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Airline maintenance task rescheduling in a disruptive environment

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期308卷 605-621页

作者： van Kessel, Paul J. Freeman, Floris C. Santos, Bruno F. KLM Royal Dutch Airlines NL-1117 ZL Schiphol Netherlands Delft Univ Technol Fac Aerosp Engn Air Transport & Operat Delft Netherlands

Airline maintenance task scheduling takes place in a disruptive environment. The stochastic arrival of corrective maintenance tasks and changes in both fleet and resource availability require schedules to be continuously adjusted. An optimal schedule ensures that all tasks are executed before their due date in both an efficient (at minimum use of ground-time) and a stable (limited number of schedule changes) manner. This paper is the first study to address disruption management for the hangar maintenance task scheduling problem, proposing a practical and efficient modeling framework. The framework comprises a mixed integer linear programming model for airline maintenance task rescheduling in a disruptive envi-ronment, in which task scheduling is constrained by the availability of resources. The model's capabilities include creating and adjusting maintenance schedules continuously and dynamically reacting to new in-formation when this becomes available. The modeling framework was tested in a case study provided by a large airline, and its performance was compared to the current practice of the airline. The results show that the proposed approach produces more efficient and stable results. A 3% ground time decrease was achieved, while the number of schedule changes in the last days before operations was decreased by more than half.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Maintenance Task rescheduling Disruption management mixed integer linear programming Airline maintenance

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Optimization of Sparsity-Constrained Neural Networks as a mixed integer linear Program NN2MILP

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2023年第3期199卷 931-954页

作者： Rosenhahn, Bodo Leibniz Univ Hannover Appelstr 9A D-30167 Hannover Germany

The literature has shown how to optimize and analyze the parameters of different types of neural networks using mixed integer linear programs (MILP). Building on these developments, this work presents an approach to do so for a McCulloch/Pitts and Rosenblatt neurons. As the original formulation involves a step-function, it is not differentiable, but it is possible to optimize the parameters of neurons, and their concatenation as a shallow neural network, by using a mixed integer linear program. The main contribution of this paper is to additionally enforce sparsity constraints on the weights and activations as well as on the amount of used neurons. Several experiments demonstrate that such constraints effectively prevent overfitting in neural networks, and ensure resource optimized models.

关键词： mixed integer linear programming Neural networks Feature selection Sparse networks Resource optimization

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Multi-instance learning by maximizing the area under receiver operating characteristic curve

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JOURNAL OF GLOBAL OPTIMIZATION 2023年第2期85卷 351-375页

作者： Sakarya, I. Edhem Kundakcioglu, O. Erhun Eindhoven Univ Technol Dept Ind Engn & Innovat Sci Eindhoven Netherlands Ozyegin Univ Dept Ind Engn Istanbul Turkey

The purpose of this study is to solve the multi-instance classification problem by maximizing the area under the Receiver Operating Characteristic (ROC) curve obtained for witness instances. We derive a mixed integer linear programming model that chooses witnesses and produces the best possible ROC curve using a linear ranking function for multi-instance classification. The formulation is solved using a commercial mathematical optimization solver as well as a fast metaheuristic approach. When the data is not linearly separable, we illustrate how new features can be generated to tackle the problem. We present a comprehensive computational study to compare our methods against the state-of-the-art approaches in the literature. Our study reveals the success of an optimal linear ranking function through cross validation for several benchmark instances.

关键词： Multi-instance learning mixed integer linear programming Area under curve

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A mathematical programming-based solution method for the nonstationary inventory problem under correlated demand

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期304卷 515-524页

作者： Xiang, Mengyuan Rossi, Roberto Martin-Barragan, Belen Tarim, Armagan Xian Jiaotong Liverpool Univ Entrepreneur Coll Sch Intelligent Finance & Business Suzhou Peoples R China Univ Edinburgh Business Sch Edinburgh Scotland Univ Coll Cork Business Sch Cork Ireland

This paper extends the single-item single-stocking location nonstationary stochastic inventory problem to relax the assumption of independent demand. We present a mathematical programming-based solu-tion method built upon an existing piecewise linear approximation strategy under the receding horizon control framework. Our method can be implemented by leveraging off-the-shelf mixed-integer linear pro-gramming solvers. It can tackle demand under various assumptions: the multivariate normal distribution, a collection of time-series processes, and the Martingale Model of Forecast Evolution. We compare against exact solutions obtained via stochastic dynamic programming to demonstrate that our method leads to near-optimal plans.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Inventory Correlated demand Stochastic programming mixed integer linear programming Martingale model of forecast evolution

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Efficient algorithms for the bucket wheel reclaimer scheduling problem with sequence-dependent setup times

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ANNALS OF OPERATIONS RESEARCH 2024年 1-46页

作者： Benbrik, Oualid Benmansour, Rachid Elidrissi, Abdelhak Todosijevic, Raca INSEA SI2M Lab Rabat 10112 Morocco Polytech Univ Hauts De France UPHF LAMIH CNRS UMR 8201 INSA Hauts De France Campus Mont Houy F-59313 Valenciennes 9 France Int Univ Rabat Rabat Business Sch Parc Technopolis Rabat Morocco

The effective management of resources in bulk ports presents substantial challenges, prominently concerning the intricate scheduling of bucket wheel reclaimers (BWRs). These essential machines play a vital role in the reclamation of dry bulk materials, facilitating their loading onto vessels via ship-loaders. The research at hand centers around the BWR scheduling problem, wherein the sequence-dependent setup times and eligibility restrictions of these machines are duly considered. The optimization of BWR schedules holds direct implications for terminal throughput, which is a paramount performance metric for dry bulk terminals. The primary objective revolves around the minimization of total completion times, as it pertains to enhancing the overall efficiency and productivity of the terminal operations. For this NP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal{N}\mathcal{P}$$\end{document}-hard problem, we present a novel mixed-integer linear programming (MILP) formulation based on one commodity variables. Additionally, we develop three efficient greedy heuristics, each with three variants, resulting in a total of nine heuristics, to solve large-sized instances of the problem. Furthermore, an efficient general variable neighborhood search (GVNS) algorithm is proposed to improve the quality of the heuristic solutions. Through extensive computational experiments, we assess the effectiveness of the proposed methods. The results demonstrate that the developed greedy heuristics efficiently yield high-quality approximation solutions for solving large-scale instances of the problem. Moreover, the application of the GVNS algorithm further enhances the solutions obtained through the heuristics, leading to an improvement in scheduling efficiency.

关键词： Reclaimer scheduling Dry bulk terminal Sequence-dependent setup times Machine eligibility restrictions mixed integer linear programming Greedy heuristics General variable neighborhood search

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On the General Problem Statement of Cargo Carriages Scheduling and Ways to Solve It

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AUTOMATION AND REMOTE CONTROL 2023年第4期84卷 443-455页

作者： Ignatov, A. N. Moscow Inst Aviat Technol Moscow Russia

A new mathematical model of transportation along the transport network represented by an undirected multigraph is formulated. A new criterion for the optimality of cargo carriages schedule is proposed. The criterion in addition to the time characteristics of transportation includes their cost, the number of undelivered cargoes. The problem to find the optimal schedule is formulated as a problem of mixed integer linear programming. Various variants of the algorithm for searching for an approximate solution to the problem are proposed. Informative examples are considered.

关键词： transport network multigraph cargo carriage schedule mixed integer linear programming

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Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme

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BIG DATA AND COGNITIVE COMPUTING 2024年第12期8卷 172-172页

作者： Ng, Hibatul Azizi Hisyam Mahmoodi, Toktam Kings Coll London Dept Engn London WC2R 2LS England

Machine learning is taking on a significant role in materializing a new vision of 6G. 6G aspires to provide more use cases, handle high-complexity tasks, and improvise the current 5G and beyond 5G infrastructure. Artificial Intelligence (AI) and machine learning (ML) are the optimal candidates to support and deliver these aspirations. Traffic steering functions encompass many opportunities to help enable new use cases and improve overall performance. The emergence and advancement of the non-terrestrial network is another driving factor for creating an intelligence selection scheme to have a dynamic traffic steering function. With service-based architecture, 5G and 6G are data-driven architectures that use massive transactional data to emerge a new approach to handling highly complex processes. A highly complex process, a massive volume of data, and a short timeframe require a scheme using machine learning techniques to resolve the challenges. In this paper, the study creates a scheme to use the massive historical data and provide a decision scheme that enables dynamic traffic steering functions addressing the future emergence of the heterogeneous transport network and aligns with the Open Radio Access Network (O-RAN). The proposed scheme in this paper gives an inference to be programmed in the telecommunication nodes. It provides a novel scheme to enable dynamic traffic steering functions for the 6G transport network. The study shows an appropriate data size to create a high-performance multi-output classification model that produces more than 90% accuracy for traffic steering functions.

关键词： machine learning Open Radio Access Network non-terrestrial network QoS flow identifier mixed integer linear programming optimization solver service function chaining Virtual Network Function multi-output classification random forest algorithm

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Marginal fixed premium for clean hydrogen portfolio standard (CHPS) considering techno-economic analysis of clean hydrogen production based on hydropower plants

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ENERGY REPORTS 2023年 10卷 1356-1368页

作者： Son, Yeong Geon Kwag, Hyung Geun Lee, Sung Hun Kim, Sung Yul Keimyung Univ Daegu South Korea Korea Power Exchange Naju South Korea Korea Water Resources Corp Daejeon South Korea

As the hydrogen economy recently gains momentum, the Korean government is actively encouraging Independent Power Producers (IPPs) to leverage hydrogen energy. The surging uncertainties in the power market, precipitated by the rapid increase in renewable energy sources (RES), necessitates IPPs to formulate a new benefit structure that utilizes green hydrogen. However, the current economic challenges associated with the operation of RES linked to water electrolysis result in diminished participation from IPPs. In response, the Korean government is planning to implement the Clean Hydrogen Portfolio Standard (CHPS), which requires clean hydrogen power generation and offers corresponding Fixed Premium (FP) subsidies. In this paper, we incrementally increase the FP for RES linked to electrolyzers (ELZ) and derive a marginal fixed premium (FPmar). This method initiates a shift towards higher economic feasibility compared to the traditional benefit structure of power tradingbased RES. We suggest a green hydrogen-based business model (BM) that synergizes hydropower with water electrolysis and calculate a suitable CHPS subsidy to ensure the economic feasibility of this BM. In conclusion, this paper equips IPPs operating hydropower with the capability to evaluate the adequacy of the government-provided FP for the economical operation of a hydropower plant linked to water electrolysis. Furthermore, it provides policymakers with the framework to make informed decisions regarding the necessary FPmar to stimulate IPPs' participation.& COPY;2023 The Authors. Published by Elsevier *** is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Clean hydrogen portfolio standard Fixed premium Hydrogen energy Hydropower mixed integer linear programming

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Optimization and analysis of a hydrogen supply chain in terms of cost, CO2 emissions, and risk: the case of Turkey

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第60期48卷 22752-22765页

作者： Erdogan, Ahmet Guler, Mehmet Guray Yildiz Tech Univ Fac Mech Engn Dept Ind Engn Istanbul Turkiye

The transportation sector, which is largely dependent on oil, is faced with many problems such as the danger of depletion of fossil fuels that are harmful to the environment. Moreover, the situations such as epidemics and war cause excessive fluctuations in oil prices. Therefore, there is a need for new solutions based on alternative energy sources for a sustainable transportation sector. Hydrogen fuel cell electric vehicles (HFCEV) are one of the significant alternatives for an efficient, zero emissions and sustainable transportation system. Considering the potential investment in HFCEV technology, the need for a cost effective, green, and low risk Hydrogen Supply Chain (HSC) network infrastructure is inevitable. In this study, the HSC design of the Turkish transportation sector over a 25-year period (2026-2050) is investigated. The problem is modeled using a multi-period mixed integer linear programming (MIP) model. Three objectives are addresses: cost, carbon dioxide (CO2) emissions and safety risk. In order to consider the uncertainty in the hydrogen demand, five different scenarios are analyzed using fuzzy concept. There are four main results. First, unit hydrogen cost is found to be very high due to low demand and high capital cost in the initial period (2026-2031). Second, HSC network is established in a decentralized setting in all scenario solutions. The level of decentralization gets stronger over time and with increasing demand. Third, short-distance road transport is generally preferred for hydrogen transport. Fourth, since the aim is to minimize cost, CO2 emissions, and risk level, a mixed production strategy based on cost-oriented SMR and zeroemissions-oriented Electrolysis (ELE) is observed in all scenarios.& COPY;2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen supply chain network Multi-objective optimization mixed integer linear programming

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Decentralized Weapon-Target Assignment Under Asynchronous Communications

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第2期46卷 312-324页

作者： Hendrickson, Katherine Ganesh, Prashant Volle, Kyle Buzaud, Paul Brink, Kevin Hale, Matthew Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Univ Florida Dept Mech & Aerosp Engn Shalimar FL 32579 USA Air Force Res Lab Nav Control Branch Weap Dynam Eglin AFB FL 32542 USA

The weapon-target assignment problem is a classic task assignment problem in combinatorial optimization, and its goal is to assign some number of workers (the weapons) to some number of tasks (the targets). Classical approaches for this problem typically use a centralized planner leading to a single point of failure and often preventing real-time replanning as conditions change. This paper introduces a new approach for distributed, autonomous assignment planning executed by the weapons where each weapon is responsible for optimizing over distinct subsets of the decision variables. A continuous, convex relaxation of the associated cost function and constraints is introduced, and a distributed primal-dual optimization algorithm is developed that will be shown to have guaranteed bounds on its convergence rate, even with asynchronous computations and communications. This approach has several advantages in practice due to its robustness to asynchrony and resilience to time-varying scenarios, and these advantages are exhibited in experiments with simulated and physical commercial off-the-shelf ground robots as weapon surrogates that are shown to successfully compute their assignments under intermittent communications and unexpected attrition (loss) of weapons.

关键词： Weapon Target Assignment Asynchronous Communications Robots Optimization Algorithm COTS mixed integer linear programming Fire and Forget Histograms Computing Evolutionary Algorithm

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