Designing stable embedded control systems presents challenges when limited communication and computation resources are involved. This document provides an approach to codesign a scheduler and a resilient controller fo...
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Designing stable embedded control systems presents challenges when limited communication and computation resources are involved. This document provides an approach to codesign a scheduler and a resilient controller for a given set of linear mixed-critical embedded control systems sharing a number of computation resources. Precisely, we are interested in optimizing control inputs and resource allocation in order to ensure control performance, resilience, and faster feedback control response for more critical tasks. The approach is based on a formulation of the problem in the form of a mixed-integer MPC problem. The latter is solved online and provide controllers with new updates, while the latest previous inputs are held constant otherwise. We demonstrate the validity of our approach on an illustrative example.
作者:
Mallach, SvenUniv Bonn
High Performance Comp & Analyt Lab Friedrich Hirzebruch Allee 8 D-53115 Bonn Germany
We present and compare novel binary programs for linear ordering problems that involve the notion of asymmetric betweenness and expose relations to the quadratic linear ordering problem and its linearization. While tw...
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We present and compare novel binary programs for linear ordering problems that involve the notion of asymmetric betweenness and expose relations to the quadratic linear ordering problem and its linearization. While two of the binary programs prove particularly superior from a computational point of view when many or all betweenness relations shall be modeled, the others arise as natural formulations that resemble important theoretical correspondences and provide a compact alternative for sparse problem instances. A reasoning for the strengths and weaknesses of the different formulations is derived by means of polyhedral considerations with respect to their continuous relaxations.& COPY;2023 The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).
Synthesis of sustainable processing pathway is an important initial step in deciding investments in carbon capture, utilization, and sequestration (CCUS). For a best decision, it is necessary to analyze a very large n...
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Synthesis of sustainable processing pathway is an important initial step in deciding investments in carbon capture, utilization, and sequestration (CCUS). For a best decision, it is necessary to analyze a very large number of potential processing pathways at once in terms of their economics and net carbon emission. Such analysis may also reveal which parts of a pathway incur significant portions of the costs and carbon emissions, suggesting hotspots for improvement. Frameworks used should also be flexible enough to accommodate varying feed conditions and market/emission data as they tend to vary according to the sources of CO 2 and geographical locations. The superstructure method along with a state-task network (STN) representation offers such flexibility in analyzing a CCUS system. This work proposes to use a STN representation of a process in a superstructure composed of feeds, processes, and products, to represent and optimize among various options of CCUS pathways through mathematical programming. A case study is conducted to illustrate the utility of the STN representation in the CCUS superstructure optimization.
Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their...
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Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their immediate impact on the system's preparedness. However, the unavailability of the relocated vehicles is often ignored during the relocation, thus the system's preparedness is overestimated. This paper presents a novel mixed-integer programming (MIP) model developed for the relocation and deployment of emergency/fire vehicles. The proposed model incorporates the unavailability element, and estimates system preparedness for future incidents more accurately than current models. To confirm the efficiency of the proposed approach, the required simulations were conducted in Mashhad, Iran. The results demonstrated the ability of the proposed model to improve the performance of the fire department in several performance metrics. We also provide sensitivity analysis over the critical parameters to demonstrate the robustness of the model.
This work proposes a real-time implementable control strategy to optimize vehicle speed and transmission gear position simultaneously for connected and autonomous vehicles (CAVs). Co-optimization of vehicle speed and ...
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This work proposes a real-time implementable control strategy to optimize vehicle speed and transmission gear position simultaneously for connected and autonomous vehicles (CAVs). Co-optimization of vehicle speed and transmission gear position has the advantage to maximize the fuel benefits. Drivability is considered during the optimization to satisfy the acceleration requirement and avoid shift busyness. The target vehicle's speed and gear position are controlled intelligently using predicted future traffic conditions based on information enabled by connectivity. The optimal control problem is a hybrid one with both continuous (vehicle speed) and discrete (gear position) control inputs. The problem is formulated and simplified to a mixed-integer programming problem with a convex quadratic objective function and mixed-integer linear constraints. The optimal control solutions are obtained in real time using an efficient numerical solver in the model predictive control (MPC) fashion. Future traffic conditions are anticipated using a traffic prediction method based on a traffic flow model. The traffic prediction method can be applied to scenarios where both connected vehicles and nonconnected vehicles share the road. As a case study, a vehicle platooning scenario on an urban road is evaluated in both simulation and experiment. The target vehicle is at the end of the vehicle platoon and follows the preceding vehicle. The average computational time of the optimization is 0.44 s. By co-optimizing vehicle speed and gear position, the target vehicle can achieve 10.6% fuel benefits compared with the immediate preceding vehicle and 8.9% energy benefits compared with a human-driven vehicle (driven by VISSIM's car-following model). The proposed control strategy can be potentially extended to various CAV applications and traffic scenarios as well.
In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote...
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ISBN:
(纸本)9781728195056
In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high. In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.
We propose a sampling-based approach to learn Lyapunov functions for a class of discrete-time autonomous hybrid systems that admit a mixed-integer representation. Such systems include autonomous piecewise affine syste...
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ISBN:
(纸本)9781450383394
We propose a sampling-based approach to learn Lyapunov functions for a class of discrete-time autonomous hybrid systems that admit a mixed-integer representation. Such systems include autonomous piecewise affine systems, closed-loop dynamics of linear systems with model predictive controllers, piecewise affine/linear complementarity/mixed-logical dynamical systems in feedback with a ReLU neural network controller, etc. The proposed method comprises an alternation between a learner and a verifier to search for a Lyapunov function from a family of parameterized Lyapunov function candidates. In each iteration, the learner uses a collection of state samples to select a Lyapunov function candidate through a convex program in the parameter space. The verifier then solves a nonconvex mixed-integer quadratic program in the state space to either validate the proposed Lyapunov function candidate or reject it with a counterexample, i.e., a state where the Lyapunov condition fails. This counterexample is then added to the sample set of the learner to refine the set of Lyapunov function candidates in the next iteration. By designing the learner and the verifier according to the analytic center cutting-plane method from convex optimization, we show that when the set of Lyapunov functions is full-dimensional in the parameter space, our method finds a Lyapunov function in a finite number of steps. We demonstrate our stability analysis method on closed-loop MPC dynamical systems and a ReLU neural network controlled PWA system.
Collaborative delivery employing drones in last-mile delivery has been an extensively studied topic in recent years. In this paper, it is studied Truck-Drone Delivery Problems (TDDPs) in which a traditional delivery t...
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Collaborative delivery employing drones in last-mile delivery has been an extensively studied topic in recent years. In this paper, it is studied Truck-Drone Delivery Problems (TDDPs) in which a traditional delivery truck is gathered with a drone to cut delivery times and costs. The vehicles work together in a hybrid operation involving one drone launching from a larger vehicle that operates as a mobile depot and a recharging platform. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel mixedintegerprogramming (MIP) formulation and a heuristic approach to address the problem. The proposed MIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.
Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy -efficient train control, which can reduce operating costs and contribute to a reduction in CO2 emissions. This article pr...
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Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy -efficient train control, which can reduce operating costs and contribute to a reduction in CO2 emissions. This article proposes a bi-objective matheuristic to address the EETT problem for a railway network. To our knowledge, this article is the first to suggest using historical data from train operation to model the actual energy consumption, reflecting the different driving behaviours. The matheuristic employs a genetic algorithm (GA) based on NSGA-II. The GA uses a warm-start method to generate the initial population based on a mixed-integer program. A greedy first-come-first-served fail-fast repair heuristic is used to ensure feasibility throughout the evolution of the population. The objectives taken into account are energy consumption and passenger travel time. The matheuristic was applied to a real-world case from a large North European train operating company. The considered network consists of 107 stations and junctions, and 18 periodic timetables for 9 train lines. Our results show that for an entire network, a reduction up to 3.3% in energy consumption and 4.64% in passenger travel time can be achieved. The results are computed in less than a minute, making the approach suitable for integration with a decision support tool.
The Feasibility Pump (FP) is one of the best-known primal heuristics for mixed-integer programming (MIP): more than 15 papers suggested various modifications of all of its steps. So far, no variant considered informat...
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The Feasibility Pump (FP) is one of the best-known primal heuristics for mixed-integer programming (MIP): more than 15 papers suggested various modifications of all of its steps. So far, no variant considered information across multiple iterations, but all instead maintained the principle to optimize towards a single reference integer point. In this paper, we evaluate the usage of multiple reference vectors in all stages of the FP algorithm. In particular, we use LP-feasible vectors obtained during the main loop to tighten the variable domains before entering the computationally expensive enumeration stage, a procedure we refer to as mRENS. Moreover, we consider multiple integer reference vectors to explore further optimizing directions and introduce alternative objective scaling terms to balance the contributions of the distance functions and the original MIP objective. Our computational experiments demonstrate that the new method can improve performance on general MIP test sets. In detail, our modifications provide a 29.3% solution quality improvement and 4.0% running time improvement in an embedded setting, needing 16.0% fewer iterations over a large test set of MIP instances. In addition, the method's success rate increases considerably within the first few iterations. In a standalone setting, we also observe a moderate performance improvement, which makes our version of FP suitable for the two main use-cases of the algorithm.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). This is an open access article under the CC BY-NC-ND license (http:// creativecommons .org /licenses /by -nc -nd /4 .0/).
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