Learning and Control using Gaussian Processes

Building physics-based models of complex physical systems like buildings and chemical plants is extremely cost and time prohibitive for applications such as real-time optimal control, production planning and supply chain logistics. Machine learning algorithms can reduce this cost and time complexity, and are, consequently, more scalable for large-scale physical systems. However, there are many practical challenges that must be addressed before employing machine learning for closed-loop control. This paper proposes the use of Gaussian Processes (GP) for learning control-oriented models: (1) We develop methods for the optimal experiment design (OED) of functional tests to learn models of a physical system, subject to stringent operational constraints and limited availability of the system. Using a Bayesian approach with GP, our methods seek to select the most informative data for optimally updating an existing model. (2) We also show that black-box GP models can be used for receding horizon optimal control with probabilistic guarantees on constraint satisfaction through chance constraints. (3) We further propose an online method for continuously improving the GP model in closed-loop with a real-time controller. Our methods are demonstrated and validated in a case study of building energy control and Demand Response

Active Voltage Control in Distribution Networks Including Distributed Energy Resources

The structure and control methods of existing distribution networks are planned assuming unidirectional power flows. The amount of generation connected to distribution networks is, however, constantly increasing which changes the operational and planning principles of distribution networks radically. Distributed generation (DG) affects power flows and fault currents in the distribution network and its effect on network operation can be positive or negative depending on the size, type, location and time variation of the generator. In weak distribution networks, voltage rise is usually the factor that limits the network’s hosting capacity for DG. At present, voltage rise is usually mitigated either by increasing the conductor size or by connecting the generator to a dedicated feeder. These passive approaches maintain the current network operational principles but can lead to high DG connection costs. The voltage rise can be mitigated also using active voltage control methods that change the operational principles of the network radically but can, in many cases, lead to significantly smaller total costs of the distribution network than the passive approach. The active voltage control methods can utilize active resources such as DG in their control and also the control principles of existing voltage control equipment such as the main transformer tap changer can be altered. Although active voltage control can often decrease the distribution network total costs and its effect on voltage quality can also be positive, the number of real distribution network implementations is still very low and the distribution network operators (DNOs) do not consider active voltage control as a real option in distribution network planning. Some work is, hence, still needed to enable widespread utilization of active voltage control. This thesis aims at overcoming some of the barriers that are, at present, preventing active voltage control from becoming business as usual for the DNOs. In this thesis, active voltage control methods that can be easily implemented to real distribution networks are developed. The developed methods are, at first, tested using time domain simulations. Operation of one coordinated voltage control (CVC) method is tested also using real time simulations and finally a real distribution network demonstration is conducted. The conducted simulations and demonstrations verify that the developed voltage control methods can be implemented relatively easily and that they are able to keep all network voltages between acceptable limits as long as an adequate amount of controllable resources is available. The developed methods control the substation voltage based on voltages in the whole distribution network and also reactive and real powers of distributed energy resources (DERs) are utilized in some of the developed CVC methods. All types of DERs capable of reactive or real power control can be utilized in the control. The distribution network planning tools and procedures used currently are not capable of taking active voltage control into account. DG interconnection planning is based only on two extreme loading conditions (maximum generation/minimum load and minimum generation/maximum load) and network effects and costs of alternative voltage control methods cannot be compared. In this thesis, the distribution network planning procedure is developed to enable comparison of different voltage control strategies. The statistical distribution network planning method is introduced and its usage is demonstrated in example cases. In statistical distribution network planning, load flow is calculated for every hour of the year using statistical-based hourly load and production curves. When the outputs of hourly load flows (e.g. annual losses, transmission charges and curtailed generation) are combined with investment costs the total costs of alternative voltage control strategies can be compared and the most cost-effective approach can be selected. The example calculations show that the most suitable voltage control strategy varies depending on the network and DG characteristics. The studies of this thesis aim at making the introduction of active voltage control as easy as possible to the DNOs. The developed CVC methods are such that they can be implemented as a part of the existing distribution management systems and they can utilize the already existing data transfer infrastructure of SCADA. The developed planning procedure can be implemented as a part of the existing network information systems. Hence, the currently used network planning and operational tools do not need to be replaced but only enhanced

Active Voltage Control in Distribution Networks Including Distributed Energy Resources

The structure and control methods of existing distribution networks are planned assuming unidirectional power flows. The amount of generation connected to distribution networks is, however, constantly increasing which changes the operational and planning principles of distribution networks radically. Distributed generation (DG) affects power flows and fault currents in the distribution network and its effect on network operation can be positive or negative depending on the size, type, location and time variation of the generator. In weak distribution networks, voltage rise is usually the factor that limits the network’s hosting capacity for DG. At present, voltage rise is usually mitigated either by increasing the conductor size or by connecting the generator to a dedicated feeder. These passive approaches maintain the current network operational principles but can lead to high DG connection costs. The voltage rise can be mitigated also using active voltage control methods that change the operational principles of the network radically but can, in many cases, lead to significantly smaller total costs of the distribution network than the passive approach. The active voltage control methods can utilize active resources such as DG in their control and also the control principles of existing voltage control equipment such as the main transformer tap changer can be altered. Although active voltage control can often decrease the distribution network total costs and its effect on voltage quality can also be positive, the number of real distribution network implementations is still very low and the distribution network operators (DNOs) do not consider active voltage control as a real option in distribution network planning. Some work is, hence, still needed to enable widespread utilization of active voltage control. This thesis aims at overcoming some of the barriers that are, at present, preventing active voltage control from becoming business as usual for the DNOs. In this thesis, active voltage control methods that can be easily implemented to real distribution networks are developed. The developed methods are, at first, tested using time domain simulations. Operation of one coordinated voltage control (CVC) method is tested also using real time simulations and finally a real distribution network demonstration is conducted. The conducted simulations and demonstrations verify that the developed voltage control methods can be implemented relatively easily and that they are able to keep all network voltages between acceptable limits as long as an adequate amount of controllable resources is available. The developed methods control the substation voltage based on voltages in the whole distribution network and also reactive and real powers of distributed energy resources (DERs) are utilized in some of the developed CVC methods. All types of DERs capable of reactive or real power control can be utilized in the control. The distribution network planning tools and procedures used currently are not capable of taking active voltage control into account. DG interconnection planning is based only on two extreme loading conditions (maximum generation/minimum load and minimum generation/maximum load) and network effects and costs of alternative voltage control methods cannot be compared. In this thesis, the distribution network planning procedure is developed to enable comparison of different voltage control strategies. The statistical distribution network planning method is introduced and its usage is demonstrated in example cases. In statistical distribution network planning, load flow is calculated for every hour of the year using statistical-based hourly load and production curves. When the outputs of hourly load flows (e.g. annual losses, transmission charges and curtailed generation) are combined with investment costs the total costs of alternative voltage control strategies can be compared and the most cost-effective approach can be selected. The example calculations show that the most suitable voltage control strategy varies depending on the network and DG characteristics. The studies of this thesis aim at making the introduction of active voltage control as easy as possible to the DNOs. The developed CVC methods are such that they can be implemented as a part of the existing distribution management systems and they can utilize the already existing data transfer infrastructure of SCADA. The developed planning procedure can be implemented as a part of the existing network information systems. Hence, the currently used network planning and operational tools do not need to be replaced but only enhanced

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Benchmarking VisualStudio.NET for the development and implementation of a manufacturing execution system

The focus of this thesis is to show the utility of Microsoft\u27s\u27 .NET framework in developing and implementing a MES system. The manufacturing environment today, more than ever, is working towards achieving better yields, productivity, quality, and customer satisfaction. Companies such as DELL are rapidly outgrowing their competition due to better management of their product lifecycles. The time between receiving a new order to the time the final product is shipped is getting shorter. Historically, business management applications such as Enterprise Resource Planning (ERP) systems and Customer Relationship Management (CRM) systems have been implemented without too much importance given to the operational and shop floor needs. The fact is that these business systems can be successful only when they are properly integrated with real-time data from the shop floor, which is the core of any manufacturing set-up. A Manufacturing Execution System or a MES is this link between the shop floor and the top floor. MESA international defines MES as Systems that deliver information enabling the optimization of production activities from order launch to finished goods Thus, a MES provides the right information to the right people at the right time in a right format, to help them make well-informed decisions. Thus, a necessity for an efficient MES is high capability of integration with the existing systems on the operational level. This is where Microsoft\u27s\u27 VS.NET fits in. Microsoft defines .NET as A set of software technologies for connecting information, people, systems and devices . The vision of .NET is to enable the end user to connect to information from any place at anytime, using any device and in a manner that is independent of the platform on which the service is based. The building block of the .NET framework is the Common Language Runtime or CLR, which is capable of converting data from its original format into a format understandable to .NET and then use that format to interface with its client. This feature that .NET provides holds the key in the context of a MES development and implementation. The aim of this applied research is to design a MES using VS.NET to control the working of a Flexible Manufacturing System (FMS) namely CAMCELL. The architecture used for the MES will then be gauged against an MES implementation done previously using a Siemens\u27 PC-based automation technology and Visual FoxPro. This study will integrate the Siemens\u27 technology with the .NET framework to enhance the resulting MES efficiency. The shop floor details or the real-time data collection will be done using the databases from WinCC and data aggregation and manipulation will be done within the .NET framework. The software architecture used for this study will achieve vertical integration between the CAMCELL ERP layer, the MES layer and the Control layer. The study will demonstrate how the data stored in a high level ERP database can be converted into useful information for the control layer for process control and also how real-time information gathered from the control layer can be filtered into useful information up to the ERP layer to facilitate the decision making process. VS.NET user interface screens will be proposed to support these activities. The performance of the proposed architecture will be compared to that from previous studies, thus benchmarking VS.NET for the implementation of the MES

Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions

"This is an Accepted Manuscript of an article published in International Journal of Production Research on December 2014, available online: http://www.tandfonline.com/10.1080/00207543.2014.920115."In this paper, we formulate the material requirements planning) problem of a first-tier supplier in an automobile supply chain through a fuzzy multi-objective decision model, which considers three conflictive objectives to optimise: minimisation of normal, overtime and subcontracted production costs of finished goods plus the inventory costs of finished goods, raw materials and components; minimisation of idle time; minimisation of backorder quantities. Lack of knowledge or epistemic uncertainty is considered in the demand, available and required capacity data. Integrity conditions for the main decision variables of the problem are also considered. For the solution methodology, we use a fuzzy goal programming approach where the importance of the relations among the goals is considered fuzzy instead of using a crisp definition of goal weights. For illustration purposes, an example based on modifications of real-world industrial problems is used.This work has been funded by the Universitat Politecnica de Valencia Project: 'Material Requirements Planning Fourth Generation (MRPIV)' (Ref. PAID-05-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research. 52(23):6971-6988. doi:10.1080/00207543.2014.920115S697169885223Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427-1433. doi:10.1016/j.ejor.2005.11.049Alfieri, A., & Matta, A. (2010). 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International Journal of Production Research, 45(6), 1429-1447. doi:10.1080/00207540600634949Díaz-Madroñero, M., Mula, J., & Jiménez, M. (2013). A Modified Approach Based on Ranking Fuzzy Numbers for Fuzzy Integer Programming with Equality Constraints. Annals of Industrial Engineering 2012, 225-233. doi:10.1007/978-1-4471-5349-8_27DOLGUI, A., BEN AMMAR, O., HNAIEN, F., & LOULY, M. A. O. (2013). A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty. Studies in Informatics and Control, 22(3). doi:10.24846/v22i3y201302Dubois, D. (2011). The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems, 184(1), 3-28. doi:10.1016/j.fss.2011.06.003Grabot, B., Geneste, L., Reynoso-Castillo, G., & V�rot, S. (2005). Integration of uncertain and imprecise orders in the MRP method. Journal of Intelligent Manufacturing, 16(2), 215-234. doi:10.1007/s10845-004-5890-xGuillaume, R., Thierry, C., & Grabot, B. (2010). 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Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches