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mixed integer linear programming Applied to Adaptive Directional Overcurrent Protection Considering N-1 Contingency

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第3期59卷 2807-2821页

作者： Martin, Klaus Tesser Marchesan, Adriano Cavalheiro Bassi de Araujo, Olinto Cesar Cardoso, Ghendy, Jr. da Silva, Marcelo Freitas Fed Inst Educ Sci & Technol Farroupilha BR-97760000 Jaguari RS Brazil Univ Fed Santa Maria BR-97105900 Santa Maria RS Brazil

This work proposes a methodology for directional overcurrent protection coordination in interconnected transmission systems considering a possible network contingency state. The methodology uses the short-circuit data of the current network topology;however, the maximum load current data in the protection section of each relay is obtained considering the n-1 criterion, already foreseeing the disconnection of some network line. Thus, if a line gets disconnected, other protective devices will not improperly actuate by the redistribution of load currents in the network. The objective is to propose an adaptive protection scheme to redo the coordination for each topological change in the network. To this end, this work considers a smart grid environment with a supervisory system with communication capability between this and the remote devices. To obtain the optimal performance, the coordination problem, originally non-linear and non-convex, is linearized, allowing its formulation as a mixed integer linear programming problem. The methodology is applied to the 8-bus test system in 3 different cases and the 30-bus test system. Results show that the optimal coordination is obtained in a fast computational processing time, showing the suitability of the methodology for real-time application.

关键词： Adaptive protection directional overcurrent relay mixed integer linear programming power system protection power systems

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mixed integer linear programming Based Large Neighborhood Search Approaches for the Directed Feedback Vertex Set Problem 9th

Mixed Integer Linear Programming Based Large Neighborhood Se...

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9th International Conference on Metaheuristics and Nature Inspired Computing (META)

作者： Bresich, Maria Varga, Johannes Raidl, Guenther R. Limmer, Steffen TU Wien Inst Log & Computat Vienna Austria Honda Res Inst Europe GmbH D-63073 Offenbach Germany

ISBN: (纸本)9783031692567;9783031692574

A directed feedback vertex set (DFVS) of a directed graph is a subset of vertices whose removal makes the graph acyclic. Finding a DFVS of minimum cardinality is the goal of the directed feedback vertex set problem, an NP-hard combinatorial optimization problem. We first consider two mixed integer linear programming (MILP) models for this problem, which, when solved with Gurobi, are effective on graphs of small to medium complexity but do not scale well to large instances. Aiming at better scalability and higher robustness over a large variety of graphs, we investigate a large neighborhood search (LNS) in which a destroy operator removes randomly chosen nodes from an incumbent DFVS and one of the MILP models is used for repair. Regarding the destroy operator, finding a best degree of destruction is challenging. A main contribution lies in proposing several selection strategies for this parameter as well as a strategy for choosing the more promisingMILP model for repair. We evaluate the performance of the MILP models and different LNS variants on benchmark instances and compare the approaches to each other as well as to state-of-the-art procedures. Results show that our LNS variants yield clearly better solutions on average than standalone MILP solving. Even though our approaches cannot outperform the state-of-the-art, we gain valuable insights on beneficially configuring such a MILP-based LNS.

关键词： Large neighborhood search Mathematical programming mixed integer linear programming Directed feedback vertex set problem

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Machine learning augmented branch and bound for mixed integer linear programming

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MATHEMATICAL programming 2024年 1-44页

作者： Scavuzzo, Lara Aardal, Karen Lodi, Andrea Yorke-Smith, Neil Delft Univ Technol Delft Netherlands Cornell Tech Jacobs Technion Cornell Inst New York NY USA Technion IIT New York NY USA

mixed integer linear programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. The main engine for solving MILPs is the branch-and-bound algorithm. Adding to the enormous algorithmic progress in MILP solving of the past decades, in more recent years there has been an explosive development in the use of machine learning for enhancing all main tasks involved in the branch-and-bound algorithm. These include primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This article presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address appropriate MILP representations, benchmarks and software tools used in the context of applying learning algorithms.

关键词： mixed integer linear programming Branch and bound Machine learning Metrics Benchmarks

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A mixed integer linear programming Model for Resolution of the Antenna-Satellite Scheduling Problem

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第1期60卷 463-473页

作者： Linares, Lorena Vazquez, Rafael Perea, Federico Galan-Vioque, Jorge Univ Seville Dept Aerosp Engn Fluids Mech ETSI Seville 41092 Spain Univ Seville Appl Math 2 EPS Seville 41004 Spain Univ Seville Appl Math II ETSI Seville 41092 Spain

This article deals with one of the types of "Satellite Range Scheduling" problems arising in Earth Observation Satellite operations, Antenna-Satellite Scheduling. Given a set of satellites, a set of available antennas and a time horizon, the problem consists of designing an operational plan that assigns satellites to antennas in an optimal fashion. Extending a previous integer linear programming (ILP) model (shortening model, with only integer variables), we propose a mixed ILP (MILP) (shaving model, which includes both continuous and integer variables), to more efficiently solve this problem. After computing the passes generated by the satellites' windows of visibility from the antennas, the optimal planner is able to cancel a pass, move it to another antenna, or shorten its duration, in order to avoid scheduling conflicts. In contrast to the shortening model, which used intersections between passes to determine the best schedule, the shortening operation is now referred to as shaving, since the shaving model can arbitrarily adjust the duration of a pass in a razor-like fashion, giving the model its name. Computational results obtained in tests over realistic scenarios prove that the shaving model outperforms the shortening model, producing fewer cancellations, smaller shaved times, and a fairer distribution of cancelled passes among satellites, with much shorter preprocessing times.

关键词： Satellites Satellite antennas Task analysis Computational modeling Genetic algorithms Optimal scheduling Antennas Antenna-satellite allocation deconfliction mixed integer linear programming optimal satellite scheduling satellite range scheduling

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A multi-period multiple parts mixed integer linear programming model for AM adoption in the spare parts supply Chain

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第5期37卷 550-571页

作者： Mecheter, Asma Pokharel, Shaligram Tarlochan, Faris Tsumori, Fujio Qatar Univ Dept Mech & Ind Engn Doha Qatar Kyushu Univ Fac Engn Dept Aeronaut & Astronaut Fukuoka Japan Qatar Univ Dept Mech & Ind Engn POB 2713 Doha Qatar

This research proposes a multi-period multiple parts mixed-integer linear programming optimization model for the trade-off analysis of spare parts supply through computer numerical control (CNC) manufacturing and additive manufacturing (AM). The multiple spare parts have different characteristics such as volume, shape size, and geometry complexity. The model focuses on minimizing lead times and thus reducing downtime costs. Scenario analyses are developed for some parameters to assess the robustness of the model. The analysis shows that the mix between AM-based spare parts and CNC-based spare parts is sensitive to changes in demand. For the given data, the findings demonstrate that AM is cost-effective with spare parts having high geometry complexity while CNC-based manufacturing is economically feasible for spare parts with low geometry complexity and large sizes. The proposed model can support decision-makers in selecting the optimal manufacturing method for multiple spare parts having different characteristics and attributes. The paper concludes with limitations and future directions.

关键词： Additive manufacturing supply chain spare parts optimization mixed integer linear programming conventional manufacturing

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A Fast and Accurate Self-Healing Scheme for Intelligent Distribution Networks Using mixed integer linear programming

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IEEE ACCESS 2024年 12卷 21586-21595页

作者： Lu, Jiangang Su, Junni Zhao, Ruifeng Chen, Fengchao Wang, Qiujie Yan, Wei Guangdong Power Grid Co Ltd Elect Power Dispatching & Control Ctr Guangzhou 510600 Peoples R China Guangdong Power Grid Co Ltd Dongguan Power Supply Bur Dongguan 523000 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Sec Chongqing 400044 Peoples R China

Fast and accurate self-healing after faults is an important way to improve the reliability of distribution networks. To improve the self-healing speed and accuracy of distribution networks, this paper proposes a fast and accurate self-healing scheme for distribution networks using mixed integer linear programming. Firstly, this method constructs a centralized 5G communication network architecture, which can effectively reduce the communication delay during the self-healing process. Secondly, the principle of logical algebraic transformation is utilized to linearize the switch function in the fault location model, thereby achieving equivalent transformation of the fault location model. Thirdly, using the polyhedral approximation method, the mixed integer second-order cone programming of the power supply recovery model is transformed into a mixed integer linear programming. The proposed method was validated by distribution network systems with different nodes in MATLAB/Simulink, and the results showed that the proposed method significantly improved self-healing speed and accuracy.

关键词： Distribution networks Power supplies Fault location 5G mobile communication Location awareness Fault currents Delays mixed integer linear programming Distribution network fault location fault recovery self-healing mixed integer linear programming 5G communication

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mixed integer linear programming model for a multi-depot arc routing problem with different arc types and flexible assignment of end depot

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Transportation Research Procedia 2025年 82卷 1109-1119页

作者： Ameni Kraiem Jean-François Audy Amina Lamghari Department of Management Université du Québec à Trois-Rivières Trois-Rivières QC G8Z 4M3 Canada Interuniversity Research Center on Enterprise Networks Logistics and Transportation (CIRRELT) Montréal QC H3T 1J4 Canada Group for Research in Decision Analysis (GERAD) Montréal QC H3T 1J4 Canada

This article addresses a street sweeping problem that extends the class of multi-depot arc routing problem by integrating a flexible assignment of end depot for each working shift. A unique team of specialized vehicles starts each shift from a depot, visits the required arcs and returns at the end of each shift to a depot. The service in the next shift should start from the end depot of the previous one. An additional new constraint imposes that the highway arcs must be serviced during a night shift while all others arcs (boulevard, street, etc.) can be swept during both day and night. The aim of this arc routing problem is to find optimal shifts that satisfy these two practical aspects, along with other constraints such as maximum shift duration. The problem is motivated by a real-world application that has not been previously studied in the literature. A mixed integer linear programming model is formulated with the objective of minimizing the total travel time and tested on newly generated instances based on Cordeau's multi-depot vehicle routing problem instances. The results show a generation of total travel time savings up to 12 % compared to the single depot arc routing problem.

关键词： Multi-depot arc routing problem flexible end depot mixed integer linear programming street sweeping

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mixed integer linear programming model and an effective algorithm for the bi-objective double-floor corridor allocation problem

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COMPUTERS & OPERATIONS RESEARCH 2021年 132卷 105283-105283页

作者： Guan, Chao Zhang, Zeqiang Gong, Juhua Liu, Silu Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu 610031 Peoples R China

The bi-objective double-floor corridor allocation problem (bDFCAP) investigated here explores the effective placement of given departments in a double-floor space to minimise the overall flow cost and the corridor length objectives. Within each floor, departments are arranged in two parallel rows on opposite sides along a central corridor without overlapping. In this study, the bDFCAP is formulated as a mixed integer linear programming model, which has improved the performance over the previous one. Thereafter, a genetic algorithm with a variable neighbourhood search technique is designed and employed to solve the bDFCAP in a more effective manner. This technique is utilized to improve the local search capability by adaptively transforming between a deep-searching strategy and broad-searching strategy, and the superior performance of the proposed method is proven through comparisons with two other algorithms in current literature. Besides, the state-of-the-art lower bounds of several benchmark instances are updated.

关键词： Facility layout design Multi-objective optimisation Genetic algorithm mixed integer linear programming Variable neighbourhood search

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A multi-objective optimization model based on mixed integer linear programming for sizing a hybrid PV-hydrogen storage system

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第26期48卷 9748-9761页

作者： Mohammed, Awsan Ghaithan, Ahmed M. Al-Hanbali, Ahmad Attia, Ahmed M. King Fahd Univ Petr & Minerals Construct Engn & Management Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ind & Syst Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia

In this paper, a multi-objective mixed-integer linear programming model is developed to design a hybrid PV-hydrogen renewable energy system considering two objective func-tions;minimizing total life costs and loss probability of power supply. The decisions of the hybrid system include the number of PV panels, the number of hydrogen tanks, the number of electrolyzers, the number of fuel cells, and quantity of hydrogen stored over time. An exact method embedded in GAMS software is used to solve the developed model. The model is validated using an electrical testing lab in Saudi Arabia with hourly power demand. Different plans are chosen from the obtained optimal Pareto solutions. For example, one of the plans found that a hybrid system with 212 PV panels, 617 hydrogen tanks, 30 electrolyzers, and 21 fuel cells is sufficient to satisfy the electrical testing lab load with an annual cost of $61663, the loss of power supply probability is 10%, and the CO2 saving of 214,882 kg of CO2. The results indicated the feasibility of combining an electro-lyzer, hydrogen tank storage, and fuel cell with a renewable energy system;however, the cost of energy generated is still high because of the high investment cost. Furthermore, the findings revealed that hydrogen technologies are appealing as an energy storage solution for intermittent renewable energy systems and in other applications such as trans-portation, residential, and industrial sectors. In addition, the findings demonstrated the possibility of using renewable energy as a source of energy, namely, in Saudi Arabia. However, weather conditions, geomorphological conditions, and climatic dependence on hydrogen fuel cell technology can harm the energy yields produced by renewable energy systems.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Photovoltaic system Hydrogen storage mixed integer linear programming Multi-objective optimization

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An assembly timing planning method based on knowledge and mixed integer linear programming

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第2期34卷 429-453页

作者： Qian, Jiahui Zhang, Zhijing Shi, Lingling Song, Dan Beijing Inst Technol Sch Mech Engn 5 Zhongguancun South St Beijing 100081 Peoples R China

Assembly timing planning, which aims to solve the assembly action sequence and assembly part sequence with the shortest assembly time as the goal, is a necessary and critical step in intelligent assembly process planning. However, the current focus of assembly process planning is assembly sequence planning, whereas little research has been performed on assembly timing planning. A novel assembly timing planning method based on knowledge and mixed integer linear programming (MILP) is proposed in this paper. First, a knowledge base of the assembly process for timing planning is constructed using ontology. Then, based on the proposed strategy of dividing assembly timing planning into within-group planning and between-group planning, a MILP model of assembly timing planning for automatic assembly system is constructed. In addition, a software that realizes timing planning through human-machine collaboration is developed to verify and visualize the proposed timing planning method. The implementation is as follows: assembly action sentences are formed by searching the ontology keyword library, then timing knowledge for the action sequence and assembly sequence is established, and finally optimal assembly timing results are obtained after the calculation. Compared with the traditional serial assembly process, this method significantly reduces the assembly time, thereby improving the assembly efficiency, and the assembly schedule can be obtained automatically and quickly to guide the assembly process design.

关键词： Assembly timing planning Knowledge mixed integer linear programming Intelligent assembly

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