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Design of energy-Efficient timetables in two-way railway rapid transit lines

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2017年第Aug.期102卷 142-161页

作者： Canca, David Zarzo, Alejandro Univ Seville Dept Org Ind Gest & Empresas 1 Ind Engn & Management Sci Seville Spain Univ Politecn Madrid ETS Ingenieros Ind Dept Matemat Area Ind Madrid Spain Univ Granada Inst Carlos 1 Granada Spain

A methodology to design energy-efficient timetables in Rapid Railway Transit Networks is presented. Using an empirical description of the train energy consumption as a function of running times, the timetable design problem is modelled as a mixed integer non-linear optimization problem (MINLP) for a complete two-way line. In doing so, all the services in both directions along a certain planning horizon are considered while attending a known passengers' demand. The MINLP formulation, which depends on train loads, is fully linearised supposing train loads are fixed. A sequential mixed integer linear solving procedure is then used to solve the timetabling optimization problem with unknown train loads. The proposed methodology emphasizes the need of considering all the services running during the planning horizon when designing energy-efficient timetables, as consequence of the relationship among train speeds, frequency and fleet size of each line. Moreover, the convenience of considering the energy consumption as part of a broad objective function that includes other relevant costs is pointed out. Otherwise, passengers and operators could face up to an increase in the whole cost and a decrease in the quality of service. A real data scenario, based on the C-2 Line of the Madrid Metropolitan Railways, is used to illustrate the proposed methodology and to discuss the differences between the energy efficient solutions and those obtained when considering operation and acquisition costs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Railway rapid transit Timetabling Energy consumption mixed integer non-linear programming Sequential MILP procedure

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Empirical decision model learning

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ARTIFICIAL INTELLIGENCE 2017年 244卷 343-367页

作者： Lombardi, Michele Milano, Michela Bartolini, Andrea Univ Bologna DISI I-40126 Bologna Italy Univ Bologna DEI I-40126 Bologna Italy

One of the biggest challenges in the design of real-world decision support systems is coming up with a good combinatorial optimization model. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization. In this paper, we propose a methodology called Empirical Model Learning (EML) that relies on Machine Learning for obtaining components of a prescriptive model, using data either extracted from a predictive model or harvested from a real system. In a way, EML can be considered as a technique to merge predictive and prescriptive analytics. All models introduce some form of approximation. Citing G.E.P. Box [1] "Essentially, all models are wrong, but some of them are useful". In EML, models are useful if they provide adequate accuracy, and if they can be effectively exploited by solvers for finding high-quality solutions. We show how to ground EML on a case study of thermal-aware workload dispatching. We use two learning methods, namely Artificial Neural Networks and Decision Trees and we show how to encapsulate the learned model in a number of optimization techniques, namely Local Search, Constraint programming, mixed integer non-linear programming and SAT Modulo Theories. We demonstrate the effectiveness of the EML approach by comparing our results with those obtained using expert-designed models. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Machine learning Complex systems Local search Constraint programming mixed integer non-linear programming SAT modulo theories Artificial neural networks Decision trees

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A multi-period inventory transportation model for tactical planning of food grain supply chain

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 110卷 379-394页

作者： Mogale, D. G. Dolgui, Alexandre Kandhway, Rishabh Kumar, Krishna Tiwari, Manoj Kumar Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India CNRS IS2N IMT Atlantique UMR 6004 BP 20722 F-44307 Nantes 3 France

The food grain supply chain problem of the Public Distribution System (PDS) of India is addressed in this paper to satisfy the demand of the deficit Indian states. The problem involves the transportation of bulk food grain by capacitated vehicles from surplus states to deficit states through silo storage. A mixed integer non-linear programming (MINLP) model is formulated which seeks to minimize the overall cost including bulk food grain shipment, storage, and operational cost. The model incorporates the novel vehicle preference constraints along with the seasonal procurement, silo storage, vehicle capacity and demand satisfaction restrictions. The management of Indian food grain supply chain network is more intricate and difficult issue due to many uncertain interventions and its chaotic nature. To tackle the aforementioned problem an effective meta-heuristic which based on the strategy of sorting elite ants and pheromone trail updating called Improved Max-Min Ant System (IMMAS) is proposed. The solutions obtained through IMMAS is validated by implementing the Max-Min Ant System (MMAS). A sensitivity analysis has been performed to visualize the effect of model parameters on the solution quality. Finally, the statistical analysis is carried out for confirming the superiority of the proposed algorithm over the other. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Distribution system Supply chain management Inventory Transportation mixed integer non-linear programming Ant colony optimization

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Application-oriented mixed integer non-linear programming

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2010年第3期8卷 319-322页

作者： D'Ambrosio, Claudia Univ Bologna DEIS I-40136 Bologna Italy

This is a summary of the author's PhD thesis supervised by Andrea Lodi and defended on 16 April 2009 at the University of Bologna. The thesis is written in English and available for download at http://***/staff_pages/dambrosio/Phd_Th_***. The main topic of the thesis is mixed integer non-linear programming, with focus on non-convex problems (i.e., problems for which the feasible region of the continuous relaxation is a non-convex set) and real-world applications. Different kinds of algorithms are presented: linearization methods, heuristic and global optimization algorithms. Also, different kinds of real-world applications are solved, arising, for example, from Hydraulic and Electrical Engineering problems. The last part of the thesis is devoted to software and tools for mixed integer non-linear programming problems.

关键词： mixed integer non-linear programming Real-world application non-convex Algorithms MlNLP tools

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Optimized Risk Scores 17

Optimized Risk Scores

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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Ustun, Berk Rudin, Cynthia MIT 77 Massachusetts Ave Cambridge MA 02139 USA Duke Univ Durham NC USA

ISBN: (纸本)9781450348874

Risk scores are simple classification models that let users quickly assess risk by adding, subtracting, and multiplying a few small numbers. Such models are widely used in healthcare and criminal justice, but are often built ad hoc. In this paper, we present a principled approach to learn risk scores that are fully optimized for feature selection, integer coefficients, and operational constraints. We formulate the risk score problem as a mixed integer nonlinear program, and present a new cutting plane algorithm to efficiently recover its optimal solution. Our approach can fit optimized risk scores in a way that scales linearly with the sample size of a dataset, provides a proof of optimality, and obeys complex constraints without parameter tuning. We illustrate these benefits through an extensive set of numerical experiments, and an application where we build a customized risk score for ICU seizure prediction.

关键词： classification cutting plane methods mixed integer non-linear programming risk scores interpretable models seizure prediction

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Multirotor Performance Optimization using Genetic Algorithm 7

Multirotor Performance Optimization using Genetic Algorithm

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7th International Symposium on Embedded Computing and System Design (ISED)

作者： Agarwal, Umang Natl Inst Technol Dept Mech Engn Kurukshetra 136119 Haryana India

ISBN: (纸本)9781538630327

Designing and implementing a multirotor imposes some challenges: limited flight time and take-off mass, motor/propeller matching and unsteady dynamics. In this paper, these challenges are addressed by multi-objective optimization of a multirotor's operational parameters like flight velocity, flight altitude and motor/propeller rpm, and physical parameters like motor, battery and propeller geometry. New contributions are establishing a functional dependence of rotor thrust and power coefficients on design parameters, and incorporating aerodynamic effects within the optimization environment. Additionally, the rationality of optimization is enhanced by modeling physical parameters as discrete variables. The numerical results indicate that Genetic Algorithm reliably finds an optimum design, and improves flight time and maximum take-off mass by 35%.

关键词： Unmanned Aerial Vehicles Genetic Algorithm Multi-Objective Optimization Normal Equations of Least Squares mixed integer non-linear programming

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Economic optimization of a combined heat and power plant: heat vs electricity 15th

Economic optimization of a combined heat and power plant: he...

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15th International Symposium on District Heating and Cooling (DHC)

作者： Marty, Fabien Serra, Sylvain Sochard, Sabine Reneaume, Jean -Michel Univ Pau & Pays Adour Lab Therm Energet & Proc Ipra EA1932 F-64000 Pau France

This contribution presents the economical optimization of the parallel reparation between electric and heat production for geothermal application. The 350 m(3)/h flow of geothermal fluid, assimilated to liquid water at 185 degrees C, is then separated in two streams. Its reinjection temperature is fixed at 70 degrees C. An Organic Rankine Cycle (ORC) system is used to convert a part of geothermal energy into electricity. The refrigerant chosen is the R245fa. The different components of the ORC are sized in order to calculate the installation cost that depends on one characteristic dimension of each item (exchange surface for heat exchangers and power for the turbine and pumps). The operating cost is proportional to the installation cost. In this contribution, since we do not consider the detailed structural optimization of the District Heating Network (DHN), its investment cost is proportional to the supplied heat. The selling price of the electrical net power is a function of the recovered heat by the network. A mixed integer non-linear programming (MINLP) optimization is performed using the GAMS software. The problem is solved in order to determine the maximal profit of the global system. Results show that it is preferable to produce electricity alone but this is dependent on the choice of the price of sale of heat by the owner. The sell price from which it is more profitable to produce and to sell the heat is determined for each case. The optimization for each case shows that it is not easy to predict the final results and it justifies the use of optimization. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Combined heat and power District heating network mixed integer non-linear programming Optimization Organic rankine cycle

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Day-ahead Energy Management of a Microgrid with Battery Energy Storage Integration

Day-ahead Energy Management of a Microgrid with Battery Ener...

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IEEE Innovative Smart Grid Technologies - Asia

作者： Dhanapala Prudhviraj P. B. S. Kiran Naran M. Pindoriya Electrical Engineering Indian Institute of Technology Gandhinagar

ISBN: (纸本)9781538642924

Microgrids are growing at a right pace due to their advantages towards the economical environmental sustainability. Effective operation and management of microgrids can significantly help both the microgrid operator and customers to get economic and technical benefits. In addition to distributed generation (DG) and battery energy storage system (BESS), a microgrid can effectively utilize demand response (DR) strategy to better manage the energy and improve the performance of the network. In this paper, a mathematical formulation for day-ahead energy management of a microgrid is developed. The day-ahead scheduling of resources have been done with the available information of DG, load demand and electricity price. The simulation case studies with two DR schemes have been carried out for four bus test system and results are presented to compare the DR schemes.

关键词： Battery energy storage systems Distributed generation Demand response Microgrid mixed integer non-linear programming

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Economic optimization of a combined heat and power plant: heat vs electricity

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Energy Procedia 2017年 116卷 138-151页

作者： Fabien Marty Sylvain Serra Sabine Sochard Jean-Michel Reneaume UNIV PAU & PAYS ADOUR LABORATOIRE DE THERMIQUE ENERGETIQUE ET PROCEDES-IPRA EA1932 64000 PAU FRANCE

This contribution presents the economical optimization of the parallel repartition between electric and heat production for geothermal application. The 350 m 3 /h flow of geothermal fluid, assimilated to liquid water at 185 °C , is then separated in two streams. Its reinjection temperature is fixed at 70 °C . An Organic Rankine Cycle (ORC) system is used to convert a part of geothermal energy into electricity. The refrigerant chosen is the R245fa. The different components of the ORC are sized in order to calculate the installation cost that depends on one characteristic dimension of each item (exchange surface for heat exchangers and power for the turbine and pumps). The operating cost is proportional to the installation cost. In this contribution, since we do not consider the detailed structural optimization of the District Heating Network (DHN), its investment cost is proportional to the supplied heat. The selling price of the electrical net power is a function of the recovered heat by the network. A mixed integer non-linear programming (MINLP) optimization is performed using the GAMS ® software. The problem is solved in order to determine the maximal profit of the global system. Results show that it is preferable to produce electricity alone but this is dependent on the choice of the price of sale of heat by the owner. The sell price from which it is more profitable to produce and to sell the heat is determined for each case. The optimization for each case shows that it is not easy to predict the final results and it justifies the use of optimization.

关键词： Combined heat power District heating network mixed integer non-linear programming Optimization Organic rankine cycle

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Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy

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APPLIED ENERGY 2016年 164卷 590-606页

作者： Nosratabadi, Seyyed Mostafa Hooshmand, Rahmat-Allah Gholipour, Eskandar Univ Isfahan Dept Elect Engn Fac Engn Esfahan Iran

One of the main classified microgrids in a power system is the industrial microgrid. Due to its behaviors and the heavy loads, its energy management is challengeable. Virtual Power Plant (VPP) can be an important concept in managing such problems in this kind of grids. Here, a transmission power system is considered as a Regional Electric Company (REC) and the VPPs comprising Distributed Generation (DG) units and Demand Response Loads (DRLs) are determined in this system. This paper focuses on Industrial VPP (IVPP) and its management. An IVPP can be determined as a management unit comprising generations and loads in an industrial microgrid. Since the scheduling procedure for these units is very important for their participation in a short-term electric market, a stochastic formulation is proposed for power scheduling in VPPs especially in IVPPs in this paper. By introducing the DRL programs and using the proposed modeling, the operator can select the best DRL program for each VPP in a scheduling procedure. In this regard, a suitable approach is presented to determine the proposed formulation and its solution in a mixed integer non-linear programming (MINLP). To validate the performance of the proposed method, the IEEE Reliability Test System (IEEE-RTS) is considered to apply the method on it, while some challenging aspects are presented. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Virtual power plant Stochastic scheduling Industrial microgrid Demand response load mixed integer non-linear programming

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