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A Study of TMA Aircraft Conflict-Free Routing and Operation: With mixed integer linear programming, Multi-Agent Path Finding, and Metaheuristic-Based Neighborhood Search

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13976-13990页

作者： Zhang, Yi Zhang, Sheng Zhang, Yicheng Yin, Yifang ASTAR Inst Infocomm Res I2R Singapore 138632 Singapore

In this paper, we proposed a conflict-free routing strategy combined with scheduling for Terminal Manoeuvring Area (TMA) multi-aircraft to guarantee a safe separation. By incorporating Standard Terminal Arrival Routes (STARs) as route constraints, a mixed-logic model is designed to maximize the runway throughput while ensuring minimum separation between aircraft and avoiding overtaking on each STARs segment. Control techniques such as speed recommendation and holding operations are employed to the model to address potential conflicts. Three different algorithms are developed to solve the model: branch and bound with mixed-integer linear programming, multi-agent pathfinding with constraint programming, and meta-heuristics with evolutionary neighborhood search. These algorithms are tested on multiple cases of varying scales. Finally, we demonstrate the advantages of the proposed three algorithms by simulating realistic scenarios and comparing the results with Singapore ADS-B (Automatic dependent Surveillance-Broadcast) historical dataset. In one hour testing, results show that our method could reduce the last aircraft landing time nearly 10 minutes and save more than 80 minutes for total flight travel times for all aircraft, as well as non-vectoring flight trajectories, which indicates its potential to be used as an auxiliary decision-making tool for Air Traffic Controllers (ATCOs).

关键词： Aircraft Atmospheric modeling Routing Stars Sequential analysis Air traffic control Safety Air traffic management trajectory based operation mixed-integer linear programming constraint programming meta-heuristics harmony search priority-based multiagent path finding

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A mixed integer linear programming approach for the design of chemical process families

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COMPUTERS & CHEMICAL ENGINEERING 2024年 183卷

作者： Stinchfield, Georgia Morgan, Joshua C. Naik, Sakshi Biegler, Lorenz T. Eslick, John C. Jacobson, Clas Miller, David C. Siirola, John D. Zamarripa, Miguel Zhang, Chen Zhang, Qi Laird, Carl D. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Natl Energy Technol Lab NETL Pittsburgh PA 15236 USA NETL Support Contractor Pittsburgh PA 15236 USA Carrier Global Corp Palm Beach Gardens FL 33418 USA Sandia Natl Labs Albuquerque NM 87123 USA Univ Minnesota Dept Chem Engn & Mat Sci Minneapolis MN 55455 USA

The need for rapid and widespread deployment of new technologies to address climate change goals (e.g., deep, economy -wide decarbonization) presents new opportunities for advancing modular design strategies. Conventional engineering approaches focus on unique designs for each installation, while missing opportunities for manufacturing standardization. Extending insights from the automotive industry, we optimize a platform of common unit module designs while simultaneously designing an entire family of process variants that make use of that platform. This reduces engineering effort, deployment timelines, and manufacturing costs. We propose a nonlinear generalized disjunctive programming formulation and convert this to an efficient mixed -integer linear programming (MILP) formulation through discretization of the design space. We formulate our optimization in Pyomo with costing from IDAES, and we demonstrate the computational performance and solution quality on a water treatment desalination system from the PARETO framework and a carbon capture system built in Aspen Plus (R) as part of CCSI2.

关键词： Process Family Design Manufacturing Optimization mixed-integer linear programming Carbon capture

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mixed-integer linear programming approaches for tree partitioning of power networks

arXiv

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arXiv 2021年

作者： Lan, Leon Zocca, Alessandro

In transmission networks, power flows and network topology are deeply intertwined due to power flow physics. Recent literature shows that a specific more hierarchical network structure can effectively inhibit the propagation of line failures across the entire system. In particular, a novel approach named tree partitioning has been proposed, which seeks to bolster the robustness of power networks through strategic alterations in network topology, accomplished via targeted line switching actions. Several tree partitioning problem formulations have been proposed by considering different objectives, among which power flow disruption and network congestion. Furthermore, various heuristic methods based on a two-stage and recursive approach have been proposed. The present work provides a general framework for tree partitioning problems based on mixed-integer linear programming (MILP). In particular, we present a novel MILP formulation to optimally solve tree partitioning problems and also propose a two-stage heuristic based on MILP. We perform extensive numerical experiments to solve two tree partitioning problem variants, demonstrating the excellent performance of our solution methods. Lastly, through exhaustive cascading failure simulations, we compare the effectiveness of various tree partitioning strategies and show that, on average, they can achieve a substantial reduction in lost load compared to the original topologies. Copyright © 2021, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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A mixed-integer linear programming for Nodal Clearing Price in Day Ahead Market Considering Security Constraints

A Mixed-Integer Linear Programming for Nodal Clearing Price ...

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Asia Conference on Geological Research and Environmental Technology (GRET)

作者： Sun, Guanghui Wang, Xiaowei Yang, Libo Lu, Peng Ma, Bin Lv, Hao Yuan, Long State Grid Hebei Elect Power CO LTD Shijiazhuang 050021 Hebei Peoples R China

A nodal clearing price model in day ahead market considering security constraints is established. First, the intra-region and inter-region transaction modes are explained, and the order types are shown. The clearing model takes the total social welfare as the goal, considers the security constraints of the network, and specifies the market clearing price expressed by the Lagrangian multiplier. Then the mixed linear programming algorithm is used to solve the model in this paper, and a verification method based on the N-1 criterion is performed. Finally, a numerical case is performed to calculate the clearing price of the network, which illustrates the effectiveness of the proposed method and model.

关键词： Clearing price electricity transaction order mixed-integer linear programming security constraints

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Multistage reliability-based expansion planning of ac distribution networksusing a mixed-integer linear programming model

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 138卷 107916-107916页

作者： Tabares, Alejandra Munoz-Delgado, Gregorio Franco, John F. Arroyo, Jose M. Contreras, Javier Los Andes Univ Dept Ind Engn Bogota Colombia Univ Castilla La Mancha Escuela Tecn Super Ingn Ind Ciudad Real Spain Sao Paulo State Univ Sch Energy Engn Sao Paulo Brazil

A new mathematical model for the multistage distribution network expansion planning problem consideringreliability is proposed in this paper. Decisions related to substation and branch expansion are driven bythe minimization of the total cost, which comprises investment and operating costs including the impactof reliability. The proposed model features two main novelties. First, a set of novel algebraic expressions isdevised for a standard reliability index, namely the expected energy not supplied. As a result, the dependence ofreliability on network topology is explicitly and effectively cast in the mathematical formulation of the planningproblem at hand. In addition, the effect of the network is characterized by a computationally efficient piecewiselinear representation of the ac power flow model that takes into account both real and reactive power. Theresulting optimization problem is formulated as an instance of mixed-integer linear programming, whichprovides a suitable framework for the attainment of high-quality solutions with acceptable computational effortusing efficient off-the-shelf software with well-known convergence properties. The effectiveness of the proposedplanning methodology is empirically demonstrated by providing cheaper expansion plans that enhance systemreliability and by achieving better computational results as compared with state-of-the-art models.

关键词： ac network model Distribution network expansion planning mixed-integer linear programming Multistage Reliability

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A mixed-integer linear programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

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ENERGIES 2022年第9期15卷 3063-3063页

作者： Gallego Pareja, Luis A. Lopez-Lezama, Jesus M. Gomez Carmona, Oscar State Univ Londrina UEL Dept Elect Engn BR-86057970 Londrina Parana Brazil Univ Antioquia Dept Ingn Elect Res Grp Efficient Energy Management GIMEL Calle 67 53-108 Medellin 050010 Colombia Univ Tecnol Pereira Fac Technol Cr 27 10-02 Pereira 660003 Colombia

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time;furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

关键词： distribution systems reconfiguration distributed generation mixed-integer linear programming

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A mixed-integer linear programming Model for the Cutting Stock Problem in the Steel Industry 8th

A Mixed-Integer Linear Programming Model for the Cutting Sto...

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8th Workshop on Engineering Applications (WEA)

作者： Morillo-Torres, Daniel Torres Baena, Mauricio Wilmer Escobar, John Romero-Conrado, Alfonso R. Coronado-Hernandez, Jairo R. Gatica, Gustavo Pontificia Univ Javeriana Cali Cali Colombia Univ Valle Cali Colombia Univ Costa Barranquilla Colombia Univ Andres Bello Santiago Chile

ISBN: (纸本)9783030867027;9783030867010

A mixed-integer linear programming (MILP) model is proposed for solving a one dimension cutting stock problem (1D-CSP) in the steel industry. A case study of a metallurgical company is presented and the objective is to minimize waste in the cutting process of steel bars, considering inventory constraints and the potential use of the resulting leftovers. The computational results showed that an optimal solution was always found with an average improvement in waste reduction of 80%. There was no significant difference when comparing results between the complete model and the model without inventory constraints.

关键词： Cutting stock problem mixed-integer linear programming Steel bars Industrial application

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A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty

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JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2021年第1期38卷 1-31页

作者： Gharibi, Kazhal Abdollahzadeh, Sohrab Urmia Univ Technol Orumiyeh Iran

Purpose To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA. Design/methodology/approach The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected. Findings The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased. Originality/value (1) Design a closed-loop reverse logistics network for after-sales services;(2) Introduce a multi-objective multi-echelon mixed integer linear programming model;(3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units;(4) Use the GSC factors and DEA method in reverse logistics network.

关键词： Green reverse supply chain Inverse data envelopment analysis mixed-integer linear programming Circular economy Electrical and electronic waste Closed-loop supply chain

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A Novel mixed-integer linear programming Formulation for Continuous-Time Inventory Routing

arXiv

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arXiv 2023年

作者： Wang, Akang Li, Xiandong Arbogast, Jeffrey E. Wilson, Zachary Gounaris, Chrysanthos E. Department of Chemical Engineering Center for Advanced Process Decision-making Carnegie Mellon University PittsburghPA15213 United States Shenzhen Research Institute of Big Data Guangdong Shenzhen China Air Liquide NewarkDE19702 United States

Inventory management, vehicle routing, and delivery scheduling decisions are simultaneously considered in the context of the inventory routing problem. This paper focuses on the continuous-time version of this problem where, unlike its more traditional discrete-time counterpart, the distributor is required to guarantee that inventory levels are maintained within the desired intervals at any moment of the planning horizon. In this work, we develop a compact mixed-integer linear programming formulation to model the continuous-time inventory routing problem. We further discuss means to expedite its solution process, including the adaptation of well-known rounded capacity inequalities to tighten the formulation in the context of a branch-and-cut algorithm. Through extensive computational studies on a suite of 90 benchmark instances from the literature, we show that our branch-and-cut algorithm outperforms the state-of-the-art approach. We also consider a new set of 63 instances adapted from a real-life dataset and show our algorithm’s practical value in solving instances with up to 20 customers to guaranteed optimality. © 2023, CC BY-NC-SA.

关键词： mixed-integer linear programming

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A mixed-integer linear programming approach for collateral management

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Decision Analytics Journal 2024年 10卷

作者： Goblet, Jordan Willame, Fabien Grauls, Jan Euroclear Koning Albert II Laan 1 Brussels 1210 Belgium

Collateral management involves the efficient monitoring and allocation of assets to mitigate credit risk in financial transactions and is crucial for institutions such as commercial banks, investments banks, and central counterparties. It helps to ensure financial stability and compliance with regulatory requirements. This study is centered on a fundamental objective of collateral management, namely to collateralize as much as possible a set of transactions given a limited asset portfolio. In practice, an allocation is dynamic, necessitating periodic adjustments due to fluctuations over time in the asset pool, the transactions set, and asset value. Consequently, the objective is not merely the identification of an optimal allocation from zero but the optimization of an obsolete allocation in response to these fluctuations. The size and complexity of real-world problems often preclude direct resolution through conventional MILP solvers. In addition, each collateral adjustment movement incurs associated costs. In this research, a two-step method based on linear and mixed-integer linear programming formulations with ℓ₁ penalties is devised, with the principal aim of minimizing under-allocation while simultaneously controlling the number of adjustment movements required. The method is tested on 20 real-world collateral givers and invariably reaches a good trade-off between solution quality, computational time, and the number of movements. © 2024 The Authors

关键词： Collateral management Collateral optimization linear programming mixed-integer linear programming ℓ1 regularization

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