Thermal unit commitment (UC) is a nonlinear combinatorial optimization problem that minimizes total operating costs while considering system load balance, on/off restrictions and other constraints. Successfully solvin...
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Thermal unit commitment (UC) is a nonlinear combinatorial optimization problem that minimizes total operating costs while considering system load balance, on/off restrictions and other constraints. Successfully solving the thermal UC problem contributes to a more reliable power system and reduces thermal costs. This paper presents an exact mixed-integer quadratic programming (EMIQP) method for large-scale thermal UC problems. EMIQP revolutionizes the landscape by seamlessly translating the intricate nonlinear combinatorial optimization problem of UC into an exact mixed-integerquadratic formulation. This approach also elegantly reimagines on/off constraints as mixed-integer linear equations, employing both the sum and respective approaches. Our case studies unequivocally demonstrate the exceptional prowess of the EMIQP method, consistently securing the global optimum. Moreover, the mathematical-based EMIQP method produces identical results at each run, which is extremely important for UC in the real world.
Ensuring driving operational safety in emergency scenarios is paramount for autonomous vehicles to prevent accidents, particularly when vehicle motion completely depends on autonomous systems. Numerous factors must be...
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Ensuring driving operational safety in emergency scenarios is paramount for autonomous vehicles to prevent accidents, particularly when vehicle motion completely depends on autonomous systems. Numerous factors must be evaluated when designing emergency collision avoidance strategies for critical situations, such as trajectory feasibility, vehicle motion stability, and driver comfort. Therefore, this study proposes a framework for emergency operation that uses collision-free area calculations to inform maneuver decisions and facilitate collision avoidance trajectory planning, preventing vehicle collisions. Incase of danger, the emergency maneuver decision module evaluates the safety level and selects safety terminal state by considering a pre-specified cluster of candidate maneuvers before generating trajectories. This process avoids infeasible trajectories and selects maneuvers for greater driver comfort when available. Subsequently, the dynamic trajectory planning module converts the collision-free area into mixed-integer constraints, utilizing time-varying Nonlinear Model Predictive Control (NMPC) for trajectory planning and ensuring vehicle motion stability by integrating dynamic and collision-free constraints throughout the motion planning process. Eventually, simulations and field testing validate the framework's effectiveness, mitigating collisions in emergency scenarios with prompt and safe operations. The framework is designed to function autonomously, independent of the intelligent driving system, engaging only during risk events and restoring control to the driver or the intelligent system after the event.
Traffic situations with interacting participants pose difficulties for today's autonomous vehicles to interpret situations and eventually achieve their own mission goal. Interactive planning approaches are promisi...
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Traffic situations with interacting participants pose difficulties for today's autonomous vehicles to interpret situations and eventually achieve their own mission goal. Interactive planning approaches are promising solutions for solving such situations. However, most approaches are only assessed in simulation, as researchers lack the resources to operate an autonomous vehicle. Likewise, open-source stacks for autonomous driving, such as Apollo, provide competitive and resource-efficient state-of-the-art planning algorithms. However, promising planning concepts from research are usually not included within a reasonable time, possibly due to resource restrictions or technical limitations. Without evaluating these novel algorithms in reality, the benefits and shortcomings of proposed approaches cannot be thoroughly assessed. This work aims to contribute methodology and implementation to integrate a novel mixed-integer optimization-based planning algorithm in Apollo's planning component and assess its performance and real-time capability in theory and practice. It discusses the necessary modifications of Apollo for deployment on a different vehicle and presents three real-world driving experiments on a public road alongside a detailed experience report. The driving experiments show a smooth trajectory tracking performance operating robustly under varying perception data quality and the real-time capability of the closed-loop system.
The physician assignment process consists of coverage of shifts and duties allocated to physicians in a planning period, taking into account work regulations, individual preferences, and organizational rules, which mo...
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The physician assignment process consists of coverage of shifts and duties allocated to physicians in a planning period, taking into account work regulations, individual preferences, and organizational rules, which mostly conflict with each other. In this work, we propose a reformulated mixed-integerprogramming model based on the literature to tackle fairness in physician scheduling in Emergency Rooms (ERs). In particular, we propose two mixed-integer quadratic programming formulations that consider quadratic costs and two models with linear costs. Our approaches provide balanced schedules concerning target hours and weekends in terms of fairness. Our models also provide a high degree of demand coverage, providing decision-makers a significant advantage.
This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem,which can be formulated as a mixed-integer quadratic programming problem and pr...
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This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem,which can be formulated as a mixed-integer quadratic programming problem and proven to be *** theoretical bounds for the absolute errors and relative errors of the approximate solutions generated by the proposed algorithm are *** results support the effectiveness and efficiency of the proposed algorithm for solving large-scale problems.
Buttazzo et al.'s elastic scheduling model allows task utilizations to be "compressed" to ensure schedulability atop limited resources. Each task is assigned a range of acceptable utilizations and an &qu...
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ISBN:
(纸本)9798331540265;9798331540272
Buttazzo et al.'s elastic scheduling model allows task utilizations to be "compressed" to ensure schedulability atop limited resources. Each task is assigned a range of acceptable utilizations and an "elastic constant" representing the relative adaptability of its utilization. In this paper, we consider federated scheduling, under which each high-utilization parallel task is assigned dedicated processor cores. We propose a new model of elastic workload compression for parallel DAG tasks that assigns each subtask its own elastic constant and continuous range of acceptable workloads. We show that the problem can be solved offline as a mixed-integerquadratic program, or online using a pseudo-polynomial dynamic programming algorithm. We also consider joint core allocation and compression of low-utilization sequential tasks and present a mixed-integer linear program for optimal elastic compression of tasks under partitioned EDF scheduling. We show empirical improvements in schedulability over the prior work and present a case study for the Fast Integrated Mobility Spectrometer (FIMS).
The increasing demand for air travel combined with uncertainties has put additional strain on airport infrastructure and ground handling resources. To improve the efficiency of airport operations, in this paper, We fi...
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ISBN:
(纸本)9798350399462
The increasing demand for air travel combined with uncertainties has put additional strain on airport infrastructure and ground handling resources. To improve the efficiency of airport operations, in this paper, We firstly perform an in-depth analysis of the A-SMGCS dataset for Singapore Changi Airport, and then propose taxiing routing solutions for both landing and departure aircraft with involved departure uncertainty, formulating the problem as a mixed-integer quadratic programming (MIQP) problem. The proposed model considers the waypoint-based conflict checking, as well as incorporates anti-overtaking constraints, and head-on constraints exclusive for bidirected graph, aiming to minimize the taxiing time as well as the gap with the scheduled in-block time for landing aircraft and scheduled take-off time for departure aircraft. The presence of departure uncertainty prompts us to build a stochastic model, where constraints with stochastic variables are converted into corresponding chance constraints under designated confidence levels. Incorporating stochastic factors enhances the resilience and dependability of our solution. To evaluate the efficiency of our proposed method, we have elaborately investigated the computational complexity under various test scales, and analyzed how changes in uncertainty and confidence levels impact routing and scheduling solutions on a simplified Singapore Changi Airport network, which could provide significant reference for other work.
The control system for energy-efficient train operation with the inclusion of a detailed train motion model and train traction system energy efficiency is presented in the paper. A piecewise affine train model is cons...
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The control system for energy-efficient train operation with the inclusion of a detailed train motion model and train traction system energy efficiency is presented in the paper. A piecewise affine train model is constructed with the parameters obtained for the electromotive train of an industrial manufacturer. The model encompasses intrinsic features of the train system such as linearized resistance force, a set of traction and braking force physical limitations and passengers comfort constraints. The resulting quadratic optimization problem is solved parametrically through dynamic programming giving the off-line precomputed optimal control law that is a function of train speed and traversed path. The on-line computed traction force profile is then tuned with respect to the traction system energy efficiency. The developed control system is evaluated on a detailed real case study scenario put together with a railway operator and the train manufacturer. The presented results show the possibility of significant energy consumption reductions achieved by energy-efficient train control.
The possible services of batteries are expanding within the scope of transforming electricity networks. In this study, the potential of batteries to increase transformer efficiency is revealed. It is aimed to maximize...
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ISBN:
(纸本)9781665469258
The possible services of batteries are expanding within the scope of transforming electricity networks. In this study, the potential of batteries to increase transformer efficiency is revealed. It is aimed to maximize the transformer efficiency depending on the demand and the installed power of the transformer. Based on this aim, optimization algorithms that can decide on the optimal battery size are developed. The daily charge-discharge cycle of the battery within the scope of transformer efficiency is explained. According to the pilot site studies carried out, up to 22% efficiency increase potential is discovered in a transformer with the battery. Considering this potential, the use of batteries to support transformer efficiency is recommended as a new local service concept.
As the electric vehicle charging demand for highway travel increases, the charging operation is gradually becoming a significant challenge for highway charging station operators. Distributed renewable power generators...
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ISBN:
(纸本)9781665409186
As the electric vehicle charging demand for highway travel increases, the charging operation is gradually becoming a significant challenge for highway charging station operators. Distributed renewable power generators could satisfy the charging demand by providing clean energy for charging stations. In this paper, a charging price-setting scheme for highway charging station operators is formulated. We model this scenario as a mixed-integer quadratic programming problem, aiming to maximize profits while achieving spatial and temporal shifting of EVs charging loads to consume renewable energy (e.g., photovoltaic and wind power). The numerical case study shows that highway charging station operators adopting the proposed method can effectively promote renewable energy consumption and boost their profits.
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