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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Wibowo, Rony Seto Purnomo, Fiona Yolita Aryani, Ni Ketut Penangsang, Ontoseno Inst Teknol Sepuluh Nopember ITS Surabaya Indonesia

ISBN: (纸本)9781538627082

This paper presents a study about optimization in microgrid with energy storage. The development of technology makes it possible for energy storage utilization as solution for energy management problem due to renewable integration. The ability of energy storage to store and supply power can be used to solve intermittency problem of renewable energy. Furthermore, the possibility of energy storage integration to make profit is researched deeper by considering charging and discharging cost. The DC Optimal Power Flow calculation is done to optimize the system with energy storage using mixed-integer quadratic programming. The output of this research are generation power and energy storage power in each period with minimum generation cost and the effect of different charging and discharging cost towards it.

关键词： battery DDCOPF microgrid mixed-integer quadratic programming charging and discharging cost

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A Numerically Robust mixed-integer quadratic programming Solver for Embedded Hybrid Model Predictive Control

A Numerically Robust Mixed-Integer Quadratic Programming Sol...

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6th International-Federation-of-Automatic-Control (IFAC) Conference on Nonlinear-Model-Predictive-Control (NMPC)

作者： Bemporad, Alberto Naik, Vihangkumar V. IMT Sch Adv Studies Lucca Lucca Italy

The deployment of hybrid model predictive control (MPC) in practical applications requires primarily an efficient and robust on-line mixed-integer quadratic programming (MIQP) solver that runs in real time. In this paper we propose a new algorithm for solving MIQP problems which is particularly tailored to solve small-scale MIQPs, such as those that arise in embedded hybrid MPC applications. The algorithm couples a branch and bound (B&B) scheme with a recently proposed numerically robust quadratic programming (QP) solver based on nonnegative least squares (NNLS) and proximal-point iterations. The resulting MIQP solver supports positive semidefinite Hessian matrices, often appearing in hybrid MPC formulations, and warm starts with respect to both binary and real variables. We show that the speed of execution of our solver is comparable with state-of-the-art commercial solvers, while it is relatively simple to code in an embedded control system. (c) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： mixed-integer quadratic programming quadratic programming active-set methods nonnegative least squares model predictive control hybrid systems

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Using dual relaxations in multiobjective mixed-integer convex quadratic programming

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JOURNAL OF GLOBAL OPTIMIZATION 2024年第1期92卷 159-186页

作者： De Santis, Marianna Eichfelder, Gabriele Patria, Daniele Warnow, Leo Univ Firenze Dipartimento Ingn informaz Via Santa Marta 3 I-50139 Florence Italy Tech Univ Ilmenau Inst Math Po 10 05 65 D-98684 Ilmenau Germany Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest Via Ariosto 25 I-00185 Rome Italy

We present a branch-and-bound method for multiobjective mixed-integer convex quadratic programs that computes a superset of efficient integer assignments and a coverage of the nondominated set. The method relies on outer approximations of the upper image set of continuous relaxations. These outer approximations are obtained addressing the dual formulations of specific subproblems where the values of certain integer variables are fixed. The devised pruning conditions and a tailored preprocessing phase allow a fast enumeration of the nodes. Despite we do not require any boundedness of the feasible set, we are able to prove that the method stops after having explored a finite number of nodes. Numerical experiments on a broad set of instances with two, three, and four objectives are presented.

关键词： Multiobjective optimization Convex quadratic optimization mixed-integer quadratic programming Branch-and-bound algorithm

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mixed-integer quadratic Constrained programming versus quadratic programming Methods for Distribution Network Reconfiguration

Mixed-Integer Quadratic Constrained Programming versus Quadr...

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International Conference on Advanced Electrical Engineering (ICAEE)

作者： Tami, Y. Sebaa, K. Lahdeb, M. Nouri, H. Univ Medea LSEA Medea 26000 Algeria Univ Laghouat LaACoSE Elect PB 37G Laghouat 03000 Algeria Univ West England Dept Engn Design & Math Power Syst Elect & Control Res Grp Bristol BS16 1QY Avon England

ISBN: (纸本)9781728122205

This paper will examine the solution of the minimum loss reconfiguration problem of distribution networks that determine the optimal switches, by means of a mixed-integer quadratically-constrained programming (MIQCP) model. The proposed MIQCP model is a convex formulation, which allows to find the optimal solution using optimization solvers. Our mathematical model considers the typical operating constraints of distribution networks (radial configuration, the maximum currents or the voltage plane).The accuracy of the results and the computational performance of the proposed MIQCP model are evaluated by referring to another MIQP (mixed integer quadratic programming) model already adopted for the problem of interest. We used a real distribution system of 106 nodes to show the effectiveness of the proposed model compared to another MIQP model that was tested on the same network. The mathematical model adopted has been solved using the Cplex optimization software implemented under Matlab. The overall optimality of the guaranteed solution is obtained by the convex relaxation of the continuous decision variables.

关键词： convex optimization quadratically constrained convex optimization mixed-integer quadratic programming power loss Radial distribution networks

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A railcar re-blocking strategy via mixed integer quadratic programming

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年 162卷

作者： Zu, Yue Heydari, Ruhollah Chahar, Kiran Pranoto, Yudi Cheng, Clark Norfolk Southern Corp 650 West Peachtree St Atlanta GA 30308 USA Delta Air Lines 1030 Delta Blvd Atlanta GA 30320 USA

This article presents a novel solution namely Pre-blocking through Re-waybilling to minimize railcar cuts in freight railways. The Pre-blocking through Re-waybilling Problem (PRP) can be described as follows: A freight train carrying m railcars, loaded or empty, arrives at a local classification yard where, based on the destination on their electronic waybills, railcars will be physically switched to n outbound trains. Number of railcar cuts depends on the order of railcars on train. The objective is to minimize the number of railcar cuts by means of changing the electronic waybills of empty railcars, i.e. re-assignment of empty railcars to customers, while respecting supply, demand, and feasibility constraints. In order to solve this problem, a supply-demand network is constructed first, where supply nodes and demand nodes represent railcars and destinations, respectively. The problem is then formulated as a modified transportation problem. Unlike the conventional transportation problem, a linear arc cost function is proposed to describe the adjacent cars' impact on railcar cut. The objective is converted to a quadratic function, leading to a mixed-integer quadratic programming (MIQP) problem. We improve proposed MIQP to a Convex mixed-integer quadratic programming (CMIQP) problem by adding a quadratic term to the objective function. Illustrative and realistic examples are presented to validate the feasibility and efficiency of proposed pre-blocking optimizer.

关键词： Railroad freight transportation Railcar distribution Transportation problem mixed-integer quadratic programming Pre-blocking

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A FAST GLOBAL ALGORITHM FOR MULTI-LINEARLY CONSTRAINED SEPARABLE BINARY quadratic PROGRAM

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第2期21卷 1456-1473页

作者： Qu, Guangtai Li, Shaoze Deng, Zhibin Lu, Cheng North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

The multi-linearly constrained separable binary quadratic programming problem (MLSBQP) is a direct extension of the classical singly-linearly constrained separable binary quadratic programming problem (SLS-BQP). In the literature, several algorithms have been proposed for SLSBQP which can solve problem instances with millions of variables globally. However, for the case of MLSBQP, the current state-of-the-art solver can only solve problems with up to 40000 binary variables within a reasonable computational time. In this paper, we propose an extremely efficient branch-and-bound algorithm for solving MLSBQP. The main feature of the proposed algorithm is that an efficient dual coordinate ascent algorithm is applied to obtain lower bounds, and the complexity of each inner loop of the dual coordinate ascent algorithm is O(n log n). Our numerical experiments show that the proposed branch-and-bound algorithm can solve MLSBQP with up to millions of variables within only several minutes, and performs much faster than Gurobi when the number of linear constraints is small.

关键词： mixed-integer quadratic programming coordinate ascent branch-and-bound

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An emergency operation strategy and motion planning method for autonomous vehicle in emergency scenarios

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ACCIDENT ANALYSIS AND PREVENTION 2025年 210卷 107842页

作者： Gong, Tianyang Yu, Xiumin Zhang, Qunli Feng, Zilin Yang, Shichun Cao, Yaoguang Xu, Jingyun Feng, Xinjie Pang, Zhaowen Wang, Yu Wang, Peng Jilin Univ Coll Automot Engn Changchun 130022 Jilin Peoples R China Jilin Univ Natl Key Lab Automot Chassis Integrat & Bion Changchun 130022 Jilin Peoples R China Huawei Technol Co Ltd Huawei 2012 Res Ctr Shenzhen 518000 Guangdong Peoples R China Huawei Technol Co Ltd IAS Tech Dev Dept Shenzhen 518000 Guangdong Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Beihang Univ Hangzhou Int Innovat Inst Hangzhou 310051 Zhejiang Peoples R China Beihang Univ State Key Lab Intelligent Transportat Syst Beijing 100091 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China

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.

关键词： Autonomous driving Decision making Trajectory planning mixed-integer quadratic programming

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Zero-feedback open-loop distributed transmit beamforming

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DIGITAL SIGNAL PROCESSING 2025年 156卷

作者： Nguyen, Khoa D. Lechner, Gottfried Ahmad, Ishtiaq Univ South Australia STEM Mawson Lakes SA Australia Def Sci & Technol Grp Edinburg SA Australia

This paper presents a novel open-loop distributed transmit beamforming technique, where no collaboration or feedback from the receiver is required. In general, distributed transmit beamforming without receiver collaboration can be performed by observing a reference signal, together with positional information of individual array nodes. This approach is usually impractical due to the stringent accuracy requirement on positional information. We propose a practically feasible approach to substantially relax this requirement by combining observations from multiple reference signals. The scheme is highly practical since reference signals can be observed from existing broadcast stations, while the beamforming solution can be computed with low complexity. Our numerical results show that significant beamforming gains are achievable with relatively imprecise positional information.

关键词： Virtual array Distributed beamforming Positioning error Opportunistic training mixed-integer quadratic programming

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Automatic control of flow gathering networks: A mixed-integer receding horizon control applied to an onshore oilfield

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CONTROL ENGINEERING PRACTICE 2019年 86卷 48-55页

作者： de Lima, Marcelo Lopes Camponogara, Eduardo de Campos, Mario C. M. M. Miyatake, Luis Kin Petrobras Res Ctr BR-21941970 Rio De Janeiro RJ Brazil Univ Fed Santa Catarina Dept Automat & Syst Engn Cx P 476 BR-88040900 Florianopolis SC Brazil

This paper presents a real-world application of a mixed-integer receding horizon control in an onshore oil gathering network. The objective is to stabilize the operation of the gathering network by coordinating the automatic switching of pumps to avoid abrupt flow variations at the central station, minimizing pump switching and, at the same time, maintaining the level of fluid in the satellite stations under control. The applied technological solution easily integrates different systems, optimization tools and heuristic rules, allowing in-house development of complex applications.

关键词： Gathering networks Onshore oilfield mixed-integer receding horizon control mixed-integer quadratic programming Pump scheduling

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mixed-integer Motion Planning on German Roads Within the Apollo Driving Stack

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 851-867页

作者： Kessler, Tobias Esterle, Klemens Knoll, Alois Fortiss GmbH Res Inst Free State Bavaria D-80805 Munich Germany Tech Univ Munich Robot Artificial Intelligence & Real Time Syst D-80333 Munich Germany

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.

关键词： Planning Trajectory Roads Autonomous vehicles Open source software Real-time systems Task analysis Autonomous driving motion planning mixed-integer quadratic programming

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