412B01)
文摘In hard real-time systems, schedulability analysis is not only one of the important means of guaranteeing the timelines of embedded software but also one of the fundamental theories of applying other new techniques, such as energy savings and fault tolerance. However, most of the existing schedulability analysis methods assume that schedulers use preemptive scheduling or non-preemptive scheduling. In this paper, we present a schedulability analysis method, i.e., the worst-case hybrid scheduling (WCHS) algorithm, which considers the influence of release jitters of transactions and extends schedulability analysis theory to timing analysis of linear transactions under fixed priority hybrid scheduling. To the best of our knowledge, this method is the first one on timing analysis of linear transactions under hybrid scheduling. An example is employed to demonstrate the use of this method. Experiments show that this method has lower computational complexity while keeping correctness, and that hybrid scheduling has little influence on the average worst-case response time (WCRT), but a negative impact on the schedulability of systems.
基金supported by the National Basic Research Program of China(No.2007CB316502)the National Natural Science Foundation of China(No.60534060)
文摘Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.
基金This project is supported by National Natural Science Foundation of China (No. 50575137)Provincial Natural Science Foundation of Zhejiang, China (No. Z604342)Scientific Research Fund of Zhejiang Provincial Educational Committee, China (No. 20051643).
文摘A new concept of multi-shop (M ) is put forward which contains all basic shops including open shop (O), job shop (J ), flow shop (F ) and hybrid flow shop (H ) so that these basic shop can be scheduled together. Several algorithms including ant colony optimization (ACO), most work remaining (MWR), least work remaining (LWR), longest processing time (LPT) and shortest processing time (SPT) are used for scheduling the M. Numerical experiments of the M adopting data of some car and reC series benchmark instances are tested. The results show that the ACO algorithm has better performance for scheduling the M than the other algorithms, if minimizing the makespan ( Cmax^*) is taken as the objective function. As a comparison, the separate shops contained in the M are also scheduled by the ACO algorithm for the same objective function, when the completing time of the jobs in the previous shop is taken as the ready time of these jobs in the following shop. The results show that the M has the advantage of shortening the makespan upon separate shops.
基金Supported by the National Natural Science Foundation of China(51705177,51575212)the Program for New Century Excellent Talents in University(NCET-13-0106)the Program for HUST Academic Frontier Youth Team
文摘Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony(IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability,the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.
文摘An integrated model is presented to schedule the container handling system. The objective is to improve the coop- eration between different types of equipments, and to increase the productivity of the terminal. The problem is formulated as a Hybrid Flow Shop Scheduling problem with precedence constraint, setup times and blocking (HFSS-B). A tabu search algorithm is proposed to solve this problem. The quality and efficiency of the proposed algorithm is analyzed from the computational point of view.
基金Sponsored by the National Natural Science Foundation of China(Grant No.71471135)
文摘The qualification run(qual-run) as a technique to avoid possible quality problems of products inevitably leads to much longer cycle time. To effectively balance the trade-off between the qual-run and setup times,a scheduling model of a single machine with multiple families was developed and an adaptive differential evolution algorithm based on catastrophe with depth neighborhood search was applied to resolve the problem. First,a scheduling problem domain was described,and a mathematical programming model was set up with an objective of minimizing makespan. Further,several theorems were developed to construct feasible solutions. On the basis of differential evolution,the depth neighborhood search operator was adopted to search a wide range of solutions. In addition,the adaptive process and catastrophe theory were combined to improve the performance of the algorithm. Finally,simulation experiments were carried out and the results indicated that the proposed algorithm was effective and efficient.
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.