The manufacturing of different types of cylindrical parts requires loading of parts into baskets for heat treatment operation. This loading process is complex and involves issues relating to geometry, and heterogeneit...
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ISBN:
(纸本)9781479964109
The manufacturing of different types of cylindrical parts requires loading of parts into baskets for heat treatment operation. This loading process is complex and involves issues relating to geometry, and heterogeneity in the parts and in their processing requirements. The parts loaded for heat treatment often do not utilize the available capacity adequately because layer-loading is accomplished by operator ingenuity. Productivity in heat treatment operation can be increased by improving utilization, which is determined by the loading process. This paper describes the development mathematical model using mixedintegernonlinear formulation for loading of cylindrical parts into baskets. The mathematical modeling considers the exact location of parts to be loaded on the layers with the primary objective of minimizing unutilized volume of the baskets.
In traditional Chinese railway operations, train timetable and unit assignment are usually separated from pricing and seat allocation, resulting in a decrease in revenue and an increase in operating costs. In this pap...
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In traditional Chinese railway operations, train timetable and unit assignment are usually separated from pricing and seat allocation, resulting in a decrease in revenue and an increase in operating costs. In this paper, we study the integrated problem of train timetable, unit assignment, pricing, and seat allocation, where passenger choice behavior is modeled by the multinomial logit (MNL) model. This integration enables us to explicitly capture supply-demand interactions. The problem is formulated as a mixed integer nonlinear programming model with the objective of maximizing total profit. By converting the MNL model into its equivalent convex form, we recast the model as a tractable reformulation, allowing the use of a general-purpose solver to solve practical-sized instances. Based on real data from high-speed railway lines in China, three sets of case studies are conducted to validate the effectiveness and efficiency of the proposed approach.
This work focuses on heat exchanger networks (HENs) synthesis (HENS) considering the optimal locations of multiple utilities. Based on an extended stage-wise superstructure where available heaters and coolers are plac...
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This work focuses on heat exchanger networks (HENs) synthesis (HENS) considering the optimal locations of multiple utilities. Based on an extended stage-wise superstructure where available heaters and coolers are placed at all stages, HENS is modeled as a computationally-hard mixed integer nonlinear programming (MINLP) problem. To obtain high-quality solutions, we propose a new hybrid algorithm framework that combines deterministic algorithm (commercial solver) and genetic algorithm (GA) without the use of penalty functions. In the outer level of the framework, GA is employed to optimize the integer variables which represent the existences of matches between process streams as well as the available heaters and coolers at intermediate stages. In the inner level, a reduced-size MINLP model is built to minimize the total annualized costs (TACs) of HENs generated in the outer level. We also propose three new sets to exclude infeasible stream matches, thereby the HENs generated in the outer level are all feasible and our GA does not need any penalty terms. Four literature examples are tested and optimal solutions with lower TACs are obtained within acceptable computing time compared to solutions reported in literature.
In the high-speed railway system, trains' original timetable is often disturbed by some emergencies including geological disasters and equipment failures, which brings great influence to passengers. This paper pro...
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In the high-speed railway system, trains' original timetable is often disturbed by some emergencies including geological disasters and equipment failures, which brings great influence to passengers. This paper proposes a real-time high-speed train dispatching model in case of segment blockages, where a railway network is considered. The model includes the following two parts. First, if the trains are not cancelled or decelerated after a blockage occurs, the scope of the affected trains and stations is roughly estimated via the prediction of delay propagation model. Second, with the overall delay as the objective function, this paper constructs a mixed integer nonlinear programming (MINLP) model by considering the following three adjustment strategies: cancellation, delayed departure and deceleration, where the safe headway of the train operation is guaranteed by the moving blocking principle. Furthermore, to reduce the computation complexity, the solution of the model is only considered within the scope obtained in the first stage. The model is verified by using a small railway network with Nanjing as the hub station, which shows that the model is useful for reducing the effect of a disruption on original timetable, especially in comparison with the First Scheduled First Served (FSFS) rule used in practice.
In this paper, mixed integer nonlinear programming (MINLP) optimization algorithm integrated with kriging surrogate-model is newly formulated to optimize the dispersion characteristics of photonic crystal fibers (PCFs...
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In this paper, mixed integer nonlinear programming (MINLP) optimization algorithm integrated with kriging surrogate-model is newly formulated to optimize the dispersion characteristics of photonic crystal fibers (PCFs). The MINLP is linked with full vectorial finite difference method (FVFDM) to optimize the modal properties of the PCFs. Through the optimization process, the design parameters can take real and/or integer values. The integer values can be used to selectively fill the PCF air holes to control its dispersion characteristics. However, the other optimization techniques deal with real design parameters where the PCF can be optimized using none or predefined infiltrated air holes. The MINLP algorithm is used to obtain an ultra-flat zero dispersion over a broadband of wavelength range from 1.25 to 1.6 mu m using silica PCF selectively infiltrated with Ethanol material. To show the superiority of the proposed algorithm, nematic liquid crystal selectively infiltrated PCFs are also designed with high negative flat dispersion over wide range of wavelengths from 1.25 to 1.6 mu m for the quasi transverse magnetic (TM) and the quasi transverse electric (TE) modes. Such designs have negative flat dispersions of - 163 +/- 0.9 and - 170 +/- 1.2 ps/Km nm, respectively over the studied wavelength range. Therefore, the MINLP algorithms could be used efficiently for the design and optimization of selectively filled photonic devices.
This paper proposes a mechanism to fine-tune convex approximations of probabilistic reachable sets (PRS) of uncertain dynamic systems. We consider the case of unbounded uncertainties, for which it may be impossible to...
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This paper proposes a mechanism to fine-tune convex approximations of probabilistic reachable sets (PRS) of uncertain dynamic systems. We consider the case of unbounded uncertainties, for which it may be impossible to find a bounded reachable set of the system. Instead, we turn to find a PRS that bounds system states with high confidence. Our data-driven approach builds on a kernel density estimator (KDE) accelerated by a fast Fourier transform (FFT), which is customized to model the uncertainties and obtain the PRS efficiently. However, the non-convex shape of the PRS can make it impractical for subsequent optimal designs. Motivated by this, we formulate a mixed integer nonlinear programming (MINLP) problem whose solution result is an optimal n sided convex polygon that approximates the PRS. Leveraging this formulation, we propose a heuristic algorithm to find this convex set efficiently while ensuring accuracy. The algorithm is tested on comprehensive case studies that demonstrate its near-optimality, accuracy, efficiency, and robustness. The benefits of this work pave the way for promising applications to safety-critical, real-time motion planning of uncertain dynamic systems.
作者:
Dai, QikunLiu, JunGuo, HongyanChen, HongCao, DongpuJilin Univ
Coll Commun Engn Natl Key Lab Automot Chassis Integrat & Bion Campus NanLing Changchun 130025 Peoples R China Jilin Univ
Coll Automot Engn Campus NanLing Changchun 130025 Peoples R China Jilin Univ
Coll Commun Engn Campus NanLing Changchun 130025 Peoples R China Tongji Univ
Coll Elect & Informat Engn Shanghai 201804 Peoples R China Univ Waterloo
Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada
When a vehicle faces an imminent collision, it becomes imperative for intelligent vehicles to make emergency collision avoidance decisions in order to mitigate traffic accidents and reduce injuries. To address collisi...
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When a vehicle faces an imminent collision, it becomes imperative for intelligent vehicles to make emergency collision avoidance decisions in order to mitigate traffic accidents and reduce injuries. To address collision avoidance in emergency scenarios, this study proposes a model predictive decision-making (MPDM) approach that incorporates the consideration of lane-changing time. First, a simplified integrated longitudinal and lateral decision-making model is established, and its accuracy is validated through comparison with real vehicle data. Second, a mixedintegernonlinear MPDM is designed to optimize emergency collision avoidance decisions. Within this framework, the minimum lane-changing time for intelligent vehicles is analytically derived based on vehicle dynamics, taking into account varying speeds and adhesion coefficients. Third, by reducing the dimensionality of the lane-changing time optimization variables, an equivalent suboptimization problem is introduced, which consequently diminishes the computational complexity of solving the optimization problem. Finally, a comparative analysis was performed between the MPDM method and several alternative approaches, employing Simulink-SCANeR cosimulation. Furthermore, the MPDM method was validated on a real vehicle. The results obtained highlight a significant enhancement in the safety and stability of collision avoidance due to the MPDM.
Edge computing, a new wireless communications technology, can provide abundant resources for computing and storage. Recently, it is a challenging problem to jointly consider the offloading and resource allocation of g...
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Edge computing, a new wireless communications technology, can provide abundant resources for computing and storage. Recently, it is a challenging problem to jointly consider the offloading and resource allocation of generally dependent tasks in multi Unmanned Aerial Vehicles (UAVs) multi Terminal Devices (TDs) edge computing system. In this paper, the joint problem of offloading decisions, collaborative decisions and resource allocation of general dependent tasks is investigated, considering and highlighting the cooperation between TDs. The corresponding optimization problem, which is a mixed integer nonlinear programming problem, is formulated. To make this problem solved, an iterative method based on Deep Reinforcement Learning (DRL) and Convex optimization is proposed to decompose the original problem into two subproblems. Given the resource allocation scheme, the Deep Q network (DQN) algorithm is employed to solve the offloading and collaborative decisions of all tasks. Then, given the offloading decision and cooperation decision of all tasks, the convex optimization algorithm is used to solve the optimal resource allocation scheme in the UAV enabled edge computing system. Two subproblems iterative alternately. The simulation results demonstrated that our proposed method can significantly reduce the system energy consumption compared to other schemes.
Efficient filling strategies for hydrogen fuel cell vehicles are critical for hydrogen utilization efficiency at hydrogen fuelling stations. A novel event-triggered model predictive control framework is proposed in th...
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Efficient filling strategies for hydrogen fuel cell vehicles are critical for hydrogen utilization efficiency at hydrogen fuelling stations. A novel event-triggered model predictive control framework is proposed in this article for the filling process of a hydrogen fuelling station, which involves multiple compressors, cascade storage tanks, and multiple dispensers. The filling process is formulated as a mixed-integernonlinearprogramming (MINLP) problem with the objective of minimizing the vehicle filling times and maximizing the hydrogen utilization efficiency. A solution approach that combines the mixed-integer linear programming and genetic algorithm is designed for solving the resulting MINLP problem. In addition, an event-triggered mechanism is proposed to increase the computational efficiency and to update the control inputs only when needed. Different sets of computational experiments are carried out to demonstrate the effectiveness of the mathematical formulation and the solution approach.
Although the optimal power flow (OPF) problem has been extensively studied, solving realistic OPF models that accurately represent the operating behavior of power system components remains challenging. This paper prop...
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Although the optimal power flow (OPF) problem has been extensively studied, solving realistic OPF models that accurately represent the operating behavior of power system components remains challenging. This paper proposes a novel model for the AC OPF problem, aiming to minimize the fuel costs of thermal units while taking into account valve -point loading effects (VPLE), prohibited operation zones (POZ), multiple fuel options (MFO), and operational rules associated with the discrete tap ratios of on -load tap changer (OLTC) transformers and with the discrete shunt susceptances of capacitor/reactor banks. These rules are represented using complementarity constraints. We propose a solution approach that integrates several strategies to address the non -smooth features of the objective function related to VPLE, the disjoint constraints and functions tied to POZ and MFO, the discrete characteristics of the reactive control variables, and the complementarity constraints governing operational rules linked to voltage control devices such as OLTC transformers and capacitor/reactor banks. The resulting optimization problem is designed to be compatible with commercial solver packages. Numerical tests on the IEEE 30, 118, and 300 -bus systems aim to examine the cumulative impact of these operational factors on the optimal solution. The solution strategy proposed has demonstrated its effectiveness in solving the proposed OPF problem within reasonable computation times.
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