2,968 research outputs found
A generic method for energy-efficient and energy-cost-effective production at the unit process level
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkāthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
Integrating sustainability into production scheduling in hybrid flow-shop environments
Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in
2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop
foors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance
of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into
scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of
human factors in production processes. This study proposes a new approach for optimizing the hybrid fow-shop scheduling
problems (HFSP) considering time-of-use electricity pricing, workersā fexibility, and sequence-dependent setup time (SDST).
Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective
optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution
method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto
evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs
better than the benchmarks considering the mean ideal distance, inverted generational distance, diversifcation, and quality
metrics, providing more versatile and better solutions for production and energy efciency
Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation
Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly
Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation
Ā© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly
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