The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the f...
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The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (sfl) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques. (C) 2016 Published by Elsevier Ltd.
Now a days, an integrated manufacturing environment is essential in modem manufacturing industries. To achieve truly integrated manufacturing systems, the integration of Material Requirements Planning (MRP) and job sh...
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Now a days, an integrated manufacturing environment is essential in modem manufacturing industries. To achieve truly integrated manufacturing systems, the integration of Material Requirements Planning (MRP) and job shop is essential. In the job shop scheduling problem n jobs have to be processed on m different machines and each job consists of a sequence of tasks that have to be processed during a fixed length on a given machine for an uninterrupted time period. In this work, enterprise application framework for integrating MRP with job shop scheduling is developed. This integrated system consists of two modules. First module provides a common material representation for material requirement planning and scheduling to tackle the variability in job durations and machine allocations. The second model provides an integrated heterogeneous manufacturing facilities with feasible production schedule. At the first stage, the application accepts the customer's order and performs material requirement planning. Further the master production schedule dispatches the daily planned order with all production resource requirement to job shop. At the same time, MRP sends order to supplier for purchasing raw materials. Shuffled Frog Leaping algorithm (sfl) is used to find an optimum schedule as well as refine the makespan results. Once the customers add or modify orders, MRP system will update the resource data automatically and respond to the changes of customer requirements rapidly. In this integrated application, the MRP subsystem has been proposed to computerize the existing system with *** and MYSQL Server to perform Business-to-Customer transactions and MRP logic, while the job shop simulator generates different sequences randomly and using Gantt chart initial makespan was calculated. Initial solutions refined with sfl algorithm and optimum schedule is generated. MYSQL Server Module is used for integrating MRP with Job shop simulator using Visual Basic. NET as the front end a
In this paper, a new hybrid approach based on hierarchical gradient based control and Shuffled Frog Leaping optimization algorithm (sflA) is presented for optimal control of large-scale systems. In this approach, the ...
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ISBN:
(纸本)9781467387378
In this paper, a new hybrid approach based on hierarchical gradient based control and Shuffled Frog Leaping optimization algorithm (sflA) is presented for optimal control of large-scale systems. In this approach, the large-scale system is decomposed into smaller subsystems and then solved separately at the first level. Afterward at the second level, a coordinator coordinate the subsystems to achieve overall optimal solution. For this, the discrete-time linear quadratic regulators (DLQR) with prescribed degree of stability are used to control each subsystem in the first level in which the sfl optimization algorithm is employed for optimizing the cost function of the DLQR. In the second level, the solutions obtained from the first level are coordinated using gradient-type strategy, which is updated by the error of the coordination vector. The proposed method is simulated on the aircraft system and the obtained results are compared with the centralized optimal control.
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