The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on th...The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on the hybrid flow shop scheduling problem with limited buffers(LBHFSP).This paper deeply investigates the LBHFSP to optimize the goal of the total completion time.To better solve the LBHFSP,a multi-level subpopulation-based particle swarm optimization algorithm(MLPSO)is proposed,which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO(particle swarm optimization)algorithm.In MLPSO,firstly,considering the impact of the limited buffers on the process of subsequent operations,a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers.Secondly,an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population.Afterward,a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability.Additionally,a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability.Lastly,numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years.The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP.展开更多
Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users....Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.However,most optimization research on hybrid energy storage has adopted rulebased passive-control principles,failing to fully leverage the advantages of active energy storage.To address this gap in the literature,this study develops a detailed model for an industrial park energy system with hybrid energy storage(IPES-HES),taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks.An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day.An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).A method using the improved NSGA-Ⅱ is developed for day-ahead nonlinear scheduling,based on configuration optimization.The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD(5.5%)and 1600.3 kW(43.7%),respectively,compared with an operation strategy based on proportional electricity storage on a typical summer day.Overall,the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.展开更多
This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved pr...This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.展开更多
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.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
Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.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
This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems.Two scheduling models were formulated as the elements of the composite schedul...This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems.Two scheduling models were formulated as the elements of the composite scheduling model,and the composite model was formulated composing these models with indispensable additional constraints.A hybrid genetic algorithm was developed to solve the composite scheduling problems.An improved representation based on random keys was developed to search permutation space.A genetic algorithm based dynamic programming approach was applied to select resource.The proposed technique and a previous technique are compared by three types of problems.All results indicate that the proposed technique is superior to the previous one.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 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
Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid ...Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.展开更多
The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construct...The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construction vehicles. The effect of power distribution and direction on shift characteristics of the parallel hybrid vehicle with operating loads is evaluated, which must be considered for optimal shift control. A power distribution factor is defined to accurately describe the power distribution and direction in various parallel hybrid systems. This paper proposes a Levenberg-Marquardt algorithm optimized neural network shift scheduling strategy. The methodology contains two objective functions, it is a dynamic combination of a dynamic shift schedule for optimal vehicle acceleration, and an energy-efficient shift schedule for optimal powertrain efficiency. The study is performed on a test bench under typical operating conditions of a wheel loader. The experimental results show that the proposed strategies offer effective and competitive shift performance.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
Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been prop...Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.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
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
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 ...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. Finallyo the algorithms are validated through an illustrative target tracking example.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
The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow S...The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.展开更多
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
基金supported in part by the National Natural Science Foundation of China under Grant No.52175490.
文摘The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on the hybrid flow shop scheduling problem with limited buffers(LBHFSP).This paper deeply investigates the LBHFSP to optimize the goal of the total completion time.To better solve the LBHFSP,a multi-level subpopulation-based particle swarm optimization algorithm(MLPSO)is proposed,which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO(particle swarm optimization)algorithm.In MLPSO,firstly,considering the impact of the limited buffers on the process of subsequent operations,a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers.Secondly,an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population.Afterward,a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability.Additionally,a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability.Lastly,numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years.The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP.
基金supported by National Key Research and Development Program of China(2022YFB4201003)the National Natural Science Foundation of China(52278104 and 52108076)the Science and Technology Innovation Program of Hunan Province(2023RC1042).
文摘Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.However,most optimization research on hybrid energy storage has adopted rulebased passive-control principles,failing to fully leverage the advantages of active energy storage.To address this gap in the literature,this study develops a detailed model for an industrial park energy system with hybrid energy storage(IPES-HES),taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks.An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day.An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).A method using the improved NSGA-Ⅱ is developed for day-ahead nonlinear scheduling,based on configuration optimization.The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD(5.5%)and 1600.3 kW(43.7%),respectively,compared with an operation strategy based on proportional electricity storage on a typical summer day.Overall,the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.
基金partially supported by the National Key Research and Development Program of the Ministry of Science and Technology of China(2022YFE0114200)the National Natural Science Foundation of China(U20A6004).
文摘This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
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the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
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Project supported by the Grant-in-Aid for Young Scientists (B) from the Ministry of Education,Culture,Sports,Science and Technology,Japan
文摘This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems.Two scheduling models were formulated as the elements of the composite scheduling model,and the composite model was formulated composing these models with indispensable additional constraints.A hybrid genetic algorithm was developed to solve the composite scheduling problems.An improved representation based on random keys was developed to search permutation space.A genetic algorithm based dynamic programming approach was applied to select resource.The proposed technique and a previous technique are compared by three types of problems.All results indicate that the proposed technique is superior to the previous one.
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.
基金the National Natural Science Foundation of China(Grant Number 61573264).
文摘Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.
基金Project(51805200)supported by the National Natural Science Foundation of ChinaProject(20170520096JH)supported by the Science and Technology Development Plan of Jilin Province,ChinaProject(2016YFC0802900)supported by the National Key R&D Program of China
文摘The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construction vehicles. The effect of power distribution and direction on shift characteristics of the parallel hybrid vehicle with operating loads is evaluated, which must be considered for optimal shift control. A power distribution factor is defined to accurately describe the power distribution and direction in various parallel hybrid systems. This paper proposes a Levenberg-Marquardt algorithm optimized neural network shift scheduling strategy. The methodology contains two objective functions, it is a dynamic combination of a dynamic shift schedule for optimal vehicle acceleration, and an energy-efficient shift schedule for optimal powertrain efficiency. The study is performed on a test bench under typical operating conditions of a wheel loader. The experimental results show that the proposed strategies offer effective and competitive shift performance.
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supported by the National Natural Science Foundation of China (52275480)the Guizhou Provincial Science and Technology Program of Qiankehe Zhongdi Guiding ([2023]02)+1 种基金the Guizhou Provincial Science and Technology Program of Qiankehe Platform Talent Project (GCC[2023]001)the Guizhou Provincial Science and Technology Project of Qiankehe Platform Project (KXJZ[2024]002).
文摘Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.
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. Finallyo the algorithms are validated through an illustrative target tracking example.
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partially supported by the Guangdong Basic and Applied Basic Research Foundation(2023A1515011531)the National Natural Science Foundation of China under Grant 62173356+2 种基金the Science and Technology Development Fund(FDCT),Macao SAR,under Grant 0019/2021/AZhuhai Industry-University-Research Project with Hongkong and Macao under Grant ZH22017002210014PWCthe Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(22JR10KA007).
文摘The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.
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.
