This paper presents a hybrid refinery scheduling system combining mathematical programming model and expert system. mixed-integer linear programming models for crude oil movement between units are merged into the expe...
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This paper presents a hybrid refinery scheduling system combining mathematical programming model and expert system. mixed-integer linear programming models for crude oil movement between units are merged into the expert system that is for qualitative issues concerning crude vessel unloading operations. The target problem ranging from the crude unloading to the crude charging to distillation towers is decomposed into several module problems for efficiency. Compared with existing scheduling approaches for oil movement, the proposed hybrid refinery scheduling system is very effective in dealing with timing decisions involving vessel unloading operations due to the advantages of an expert system. Since the proposed scheduling system can generate solutions so fast, it is expected to play a key role in the real processes.
This paper presents a formulation of security-constrained unit commitment (SCUC) problem based on mixedintegerprogramming (MIP) method with considering prohibited operating zone limits of thermal and hydro units. Th...
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ISBN:
(纸本)9781424420292
This paper presents a formulation of security-constrained unit commitment (SCUC) problem based on mixedintegerprogramming (MIP) method with considering prohibited operating zone limits of thermal and hydro units. The objective of SCUC problem is to obtain minimum system operating cost while maintaining the system security. Thermal and hydro units in power system studies are assumed to possess smooth and convex input-output (IO) curves. Practically, not all the operating zones of generation units are available for load allocation due to some physical operation limits. This may cause to have a generator with nonsmooth IO curve with equality and inequality constraints which may not be solved easily by conventional mathematical methods. In this paper, non-convex characteristic of generator cost function, representing prohibited operating zones is considered in SCUC. Test results with an eight-bus system show the accuracy of the model and formulations.
Risk-averse multi-stage mixed-integer stochastic programming problems form a class of extremely challenging problems since the problem size grows exponentially with the number of stages, they are non-convex due to int...
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Risk-averse multi-stage mixed-integer stochastic programming problems form a class of extremely challenging problems since the problem size grows exponentially with the number of stages, they are non-convex due to integrality restrictions, and their objective functions are nonlinear in general. In this thesis, we first focus on such problems with an objective of dynamic mean conditional value-at-risk. We propose a scenario tree decomposition approach to obtain lower and upper bounds for their optimal values and then use these bounds in an evaluate-and-cut procedure which serves as an exact solution algorithm for such problems with integer first-stage decisions. Later, we consider a risk-averse day-ahead scheduling of electricity generation or unit commitment problem where the objective is a dynamic coherent risk measure. We consider two different versions of the problem: adaptive and non-adaptive. In the adaptive model, the commitment decisions are updated in each stage, whereas in the non-adaptive model, the commitment decisions are fixed in the first-stage. We provide theoretical and empirical analyses on the benefit of using an adaptive multi-stage stochastic model. Finally, we investigate the trade off between the adaptivity of the model and the computational effort to solve it for risk-averse multi-stage production planning problems with an objective of dynamic coherent risk measure. We also conduct computational experiments in order to verify the theoretical findings and discuss the results of these experiments.
Conditional VaR is also called mean excess loss or tail VaR, and CVaR is a coherent measurement of risk. Based on portfolio optimization theory of CVaR put forward by Rockafeller and Uryasev, and combined with Monte C...
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Conditional VaR is also called mean excess loss or tail VaR, and CVaR is a coherent measurement of risk. Based on portfolio optimization theory of CVaR put forward by Rockafeller and Uryasev, and combined with Monte Carlo simulation and branch and bound algorithm, a portfolio optimization model of CVaR is established. The stocks which composed shangzheng 50 index are selected to compose an optimal portfolio under CVaR, mean-variance and VaR measurement. Experiential analysis and comparative experiment have finally shown that the model established in this paper and the method were efficient.
E-mobility represents an important part of the EU's green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector's development it is necessary to i...
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E-mobility represents an important part of the EU's green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector's development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market.
Most of the optimization problems encountered in the real world are discrete type which involves decision variables defined in the discrete search space. Binary optimization problems, integer and mixedinteger program...
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Most of the optimization problems encountered in the real world are discrete type which involves decision variables defined in the discrete search space. Binary optimization problems, integer and mixedintegerprogramming problems are of this category, and they require suitable solution representation and search operators to be solved by nature-inspired algorithms. One of the widely-used and well-known nature-inspired algorithms is Artificial Bee Colony (ABC) that has been originally proposed to solve the problems in the continuous domain, and hence, its standard version employs the search operators to exploit the information of the solution vectors encoded in the continuous domain. To be able to cope with the discrete problems, particularly binary, integer and mixedintegerprogramming problems, which are also a group of numeric optimization problems, various encoding types, search operators and selection operators have been integrated into ABC. In this paper, we review the studies proposing new ABC variants to solve discrete numeric optimization problems. To the best of our knowledge, this will be the first comprehensive survey study on this topic. Therefore, we hope that this study would be beneficial to the readers interested in the use of ABC for the binary, integer and mixedinteger discrete optimization problems. (C) 2021 Elsevier B.V. All rights reserved.
This paper studies a repetitive project scheduling problem considering multiple crews and fixed logic with the objective to minimize the total cost without exceeding a given deadline. Multiple crews means that an acti...
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This paper studies a repetitive project scheduling problem considering multiple crews and fixed logic with the objective to minimize the total cost without exceeding a given deadline. Multiple crews means that an activity can be performed simultaneously in several units by hiring additional crews, whereas fixed logic indicates that for each activity the units assigned to the same crew must be performed by a fixed construction sequence. Existing research works use heuristic methods to solve the problem without formulating an explicit model for further analysis. In this paper, we adopt the mixed-integer programming approach to construct an exact model. To handle large-size problems, an approximate model with reduced number of constraints and variables is further presented. Extensive computational experiments demonstrate that the exact model is capable of finding optimal solutions for medium-size problems in a reasonable amount of time, and the approximate model produces good feasible solutions for large-size problems in an accepted length of time. (C) 2016 American Society of Civil Engineers.
Artificial Neural Networks (ANNs) may suffer from suboptimal training and test performance related issues not only because of the presence of high number of features with low statistical contributions but also due to ...
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Artificial Neural Networks (ANNs) may suffer from suboptimal training and test performance related issues not only because of the presence of high number of features with low statistical contributions but also due to their non-convex nature. This study develops piecewise-linear formulations for the efficient approximation of the non-convex activation and objective functions in artificial neural networks for optimal, global and simultaneous training and feature selection in regression problems. Such formulations include binary variables to account for the existence of the features and piecewise-linear approximations, which in turn, after one exact linearization step, calls for solving a mixed-integer linear programming problem with a global optimum guarantee because of convexity. Suggested formulation is implemented on two industrial case studies. Results show that efficient approximations are obtained through the usage of the method with only a few number of breakpoints. Significant feature space reduction is observed bringing about notable improvement in test accuracy. (c) 2021 Elsevier Ltd. All rights reserved.
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