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Finding low-conductance sets with dense interactions (FLCD) for better protein complex prediction

BMC SYSTEMS BIOLOGY 2017年第3-Sup期11卷 22-22页

作者： Wang, Yijie Qian, Xiaoning Texas A&M Univ Dept Elect & Comp Engn MS 3128 College Stn TX 77843 USA

Background: Intuitively, proteins in the same protein complexes should highly interact with each other but rarely interact with the other proteins in protein-protein interaction (PPI) networks. Surprisingly, many existing computational algorithms do not directly detect protein complexes based on both of these topological properties. Most of them, depending on mathematical definitions of either "modularity" or "conductance", have their own limitations: Modularity has the inherent resolution problem ignoring small protein complexes;and conductance characterizes the separability of complexes but fails to capture the interaction density within complexes. Results: In this paper, we propose a two-step algorithm FLCD ( Finding Low-Conductance sets with Dense interactions) to predict overlapping protein complexes with the desired topological structure, which is densely connected inside and well separated from the rest of the networks. First, FLCD detects well-separated subnetworks based on approximating a potential low-conductance set through a personalized PageRank vector from a protein and then solving a mixed integer programming (MIP) problem to find the minimum-conductance set within the identified low-conductance set. At the second step, the densely connected parts in those subnetworks are discovered as the protein complexes by solving another MIP problem that aims to find the dense subnetwork in the minimum-conductance set. Conclusion: Experiments on four large-scale yeast PPI networks from different public databases demonstrate that the complexes predicted by FLCD have better correspondence with the yeast protein complex gold standards than other three state-of-the-art algorithms (ClusterONE, LinkComm, and SR-MCL). Additionally, results of FLCD show higher biological relevance with respect to Gene Ontology (GO) terms by GO enrichment analysis.

关键词： Protein complex identification Low conductance set Dense subnetwork mixed integer programming

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Helicopter recovery in an oil and gas industry: Model and solution approaches

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2022年第0期11卷

作者： De La Vega, Jonathan Vieira, Thiago Santana, Mateus Pureza, Vitoria Morabito, Reinaldo Tavares, Roberto Bastos, Yan Ribas, Paulo Cesar Univ Fed Sao Carlos Dept Prod Engn BR-13565905 Sao Carlos SP Brazil Petrobras SA Res & Dev Ctr Ave Horacio Macedo 950 Rio De Janeiro RJ Brazil

Although optimization techniques have been successfully applied for a number of airline operations, aircraft recovery remains a challenge for both practitioners and researchers due to its complexity and the usual need of a quick response in practical settings. In this paper, we consider the case of a Brazilian oil and gas company that uses a heterogeneous fleet of helicopters for passenger transportation from a few mainland aerodromes to maritime units. The major difficulties in rescheduling delayed flights are resource limitations and realistic features of the company combined with safety and management practices. The problem consists of determining joint daily flight reschedules for all aerodromes that satisfy operational constraints and recovers all pending flights, while minimizing flight transfers among aerodromes, usage of helicopters and overall flight delays. We propose a mixed integer programming (MIP) model, aiming to appropriately represent the problem, and MIP-based local search and two-phase heuristics to cope with larger realistic problem instances. Computational results obtained with instances collected in the case study show the potential of these approaches to deal with this real-life aircraft recovery problem.

关键词： Aircraft recovery problem Flight rescheduling mixed integer programming Heuristics Oil and gas industry

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An Optimization Model for Overall Urban Energy Planning

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IFAC Proceedings Volumes 1978年第1期11卷 1657-1659页

作者： C. Mattsson J.A. Bubenko Department of Electric Power Systems Engineering The Royal Institute of Technology S-100 44 Stockholm 70 Sweden

A model is presented for overall energy planning of an urban region with respect to the preference of energy forms. The region is divided into subareas depending on land use and their locations. Electrical and heating demands are forecasted for each subarea and the region as a whole. The energy systems are described by plant and network capacities and linearized cost functions. The objective is to find appropriate energy forms in each subarea to minimize the annual costs. This approach will be used to analyze the economical aspects of different trends in energy consumption, land use potientials and system performance. Finally, the model can be used to analyse the consequences of the uncertainties in cost parameters and demand forecasting.

关键词： Energy planning mixed integer programming load forecast urban planning

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prioriactions: Multi-action management planning in R

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METHODS IN ECOLOGY AND EVOLUTION 2023年第0期

作者： Salgado-Rojas, Jose Hermoso, Virgilio Alvarez-Miranda, Eduardo Forest Sci Ctr Catalonia Solsona Spain Univ Politecn Cataluna Dept Stat & Operat Res Barcelona Spain CSIC Estn Biol Donana Seville Spain Griffith Univ Australian Rivers Inst Brisbane Qld Australia Univ Talca Sch Econ & Business Talca Chile Inst Sistemas Complejos Ingn Santiago Chile Inst Ecol & Biodiversidad Santiago Chile

Designing effective conservation strategies requires deciding not only where to locate conservation actions (i.e. which territorial units should be priortized), but also which type actions should be deployed. For most of conservation planning contexts, deciding where and what to do usually yields a complex and computationally challenging decision-making setting. Although the resulting optimization problems have typically been tackled using heuristic approaches, recent advances in mixed integer programming (MIP) solver technology have turned MIP-based approaches into a practical alternative for solving complex conservation planning problems. We introduce the R package prioriactions, which allows solving complex conservation planning problems comprising prioritization and action deployment decisions. prioriactions features a MIP approach that allows formulating and solving optimally (or nearly optimally) a wide class of conservation planning problems (characterized by different spatial and functional constraints and requirements). Furthermore, the package allows using a variety of commercial and open-source exact solvers enhancing its usability as well as its practical effectiveness. Here, we present a comprehensive description of the main functions available in prioriactions. This package has a workflow of three straightforward steps: (a) validation of the input data, using the inputData() function that prepares input;(b) the creation of a prioritization model, using the problem() function, allows the creation of two types of common models: the minimization of costs to achieve a recovery target and maximizing the recovery benefits given a limited budget;and (c) to solve of the model, using the solve() function. The prioriactions package provides a user-friendly platform for addressing different multi-actions management problems, allowing to identify more rigorously, transparently and in a reproducible way the spatial deployment of management actions.

关键词： biodiversity management mixed integer programming restoration spatial prioritization threats

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Simultaneous Versus Sequential Balancing and Scheduling of a Flexible Assembly Line

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IFAC Proceedings Volumes 2000年第17期33卷 741-746页

作者： Tadeusz Sawik University of Mining & Metallurgy Departrnent of Coruputer Integrated Manufacturing Al.Mickiewicza 30 30-059 Krakow POLAND

mixed integer programming formulations are presented for simultaneous or sequential balancing and scheduling of a flexible assembly line. The line is made up of a set of assembly stations in series, each with limited working space and is capable of simultaneously producing a mix of product types. The objective is to determine an assignment of assembly tasks to stations and an assembly schedule for all products so as to complete the products in a minimum time. Balancing and scheduling decisions can be made simultaneously or sequentially. In the latter approach first the station workloads are balanced, and then detailed assembly schedule is determined for prefixed task assignments and assembly routes by solving a standard job-shop problem. Balancing and scheduling with alternative or with single task assignments are considered. Numerical examples are provided and some computational results are reported to compare the two approaches.

关键词： Flexible manufacturing systems Loading Scheduling mixed integer programming

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Driver assistance algorithm for automotive collision avoidance using optimization feasibility

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IFAC Proceedings Volumes 2013年第21期46卷 416-421页

作者： Masakazu Mukai Taketoshi Kawabe Kyushu University Motooka 744 Fukuoka 819-0395 JAPAN

This paper considers a collision avoidance problem of the vehicle and the moving obstacle. The prohibited region is defined for the vehicle and the obstacle considering a specified size of them. The problem is formulated as a mixed integer programming problem. In the problem the obstacles and environments around the automobile can be represent as inequality conditions. Then the driver assistance algorithm for the collision avoidance using the feasibility of the optimization is proposed. Computer simulation shows that the proposed algorithm can provide appropriate control input for collision avoidance.

关键词： Vehicle control model predictive control collision avoidance mixed integer programming

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Simulation and Optimization Models of Steady-state Gas Transmission Networks

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Energy Procedia 2015年 64卷 130-139页

作者： Alfredo Bermúdez Julio González-Díaz Francisco J. González-Diéguez Ángel M. González-Rueda María P. Fernández de Córdoba Dpt. of Applied Mathematics. University of Santiago de Compostela Campus Vida Santiago de Compostela 15782 Spain Dpt. of Statistics and Operations Research University of Santiago de Compostela Campus Vida Santiago de Compostela 15782 Spain

Managing a gas transport network is a complex problem because of the number of possibilities of routing the gas through the pipes. The most important aim in this kind of systems is to fulfill the demand within the pressure bounds, independently of its associated costs. However, in the present work some cost drivers are also taken into account by means of different objective functions in order to manage the network in an efficient way. This work deals with mathematical modeling and optimization of gas transport networks, where a two-stage procedure is proposed. In the first stage, optimization algorithms based on mathematical programming are applied to make some decisions (whether to activate compressor stations, control valves and other control elements) and gives an initial solution to the second stage. This last stage, which is based on control theory techniques, refines the solution to obtain more accurate results. Due to the reduced complexity in each stage, both can be solved within reasonable runtimes for relatively large gas networks. Based on the mathematical methods involved, a software called GANESO TM has been developed.

关键词： gas network optimization mixed integer programming nonlinear problem sequential linear programming control theory

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Physician scheduling for emergency telemedicine across multiple facilities

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IISE TRANSACTIONS ON HEALTHCARE SYSTEMS ENGINEERING 2023年第3期13卷 182-197页

作者： Olanrewaju, Oluwasegun G. Erkoc, Murat Univ Miami Dept Ind & Syst Engn Coral Gables FL 33124 USA Univ Miami Dept Ind & Syst Engn Coral Gables FL 33146 USA

Telemedicine has emerged as an effective means to connect health service providers with patients remotely. In the context of emergency care, telemedicine typically involves a telemedicine service hub (TSH) that matches remote physicians with patients in emergency wards. Effective operation of such systems requires careful and continuous coordination between physicians and local providers and as such, physician staffing and scheduling is a major managerial challenge that a TSH has to overcome. In this context, our paper studies a setting, where the TSH must respond to an arriving emergency case by assigning a physician within a considerably short time window. An emergency case at a facility can be matched only with a physician who is credentialed at that facility. Since care is urgent, queuing patients is not an option when there is no available on-shift physician. In such cases, the system must invoke the off-shift physicians, which is referred to as "blast." The telemedicine company tries to avoid this option due to high costs. We propose a novel integer programming model for generating physician schedules with optimal mix of credentials and coverage across multiple hospitals. The proposed model aims to minimize total costs under a chance constraint that limits the blast probabilities and other tactical constraints that are unique to this setting. Two fast acting heuristic-based solution approaches are developed for real-life size problems and their computational performances were demonstrated via numerical analyses.

关键词： Physician scheduling healthcare emergency telemedicine mixed integer programming genetic algorithm

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Designing multi-phased CO2 capture and storage infrastructure deployments

RENEWABLE AND SUSTAINABLE ENERGY TRANSITION

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RENEWABLE AND SUSTAINABLE ENERGY TRANSITION 2022年 2卷

作者： Jones Jr., Erick C. Yaw, Sean Bennett, Jeffrey A. Ogland-Hand, Jonathan D. Strahan, Cooper Middleton, Richard S. Univ Texas Arlington Arlington TX USA Montana State Univ Sch Comp Bozeman MT 59717 USA Carbon Solut LLC Bloomington IN USA

CO[sub]2[/sub] capture and storage (CCS) is a climate change mitigation strategy aimed at reducing the amount of CO[sub]2[/sub] vented into the atmosphere by capturing CO[sub]2[/sub] emissions from industrial sources, transporting the CO[sub]2[/sub] via a dedicated pipeline network, and injecting it into geologic reservoirs. Designing CCS infrastructure is a complex problem requiring concurrent optimization of source selection, reservoir selection, and pipeline routing decisions. Current CCS infrastructure design methods assume that project parameters including costs, capacities, and availability, remain constant throughout the project's lifespan. In this research, we introduce a novel, multi-phased, CCS infrastructure design model that allows for analysis of more complex scenarios that allow for variations in project parameters across distinct phases. We demonstrate the efficacy of our approach with theoretical analysis and an evaluation using real CCS infrastructure data.

关键词： Carbon capture and sequestration Infrastructure optimization mixed integer programming

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Collaborative optimization of depot location, capacity and rolling stock scheduling considering maintenance requirements

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Zhong, Qingwei Yu, Yingxue Huang, Yiru Li, Wenxin Zhang, Yongxiang Yan, Xu Civil Aviat Flight Univ China Coll Air Traff Management Guanghan 618307 Peoples R China Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehic Xiangyang 441053 Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Natl United Engn Lab Integrated & Intelligent Tran Chengdu 611756 Sichuan Peoples R China

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed.

关键词： Depot location and capacity Rolling stock scheduling Maintenance requirements mixed integer programming

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