با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
21 |
Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0
زمانبندی مبتنی بر یادگیری تقویتی عمیق مبتنی بر AGV با قاعده مختلط برای کف انعطاف پذیر در صنعت 4.0-2020 Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles
(AGVs) has been widely used in flexible shop floor for material handling. However, great challenges aroused by
the high dynamics, complexity, and uncertainty of the shop floor environment still exists on AGVs real-time
scheduling. To address these challenges, an adaptive deep reinforcement learning (DRL) based AGVs real-time
scheduling approach with mixed rule is proposed to the flexible shop floor to minimize the makespan and
delay ratio. Firstly, the problem of AGVs real-time scheduling is formulated as a Markov Decision Process (MDP)
in which state representation, action representation, reward function, and optimal mixed rule policy, are
described in detail. Then a novel deep q-network (DQN) method is further developed to achieve the optimal
mixed rule policy with which the suitable dispatching rules and AGVs can be selected to execute the scheduling
towards various states. Finally, the case study based on a real-world flexible shop floor is illustrated and the
results validate the feasibility and effectiveness of the proposed approach. Keywords: Automated guided vehicles | Real-time scheduling | Deep reinforcement learning | Industry 4.0 |
مقاله انگلیسی |
22 |
A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem
یک الگوریتم ژنتیک خودآموز مبتنی بر یادگیری تقویتی برای مسئله زمان بندی انعطاف پذیر مشاغل فروشگاهی -2020 As an important branch of production scheduling, flexible job-shop scheduling problem (FJSP) is difficult to solve
and is proven to be NP-hard. Many intelligent algorithms have been proposed to solve FJSP, but their key parameters
cannot be dynamically adjusted effectively during the calculation process, which causes the solution
efficiency and quality not being able to meet the production requirements. Therefore, a self-learning genetic
algorithm (SLGA) is proposed in this paper, in which genetic algorithm (GA) is adopted as the basic optimization
method and its key parameters are intelligently adjusted based on reinforcement learning (RL). Firstly, the selflearning
model is analyzed and constructed in SLGA, SARSA algorithm and Q-Learning algorithm are applied as
the learning methods at initial and later stages of optimization, respectively, and the conversion condition is
designed. Secondly, the state determination method and reward method are designed for RL in GA environment.
Finally, the learning effect and performance of SLGA in solving FJSP are compared with other algorithms using
two groups of benchmark data instances with different scales. Experiment results show that the proposed SLGA
significantly outperforms its competitors in solving FJSP. Keywords: Flexible job-shop scheduling problem (FJSP) | Self-learning genetic algorithm (SLGA) | Genetic algorithm (GA) | Reinforcement learning (RL) |
مقاله انگلیسی |
23 |
A two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems
یک الگوریتم تکامل دیفرانسیل چند مرحله ای چند کاره برای حل مشکلات برنامه ریزی پروژه محدود شده از منابع-2020 The Resource Constrained Project Scheduling problem (RCPSP) is a complex and combinatorial
optimization problem mostly relates with project management, construction industries, production
planning and manufacturing domains. Although several solution methods have been proposed, no
single method has been shown to be the best. Further, optimal solution of this type of problem
requires different requirements of the exploration and exploitation at different stages of the optimization
process. Considering these requirements, in this paper, a two-stage multi-operator differential
evolution (DE) algorithm, called TS-MODE, has been developed to solve RCPSP. TS-MODE starts with the
exploration stage, and based on the diversity of population and the quality of solutions, this approach
dynamically place more importance on the most-suitable DE, and then repeats the same process during
the exploitation phase. A complete evaluation of the components and parameters of the algorithms by
a Design of Experiments technique is also presented. A number of single-mode RCPSP data sets from
the project scheduling library (PSPLIB) have been considered to test the effectiveness and performance
of the proposed TS-MODE against selected recent well-known state-of-the-art algorithms. Those results
reveal the efficiency and competitiveness of the proposed TS-MODE approach. Keywords: Evolutionary algorithms | Differential evolution | Adaptive operator selection | Resource constrained project scheduling | problems |
مقاله انگلیسی |
24 |
Internet-of-things-based optimal smart city energy management considering shiftable loads and energy storage
مدیریت انرژی بهینه شهر هوشمند مبتنی بر اینترنت اشیا با توجه به بارهای قابل تغییر و ذخیره انرژی-2020 Formulating a novel mixed integer linear programing problem, this paper introduces an optimal
Internet-of-Things-based Energy Management (EM) framework for general distribution networks in
Smart Cities (SCs), in the presence of shiftable loads. The system’s decisions are optimally shared between
its two main designed layers; a “core cloud” and the “edge clouds”. The EM of a Microgrid (MG),
covered by an edge cloud, is directly done by its operator and the Distribution System Operator (DSO) is
responsible for optimising the EM of the core cloud. Changing the load consumption pattern, based on
market energy prices, for the edge clouds and their peak load hours, the framework results in decreasing
the total operation cost of the edge clouds. Using the optimal trading power of the MGs aggregators as
the input parameters of the core cloud optimisation problem, the DSO optimises the network’s total
operation cost addressing the optimal scheduling of the energy storages. The energy storages are charged
in low energy prices through the purchasing power from the market and discharged in high energy
prices to meet the demand of the network and to satisfy the energy required by the edge clouds. As a
result, the shiftable loads and the energy storages are used by the DSO and the MGs to meet the energy
balance with the minimum cost. Keywords: Energy management | Internet-of-Things | Microgrids | Optimal scheduling | Renewable energy sources |
مقاله انگلیسی |
25 |
Multi-objective stochastic programming energy management for integrated INVELOX turbines in microgrids: A new type of turbines
مدیریت انرژی برنامه نویسی تصادفی چند منظوره برای توربین های یکپارچه INVELOX در میکروگریدها: نوع جدیدی از توربین ها-2020 In this paper, a new type of wind turbine that is called INVELOX has been used. INVELOX has many
advantages such as six times more power generation than previous types, work at low speed, inconsiderable
maintenance and investment costs, and reduce the environmental effects of previous wind
turbines. Moreover, other renewable and nonrenewable generators are used in the energy management
and scheduling of the microgrid. The test case is a microgrid with selling and buying energy capability in
which the cost and pollution are considered as the objective functions. In the following, Uncertainties of
wind speed, solar radiation and electrical-thermal loads are investigated and a multi-objective stochastic
mixed integer linear programming is solved in the first scenario. Then, in the second scenario, the effects
of fuel cost uncertainty on generation units and objective functions have been studied. The Epsilon
constraints method and fuzzy satisfying are utilized to solve the problem and choose the best solution,
respectively. By using of INVELOX turbines, total cost and pollution of the microgrid in both deterministic
and stochastic planning are reduced from 192.68 $ to 97.23 $ and 249.28 $ to 126.38 $, as well 3334.76 Kg
to 3302.7 and 3925.63 to 3910.2 Kg respectively. Keywords: Energy management | INVELOX turbine | Microgrid | Renewable resource | Stochastic programming |
مقاله انگلیسی |
26 |
Utilizing renewable energy sources efficiently in hospitals using demand dispatch
استفاده از منابع انرژی تجدید پذیر با کارآیی در بیمارستان ها با استفاده از تقاضای ارسال -2020 Health centers and hospitals can be categorized as one of the major consumers of electrical energy in
building sectors. Due to their competitive environment, they need to decrease their costs, including
energy costs. On the other hand, environmental problems, lack of fossil fuels, and high energy consumption
lead to using alternative energy generation methods like renewable energy sources (RESs). In
this paper, we consider that the hospital can produce part of its energy from RESs for cost reduction and
we implement demand dispatch energy program for using RESs efficiently. The challenge is that the
main goal of hospital is providing health services not energy cost reduction. Therefore, we present a biobjective
formulation for using RESs in hospitals in a way to minimize costs and dissatisfaction by
scheduling the activities of the hospital by considering hospital’s specific constraints and limitations.
With the help of the proposed model, hospitals will decrease energy costs while maintaining comfort of
patients and surgeons at the same time. The model is solved using real data of a hospital in Iran, and
sensitivity analysis on different parameters is done. The proposed model will cause reduction in energy
cost of the hospital by implementing demand dispatch program for using RESs in the hospital. Keywords: Renewable energy sources | Demand dispatch | Energy management | Health centers | Hospitals | Bi-objective |
مقاله انگلیسی |
27 |
Energy-cognizant scheduling for preference-oriented fixed-priority real-time tasks
برنامه ریزی دانش شناخت انرژی برای وظایف در زمان واقعی با اولویت ثابت تنظیم گرا -2020 Energy management is one of the crucial design issues when executing real-time applications with stringent tim- ing requirements. Dynamic slowdown of processor voltage if accompanied with processor shutdown method, helps in better saving energy. Traditionally, energy management has been applied to real-time scheduling algo- rithms that prioritize tasks based on timing parameters only, however, recently applications having tasks with different execution-preferences on the same computing unit found significant importance in various areas. In this paper, dynamic voltage scaling (DVS) and dynamic power management (DPM) techniques are used for energy management while scheduling preference-oriented fixed-priority periodic real-time tasks. Preference-oriented energy-aware rate-monotonic scheduling (PER) and preference-oriented extended energy-aware rate-monotonic scheduling (PEER) algorithms are proposed that maximize energy savings while fulfilling preference-value of tasks. Extensive simulations show that PER and PEER outperforms in terms of energy savings when compared to several related studies. Keywords: Dynamic voltage | scaling Energy management | Fixed-priority | Preference-oriented | Rate-monotonic | Real-time tasks | Task scheduling |
مقاله انگلیسی |
28 |
Comfort evaluation of seasonally and daily used residential load insmart buildings for hottest areas via predictive mean vote method
ارزیابی راحتی ساختمانهای بار مسکونی فصلی و روزانه برای گرمترین مناطق با استفاده از روش پیش بینی میانگین رای گیری-2020 tIn this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mam-dani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonallyused appliances are considered for the analysis of the efficient energy management through these con-trollers. Energy management is performed using the two Demand Side Management (DSM) strategies:load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of thedaily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances,i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are appliedin this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of thehottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occu-pancy, price, decision control variables, priority and length of operation times of the appliances, whereasthe output parameters are: energy consumption, cost and thermal and appliance usage comfort. More-over, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger’spredictive mean vote method. The comfort is further investigated by incorporating the renewable energysources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllersas compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energyconsumption and cost of the proposed scenario. Keywords:Energy management | Thermal comfort | Appliance usage comfort | Fuzzy logic | Fuzzy inference systems |
مقاله انگلیسی |
29 |
Flexibility management model of home appliances to support DSO requests in smart grids
مدل مدیریت انعطاف پذیری لوازم خانگی برای پشتیبانی از درخواست DSO در شبکه های هوشمند-2020 Several initiates have been taken promoting clean energy and the use of local flexibility towards a more sustainable
and green economy. From a residential point of view, flexibility can be provided to operators using
home-appliances with the ability to modify their consumption profiles. These actions are part of demand response
programs and can be utilized to avoid problems, such as balancing/congestion, in distribution networks.
In this paper, we propose a model for aggregators flexibility provision in distribution networks. The model takes
advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision
a request from a distribution system operator (DSO) or a balance responsible party (BRP). Due to the
complex nature of the problem, evolutionary computation is evoked and different algorithms are implemented
for solving the formulation efficiently. A case study considering 20 residential houses equipped each with seven
types of home-appliances is used to test and compare the performance of evolutionary algorithms solving the
proposed model. Results show that the aggregator can fulfill a flexibility request from the DSO/BRP by rescheduling
the home-appliances loads for the next 24-h horizon while minimizing the costs associated with the
remuneration given to end-users Keywords: Demand response | Flexibility | Home appliances | Local energy management | Smart grids |
مقاله انگلیسی |
30 |
Efficient Load Control Based Demand Side Management Schemes Towards a Smart Energy Grid System
برنامه های مدیریت کنترل تقاضا مبتنی بر کنترل بار کارآمد به سمت یک سیستم شبکه انرژی هوشمند-2020 In this paper, we propose ecient load scheduling based demand side management
schemes for the objective of peak load reduction. We propose two
heuristic algorithms, named G-MinPeak and LevelMatch, which are based
on the generalized two-dimensional strip packing problem, where each of
the appliances has their specic timing requirements to be fullled. Furthermore,
we have proposed some improvement schemes that try to modify
the resulted schedule from the proposed heuristic algorithms to reduce the
peak. All the proposed algorithms and improvement schemes are experimented
using benchmark data sets for performance evaluation. Extensive
simulation studies have been conducted using practical data to evaluate the
performance of the algorithms in real life. The results obtained show that all
the proposed methodologies are thoroughly eective in reducing peak load,
resulting in smoother load proles. Specically, for the benchmark datasets,
the deviation from the optimal values has been about 6% and 7% for Level-
Match and G-MinPeak algorithms respectively and by using the improvement
schemes the deviations are further reduced up to 3% in many cases. For the
practical datasets, the proposed improvement schemes reduce the peak by
5:21 ???? 7:35% on top of the peaks obtained by the two proposed heuristic
algorithms without much computation overhead. Keywords: Demand side management | direct load control | heuristic algorithm | scheduling | energy management | smart grid |
مقاله انگلیسی |