Plant layout is one of the most important factors for reducing plant construction costs. In the research field of plant layouts, the main purpose is to minimize the total length and cost of pipelines between equipment...
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Plant layout is one of the most important factors for reducing plant construction costs. In the research field of plant layouts, the main purpose is to minimize the total length and cost of pipelines between equipment by satisfying various constraints, such as safety regulations and passages for operators. However, previous research overlooks the consideration of operating conditions. Additionally, sufficient safety distances between equipment have to be guaranteed to mitigate danger or domino accidents, and maintenance spaces should be considered for on-site repairs or maintenance. Moreover, various multi-floor plants have been constructed. Therefore, an appropriate algorithm for handling these issues urgently needs to be developed. Equations for the mixed integer non-linear programming problem considering various issues are proposed in this study. In these equations, the objective function is the total summation of pipeline and additional energy costs generated by pressure drop and heat transfer. Additionally, predefined safety and maintenance spaces are transformed into inequality constraints. Because it is not always possible to use the derivatives of equations, such as in this study, an original particle swarm optimization technique is employed. Two case studies are illustrated to verify the efficacy of the proposed algorithm.
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 p...
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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 mixedintegernon-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 mixedintegerlinear 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.
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, ...
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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.
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 ...
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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.
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_pa...
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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.
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 ofte...
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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 mixedintegernonlinear 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.
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 ...
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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%.
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 a...
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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.
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 a...
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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.
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 ...
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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.
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