Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

A new quantum key distribution resource allocation and routing optimization scheme

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

Spatial and Temporal Distribution Characteristics of Solar Energy Resources in Tibet

The Tibet Plateau is one of the regions with the richest solar energy resources in the world.In the process of achieving carbon neutrality in China,the development and utilization of solar energy resources in the region will play an important role.In this study,the gridded solar resource data with 1km resolution in Tibet were obtained by spatial correction and downscaling of SMARTS model.On this basis,the spatial and temporal distribution characteristics of solar energy resources in the region in the past 30 years(1991–2020)are finely evaluated,and the annual global horizontal radiation resource is calculated.The results show that:1)The average annual global horizontal radiation amount in Tibet is 1816 kWh/m^(2).More than 60%of the area belongs to the“Most abundant”(GHI≥1750 kWh/m^(2))area of China’s solar energy resources category A,and nearly 40%belongs to the“Quite abundant”(1400≤GHI<1750)area of China’s solar energy resource category B.2)In space,the solar energy resources in Tibet increased gradually from north to south and from east to west.Lhasa,Central and Eastern Shigatse,Shannan,and Southwestern Ali are the most abundant cities,with a maximum annual radiation level of 2189 kWh/m2.3)In terms of time,the total horizontal radiation in Tibet was the highest in May and the lowest in December.74%of the total area belongs to the“Very stable”(R_(w)≥0.47)area of solar resource stability category A,and 26%belongs to the“stable”(0.36≤R_(w)<0.47)area of solar resource stability category B.Solar energy resources in the region show the characteristics of both strong and stable.Average solar energy resources in the region have shown a fluctuating downward trend over the past 30 years,with an average decline of about 12.86(kWh/m2)per decade.4)In terms of solar radiation resources reaching the earth’s surface,the theoretical total amount of annual horizontal radiation in Tibet is about 240.07 billion tons of standard coal or 222.91 billion kilowatts on average.

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Distribution,exploitation,and utilization of intermediate-to-deep geothermal resources in eastern China

The part of China,east of the Hu Huanyong Line,is commonly referred to as eastern China.It is characterized by a high population density and a well-developed economy;it also has huge energy demands.This study assesses and promotes the large-scale development of geothermal resources in eastern China by analyzing deep geological structures,geothermal regimes,and typical geothermal systems.These analyses are based on data collected from geotectology,deep geophysics,geothermics,structural geology,and petrology.Determining the distribution patterns of intermediate-to-deep geothermal resources in the region helps develop prospects for their exploitation and utilization.Eastern China hosts superimposed layers of rocks from three major,global tectonic domainsd namely Paleo-Asian,Circum-Pacific,and Tethyan rocks.The structure of its crust and mantle exhibits a special flyover pattern,with basins and mountains as well as well-spaced uplifts and depressions alternatively on top.The lithosphere in Northeast China and North China is characterized by a thin,low density crust and mantle,whereas the lithosphere in South China has a thin,low density crust and a thick,high density mantle.The middle and upper crust contain geobodies with high conductivity and low velocity,with varying degrees of development that create favorable conditions for the formation and enrichment of geothermal resources.Moderate-to-high temperature geothermal resources are distributed in the MesozoiceCenozoic basins in eastern China,although moderate temperature geothermal resources with low abundance dominate.Porous sandstone reservoirs,karstified fractured-vuggy carbonate reservoirs,and fissured granite reservoirs are the main types of geothermal reservoirs in this region.Under the currently available technical conditions,the exploitation and utilization of geothermal resources in eastern China favor direct utilization over large-scale geothermal power generation.In Northeast China and North China,geothermal resources could be applied for large-scale geothermal heating purposes;geothermal heating could be applied during winter along parts of the Yangtze River while geothermal cooling would be more suitable for summer there;geothermal cooling could also be applied to much of South China.Geothermal resources can also be applied to high value-added industries,to aid agricultural practices,and for tourism.

Distributed Resource Allocation in Ultra-Dense Networks via Belief Propagation

Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes promise huge performance improvement at the cost of cooperation among base stations,the large numbers of user equipment and base station make jointly optimizing the available resource very challenging and even prohibitive. How to decompose the resource allocation problem is a critical issue. In this paper,we exploit factor graphs to design a distributed resource allocation algorithm for ultra dense networks,which consists of power allocation,subcarrier allocation and cell association. The proposed factor graph based distributed algorithm can decompose the joint optimization problem of resource allocation into a series of low complexity subproblems with much lower dimensionality,and the original optimization problem can be efficiently solved via solving these subproblems iteratively. In addition,based on the proposed algorithm the amounts of exchanging information overhead between the resulting subprob-lems are also reduced. The proposed distributed algorithm can be understood as solving largely dimensional optimization problem in a soft manner,which is much preferred in practical scenarios. Finally,the performance of the proposed low complexity distributed algorithm is evaluated by several numerical results.

Resource Allocation based on Shared Criterion in OFDMA Distributed Radio Access Network

Distributed radio access network (DRAN) is a novel wireless access architecture and can solve the problem of the available spectrum scarcity in wireless communications. In this paper, we investigate resource allocation for the downlink of OFDMA DRAN. Unlike previous exclusive criterion based algorithms that allocate each subcarrier to only one user in the system, the proposed algorithms are based on shared criterion that allow each subcarrier to be allocated to multiple users through different antennas and to only one user through same antenna. First, an adaptive resource allocation algorithm based on shared criterion is proposed to maximize total system rate under each user's minimal rate and each antenna's maximal power constraints. Then we improve the above algorithm by considering the influence of the resource allocation scheme on single user. The simulation results show that the shared criterion based algorithm provide much higher total system rate than that of the exclusive criterion based algorithm at the expense of the outage performance and the fairness, while the improved algorithm based on shared criterion can achieve a good tradeoff performance.

Short-term Distributed Energy Resource Scheduling for a DC Microgrid

A microgrid is associated with a low voltage distribution power network and inherits small modular generation systems and loads that have certain coordinated functions to provide the solution to supply premium power to remote or specific areas. Similar to conventional power systems, the energy management of distributed generation resources (DERs) is carried out to minimize the operation cost and maximize benefit of installation of DERS in a microgrid. This paper presents the process of implementing the short-term DER scheduling function for a dc microgrid. The optimal scheduling results for two operation modes are then reported.

Joint resource allocation scheme for target tracking in distributed MIMO radar systems

A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them,this algorithm can help achieving a better performance within the same resource constraints.Firstly,the Bayesian Cramer-Rao bound(BCRB)is derived from it.Secondly,a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived.Thirdly,the optimization problem involved with three variable vectors is formulated,which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector.Then,the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation(SPCA)algorithm.Finally,the validity of the proposed method is demonstrated with simulation results.

Distributed Human Resource Redistribution System Based on Agent and Ontology

A computer system for human resource （HR） redistribution system is presented to solve the balance problem of the ＂surplus resources＂ and ＂surplus tasks＂ among a group of project units. The system architecture is designed in a compositional manner using the elements of agent technology and knowledge technology. A combination of generic agent models, ontology and knowledge provides an effective approach to address the dynamic, distributed and knowledge-intensive characters of the HR management. In the system, the broker agent acting as intermediary provides matchmaking services to the domain agents, and the individual domain agents communicate directly with each other. The HR ontology provides the semantic match of the surplus task and the surplus resource. Finally, an application example is presented to illustrate the achieved solution for a concrete scenario. This novel way offers a comprehensive HR exchange solution and is snitablc for both intra-organizational and inter-organizational HR management.

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

In this paper,accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network,which enables a hyper-exponential convergence rate.Specifically,an inertial fast-slow dynamical system with vanishing damping is introduced,based on which the distributed saddle point algorithm is designed.The dual variables are updated in two time scales,i.e.,the fast manifold and the slow manifold.In the fast manifold,the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol.In the slow manifold,the updating of the Lagrangian multipliers is accelerated by inertial terms.Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation.The simulation of the application in the energy dispatch problem verifies the result,which demonstrates the fast convergence of the proposed saddle point dynamics.

Collaborative Multiple Access And Energy-Efficient Resource Allocation in Distributed Maritime Wireless Networks

With the rapid increasing of maritime activities, maritime wireless networks(MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper,we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access(RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency(EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover,the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.

Optimal distributed resource allocation in a wireless sensor network for control systems

Wireless technology is applied increasingly in networked control systems. A new form of wireless network called wireless sensor network can bring control systems some advantages, such as flexibility and feasibility of network deployment at low costs, while it also raises some new challenges. First, the communication resources shared by all the control loops are limited. Second, the wireless and multi-hop character of sensor network makes the resources scheduling more difficult. Thus, how to effectively allocate the limited communication resources for those control loops is an important problem. In this paper, this problem is formulated as an optimal sampling frequency assignment problem, where the objective function is to maximize the utility of control systems, subject to channel capacity constraints. Then an iterative distributed algorithm based on local buffer information is proposed. Finally, the simulation results show that the proposed algorithm can effectively allocate the limited communication resource in a distributed way. It can achieve the optimal quality of the control system and adapt to the network load changes.

An IEEE 1547-Based Power Conditioner Test System for Distributed Energy Resources

Power conditioner, that is responsible for electric power conversion, is a critical component used in many renewable energy power generation systems. Most of the electric power produced by distributed energy resources cannot directly import to utility network without power conversion. Meanwhile, power conversion may includes several different types, for example AC/DC, and DC/AC, which is realized by a variety types of power conditioners in the electric power system. Currently, many concerns are focused on the operation of these power conditioners used in distributed energy resources due to the worse designing may cause the terrible influence on safety and performance characteristic of distributed energy resources. The power quality and reliability of interconnected electric power network may be affected as well. In the view of this, IEEE standards board provides a uniform standard for interconnection of distributed resources with electric power systems. It provides requirements relevant to the performance, operation, testing, safety considerations, and maintenance of the interconnection. Based on the IEEE 1547 standard, this paper presents a test system for power conditioners that are used in distributed energy resources or other renewable energy applications. Some of the test items that described in IEEE 1547.1 relevant to interconnection issues can be realized by proposed test system.

Multi-agent Based Protection on Highly Dominated Distributed Energy Resources

Highly penetration of Distributed Energy Resources (DER) on the grid systems nowadays makes the systems grow dynamically. The system become more complex and the protection system become more complicated. The protection relay should accommodate the system changes according to the system conditions and topologies. As part of developmental aspect of Distributed Artificial Intelligent, Multi Agent System (MAS) is a challenging method for improving the intelligent properties of relay protection. This paper introduces the use of MAS approach on radial distribution system protection dominated with DER using dispersed adaptive rule-based protection supported by distributed database agent. The simulation results confirmed that the proposed algorithm can respond within 15.05 ms.

Distributed versus Lumped Optimization of Cropping Pattern and Water Resources Utilization

Several whole-farm agro-economic optimization models have been developed to deal with lumped planning issues in the agriculture sector. However, these models cannot be used to devise appropriate management strategies at land parcel level, because of the differences between farm characteristics, and the increased complexity of the hydrological processes. Based on Spatial Farm Database (SFD) which is consisted of a number of farm-level spatial data, including location, paddock properties, owner specifications and budgets, it is possible to provide the farm manager with some suggestions regarding the optimal choice of crops and the area to be allocated for each one. To this end, genetic algorithm is used in order to cope with model nonlinearity and a large number of decision variables. In order to test the proposed model, the Mobarakabad district is modeled with 126 agriculture fields, and the optimization model is run for this area. Results showed that the optimization procedure can find more realistic farm-level optimal solutions due to its advantage in adequate modeling of field characteristics, common groundwater resources, and the associated constraints. The results of lumped optimizations could also be used as benchmarks for the purposes of comparison and interpretation.

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently.The distributed robotic control system can be used in real time for resolving various challenges such as localization,motion controlling,mapping,route planning,etc.The distributed robotic control system can manage different kinds of heterogenous devices.Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements.For instance,scheduling of resources(such as communication channel,computation unit,robot chassis,or sensor input)to the various system components turns out to be an essential requirement for completing the tasks on time.Therefore,resource scheduling is necessary for ensuring effective execution.In this regard,this paper introduces a novel chaotic shell game optimization algorithm(CSGOA)for resource scheduling,known as the CSGOA-RS technique for the distributed robotic control system environment.The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance.The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased.The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations.For validating the enhanced performance of the CSGOA-RS technique,a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures.The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.

Resources Allocation and Failures in Step Topology under Distributed Computing System

In the past years, distributed computing is gaining the popularity due to reduction in execution time and low cost involvement. On the basis of this, Mobile Adhoc Network (MANET) is also increasing worldwide with major advantage that it has no involvement of wire and transfer of data can be done by the virtual paths if the existing path is congested. In the present work, MANET is considered in the form of step topology which consists of heterogeneous collection of the devices. The work demonstrates the resources allocation for execution of tasks and it consists of selection of right path if the link failures and by pass link failures. It also consists of the resource management over the new proposed step topology. Entire work is modeled with the help of well known modeling language known as Unified Modeling Language (UML) and model demonstrates the resources allocation for execution of the tasks.