Energy saving policies for a machine tool with warm-up, stochastic arrivals and buffer information

One of the measures for saving energy in manufacturing is the implementation of control strategies that reduces energy consumption during the machine idle periods. Specifically, the paper proposes a framework that integrates different control policies for switching the machine off when the production is not critical, and on either when the part flow has to be resumed or the queue has accumulated to a certain level. A general policy is formalized by modeling explicitly the power consumed in each machine state. A threshold policy is analyzed and the optimal parameter is provided for an M/M/1/K system. Numerical results are based on data acquired with dedicated experimental measurements on a real machining centre, and a comparison with common practices in manufacturing is also reported

Autonomous Energy-aware production systems control

Energy and resource efficiency has recently become one of the most relevant topics of research in manufacturing, both as industry accounts for a major part of the world energy consumption and in the context of the increasing attention to the need of sustainable development at planetary level. This work aims at paving the way to the development of novel energy-aware control policies of production systems, by means of autonomous decisions about their states in terms of production and energy consumption, exploiting the possibilities given by the new ICT technologies, such as Internet of Things and cloud computing, which allow seamless information sharing among the machines through an appropriate and standardized ICT infrastructure. The energy saving control approach investigated in this work exploits the current trend in research to reduce the idle time of machines in favor of stand-by states, obtaining significant savings in terms of energy, by allowing novel solutions for decentralized control. The proposed control enables the production machines to autonomously share with and process the information of the other machines in the system to decide in real-time their specific energy behaviour, even postponing processing if that is possible. The approach adopted includes conceptual development of the dynamic behaviour models of the system and the proposed policies, then their deployment in an application scenario taken by actual industry cases and data, enabling study of the performance of the system with a detailed design of experiments. The proposed approach represents a significant contribution to the state of the art, as the proposed energy-aware control enables decisions based on real-time information instead of statistically-based forecasts of part arrival rates, as in the previous literature; furthermore the approach is of relevant value for the practitioner, especially as it paves the way to an operationalization to the vision of Cyber-Physical Systems and Industry 4.0

Energy efficient control strategy for machine tools with stochastic arrivals and time dependent warm-up

Energy efficiency in manufacturing is becoming a challenging goal due to the demand of this sector in the worldwide scenario. One of the measures for saving energy is the implementation of control strategies that reduce machine energy consumption during the machine idle periods. This paper extends a threshold policy, that switches off the machine during interruptions of part flow, by modelling explicitly the warm-up time as dependent on the time period the machine stays in low power consumption state. The optimal policy parameter is provided numerically for general distributions of the part arrival time and general functions modelling the warm-up time. Numerical results are based on data acquired with dedicated experimental measurements on a real machining center

Design Model of Flow Lines to Include Switch-Off Policies Reducing Energy Consumption

One of the most promising approaches to reduce the amount of energy consumed in manufacturing systems is the switch off policy. This policy reduces the energy consumed when the machines are in the idle state. The main weakness of this policy is the reduction in the production rate of the manufacturing systems. The works proposed in the literature do not consider the design of the production lines for the introduction of switch off policies. This work proposes a design model for production lines that include a targeted imbalance among the workstations to cause designed idle time. The switch-off policy introduced in such designed production lines allows for a reduction in the energy consumed with any production rate loss. Simulation tests are conducted to verify the benefits of switch off policies in production lines designed for its. The simulation results show that the proposed line design allows for a reduction in energy consumption, with a defined loss in the throughput. The application of switch-off policies in the proposed flow line leads to a significant reduction in the energy used in unproductive states controlling the production loss

Actes du 11ème Atelier en Évaluation de Performances

International audienceLe présent document contient les actes du 11ème Atelier en Évaluation des Performances qui s'est tenu les 15-17 Mars 2016 au LAAS-CNRS, Toulouse. L’Atelier en Évaluation de Performances est une réunion destinée à faire s’exprimer et se rencontrer les jeunes chercheurs (doctorants et postdoctorants) dans le domaine de la Modélisation et de l’Évaluation de Performances, une discipline consacrée à l’étude et l’optimisation de systèmes dynamiques stochastiques et/ou temporisés apparaissant en Informatique, Télécommunications, Productique et Robotique entre autres. La présentation informelle de travaux, même en cours, y est encouragée afin de renforcer les interactions entre jeunes chercheurs et préparer des soumissions de nouveaux projets scientifiques. Des exposés de synthèse sur des domaines de recherche d’actualité, donnés par des chercheurs confirmés du domaine renforcent la partie formation de l’atelier

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Dynamic Network State Learning Model for Mobility Based WMSN Routing Protocol

The rising demand of wireless multimedia sensor networks (WMSNs) has motivated academia-industries to develop energy efficient, Quality of Service (QoS) and delay sensitive communication systems to meet major real-world demands like multimedia broadcast, security and surveillance systems, intelligent transport system, etc. Typically, energy efficiency, QoS and delay sensitive transmission are the inevitable requirements of WMSNs. Majority of the existing approaches either use physical layer or system level schemes that individually can’t assure optimal transmission decision to meet the demand. The cumulative efficiency of physical layer power control, adaptive modulation and coding and system level dynamic power management (DPM) are found significant to achieve these demands. With this motivation, in this paper a unified model is derived using enhanced reinforcement learning and stochastic optimization method. Exploiting physical as well as system level network state information, our proposed dynamic network state learning model (NSLM) applies stochastic optimization to learn network state-activity that derives an optimal DPM policy and PHY switching scheduling. NSLM applies known as well as unknown network state variables to derive transmission and PHY switching policy, where it considers DPM as constrained Markov decision process (MDP) problem. Here,the use of Hidden Markov Model and Lagrangian relaxation has made NSLM convergence swift that assures delay-sensitive, QoS enriched, and bandwidth and energy efficient transmission for WMSN under uncertain network conditions. Our proposed NSLM DPM model has outperformed traditional Q-Learning based DPM in terms of buffer cost, holding cost, overflow, energy consumption and bandwidth utilization

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Modeling, Control and Optimisation of Hybrid Systems in a Manufacturing Setting

This study comprises a body of work that investigates the performance of hybrid manufacturing systems. And we have provided a valuable insight into the development of the optimisation techniques for hybrid manufacturing system. With the primary objective of developing prac-tical mathematical algorithms that balance trade-o? cost between product quality and completion time. For sta-bility criterion, a sliding mode control was deployed

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin