This study addresses integrated bus maintenance and vehicle scheduling in a day-to-day dynamic operation setting to ensure that regular preventative maintenance and newly identified predictive maintenance needs are ha...
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This study addresses integrated bus maintenance and vehicle scheduling in a day-to-day dynamic operation setting to ensure that regular preventative maintenance and newly identified predictive maintenance needs are handled promptly. The problem is formulated as a mixed-integer linear programming aiming to minimize operational costs and risks over a day-to-day rolling planning horizon. A two-stage decomposition (TSD) method is proposed to solve this challenging problem. In the first stage, bus maintenance is scheduled on a daily basis. The second stage then optimizes specific bus maintenance and vehicle schedules within each day. With such a problem decomposition, the computational complexity is significantly reduced as the within-day problems become independent. Computational experiments conducted on a real-world bus line demonstrate the effectiveness and superiority of the TSD method. Compared to a one-step solution method and an algorithm in a similar existing study, the proposed TSD method consumes far less computation time, while delivering high-quality solutions. Moreover, the TSD solution method can be further greatly accelerated if the within-day problems of the second stage are solved in parallel with different computers. The computational results also highlight the benefits of the proposed two-stage optimization approach in enhancing operational cost-efficiency and bus resource utilization.
This paper is concerned with model predictive control of piecewise affine systems with dead zone constraints based on the mixed logical dynamical modeling approach. A predictive control strategy based on a mixed logic...
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This paper is concerned with model predictive control of piecewise affine systems with dead zone constraints based on the mixed logical dynamical modeling approach. A predictive control strategy based on a mixed logic dynamical model is proposed. The strategy contains two steps: the first step is to transform the piecewise affine systems into mixed logic dynamical models;the second step is to apply a predictive control scheme based on this model. Then, the optimal control sequence can be obtained by solving the constructed mixed-integer quadratic/linear programming problem. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approaches.
Various fuel treatment practices involve removing all or some of the vegetation (fuel) from a landscape to reduce the potential for fires and their severity. Fuel treatments form the first line of defense against larg...
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Various fuel treatment practices involve removing all or some of the vegetation (fuel) from a landscape to reduce the potential for fires and their severity. Fuel treatments form the first line of defense against large-scale wildfires. In this study, we formulate and solve a bilevel integerprogramming model, where the fuel treatment planner (modeled as the leader) determines appropriate locations and types of treatments to minimize expected losses from wildfires. The follower (i.e., the lower-level decision-maker) corresponds to nature, which is adversarial to the leader and designs a wildfire attack (i.e., locations and time periods, where and when, respectively, wildfires occur) to disrupt the leader's objective function, e.g., the total expected area burnt. Both levels in the model involve integrality restrictions for decision variables;hence, we explore the model's difficulty from the computational complexity perspective. Then, we design specialized solution methods for general and some special cases. We perform experiments with semi-synthetic and real-life instances to illustrate the performance of our approaches. We also explore numerically the fundamental differences in the structural properties of solutions arising from bilevel model and its single-level counterpart. These disparities encompass factors like the types of treatments applied and the choice of treated areas. Additionally, we conduct various types of sensitivity analysis on the performance of the obtained policies and illustrate the value of the bilevel solutions.
We address a mixed-integer linear programming model which selects a cost-minimizing set of available technologies with which to design a renewable energy system and prescribe their associated dispatch decisions. Reali...
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We address a mixed-integer linear programming model which selects a cost-minimizing set of available technologies with which to design a renewable energy system and prescribe their associated dispatch decisions. Realistically sized instances of such models pose computational challenges. To this end, we develop a Lagrangian heuristic based on a decomposition methodology which partitions the model into blocks and optimizes these more manageable, smaller subproblems. It also provides a lower bound to assess solution quality. We apply this methodology to the National Renewable Energy Laboratory's Renewable Energy Integration and Optimization (REoptTM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {REopt}<^>{\textrm{TM}}$$\end{document}) model to generate near-optimal solutions to realistic instances containing, on average, approximately 300,000 variables and at least as many constraints, with a mean 30% optimality gap improvement using a five-minute solution time limit, compared to directly solving the original monolith.
The transition to fully electrified public transport is a pivotal step towards sustainable urban mobility. However, the efficient deployment of charging infrastructure poses significant challenges, particularly in opt...
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The transition to fully electrified public transport is a pivotal step towards sustainable urban mobility. However, the efficient deployment of charging infrastructure poses significant challenges, particularly in optimizing charger placement to meet operational and economic constraints. This article introduces a novel strategic planning model designed to calculate the optimal locations for extending an existing charging station network that supports the daily operations of an electric bus fleet. The planning model is a bi-objective mixedinteger-linear program that considers both the operational needs of the fleet operator and the energy pricing schema as imposed by energy management authorities. By accounting for parameters such as charging station installation costs, Time-of-Use tariffs and peak demand charges, the model addresses an integrated planning problem with two objectives: the minimization of the monetary cost required for the operation of the bus fleet and the buses' deadhead times. An implementation in a network using data from Manhattan, New York and a case study in Limassol, Cyprus, demonstrate the model's efficacy in providing actionable insights for urban planners and transport authorities, ensuring cost-effectiveness and reduced environmental impact.
We study distributionally robust chance-constrained programs (DRCCPs) with individual chance constraints under a Wasserstein ambiguity. The DRCCPs treat the risk tolerances associated with the distributionally robust ...
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We study distributionally robust chance-constrained programs (DRCCPs) with individual chance constraints under a Wasserstein ambiguity. The DRCCPs treat the risk tolerances associated with the distributionally robust chance constraints (DRCCs) as decision variables to trade off between the system cost and risk of violations by penalizing the risk tolerances in the objective function. We develop integerprogramming approaches for individual chance constraints with uncertainty either on the right-hand side or on the left-hand side. In particular, we derive mixedintegerprogramming reformulations for the two types of uncertainty to determine the optimal risk tolerance for the chance constraint. Valid inequalities are derived to strengthen the formulations. We test diverse instances of diverse sizes.
Deep neural networks (DNNs) are widely studied in various applications. A DNN consists of layers of neurons that compute affine combinations, apply nonlinear operations, and produce corresponding activations. The rect...
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Deep neural networks (DNNs) are widely studied in various applications. A DNN consists of layers of neurons that compute affine combinations, apply nonlinear operations, and produce corresponding activations. The rectified linear unit (ReLU) is a typical nonlinear operator, outputting the max of its input and zero. In scenarios like max pooling, where multiple input values are involved, a fixed-parameter DNN can be modeled as a mixed-integer program (MIP). This formulation, with continuous variables representing unit outputs and binary variables for ReLU activation, finds applications across diverse domains. This study explores the formulation of trained ReLU neurons as MIP and applied MIP models for training neural networks (NNs). Specifically, it investigates interactions between MIP techniques and various NN architectures, including binary DNNs (employing step activation functions) and binarized DNNs (with weights and activations limited to -1,0,+1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-1,0,+1$$\end{document}). The research focuses on training and evaluating proposed approaches through experiments on handwritten digit classification models. The comparative study assesses the performance of trained ReLU NNs, shedding light on the effectiveness of MIP formulations in enhancing training processes for NNs.
This work is concerned with the problem of motion planning for a group of nonholonomic robots under Visible Light Communication (VLC) connectivity requirements. Recently, multi-robot systems operating with VLC network...
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This work is concerned with the problem of motion planning for a group of nonholonomic robots under Visible Light Communication (VLC) connectivity requirements. Recently, multi-robot systems operating with VLC networks have been demonstrated as an attractive solution for inspection tasks in pipelines and underground facilities. However, there are still no established methodologies to automate their operation. We address this problem with an optimization framework based on mixed-integer Linear programming (MILP) to design a motion planner that safely coordinates the robot team while jointly addressing the nonlinearity of the robots' dynamics, the directed line-of-sight requirement for VLC connections, and the connectivity maintenance of the ensuing directed networks. We propose a novel method to encode the nonlinear dynamics of nonholonomic robots into the MILP formulation and demonstrate through comparative trials that, although the resulting optimization search space is more restricted, the approach still outperforms mixed-integer nonlinear programming in terms of optimization time and solution quality. The efficacy of the proposal is also demonstrated through realistic simulations of the Turtlebot3 robot platform.
We incorporate socially responsible investment goals in the index tracking portfolio problem. We propose a comprehensive decision-making framework to construct an ESG-based portfolio to track the market index while en...
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We incorporate socially responsible investment goals in the index tracking portfolio problem. We propose a comprehensive decision-making framework to construct an ESG-based portfolio to track the market index while ensuring ESG performance at a specific confidence level. To solve the problem, we develop a hybrid method combining genetic algorithms and chance-constrained optimization. Moreover, we estimate a linear portfolio policy to determine the portfolio weights directly using stock-level characteristics. We provide the empirical application of the proposed method using the constituents of the S&P 500 index and quantify the trade-off between the tracking error and the ESG value of the portfolios. The results indicate that imposing ESG criteria at the highest confidence level increases the annualized mean absolute deviation (MAD) by more than 14.5% compared to non-ESG-based portfolios. However, the significance of this trade-off increases with a smaller portfolio size and a shorter investment horizon. Furthermore, the empirical analysis suggests that the size of the firms is more important than their value and momentum in portfolio rebalancing. The proposed system guides investors in their portfolio choice to set targets that best match their ethical as well as monetary goals.
This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and *** proposed solution is based on a nonlinear mod...
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This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and *** proposed solution is based on a nonlinear model predictive control algorithm which determines the optimal switching operations of the distribution *** goal of the control algorithm is to find the optimal radial network topology which minimizes cumulative active power losses and maximizes voltages across the network while simultaneously satisfying all system *** optimization results are validated through multiple simulations(using real power demand data collected for a few characteristic days during winter and summer)which demonstrate the efficiency and usefulness of the developed control algorithm in reducing the grid losses by up to 14%.
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