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21st International Conference on Information Fusion (FUSION)

作者： Date, Ketan Nagi, Rakesh Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA

ISBN: (纸本)9780996452762

In this paper, we propose an integer programming based model for tracking multiple maneuverable targets in a planar region. The objective function of this model uses both pairs and triplets of observations, which offer more accurate representation for constant velocity targets. Triplet scores in this model are calculated using a novel approach based on cubic spline interpolation, while the data association problem is solved using a specialized multi-dimensional assignment formulation. We show that the spline interpolation based scoring model provides more accurate reconstruction of trajectories, when compared to a naive model based on linear interpolation, on various randomly generated trajectories, at the expense of modest increase in computation time. The proposed multi-dimensional assignment formulation has nice structural properties and tight linear programming relaxation bound, which results in small computation times.

关键词： Multi-target tracking maneuvering targets mixed-integer linear programming data association spline interpolation

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Location of Electric Vehicle Charging Stations Under Uncertainty on the Driving Range 9th

Location of Electric Vehicle Charging Stations Under Uncerta...

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9th International Conference on Computational Logistics (ICCL)

作者： Boujelben, Mouna Kchaou Gicquel, Celine UAE Univ Coll Business & Econ POB 15551 Al Ain U Arab Emirates Univ Paris Saclay LRI F-91405 Orsay France

ISBN: (纸本)9783030008987;9783030008970

We study the problem of locating electric vehicle (EV) charging stations on road networks. We consider that the driving range, i.e. the maximum distance that a fully charged EV can travel before its battery runs empty, is subject to uncertainty and seek to maximize the expected coverage of the recharging demand. We first propose a new mixed-integer linear programming formulation for this stochastic optimization problem and compare it with a previously published one. We then develop a tabu search heuristic procedure to solve large-size instances of the problem. Our numerical experiments show that the new formulation leads to a better performance than the existing one and that the tabu search heuristic provides good quality solutions within short computation times.

关键词： Flow refueling location problem Electric vehicle charging station network design Stochastic driving range mixed-integer linear programming Tabu search

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Operational optimization of energy systems, 25 years - established and promising use cases 16

Operational optimization of energy systems, 25 years - estab...

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16th International Symposium on District Heating and Cooling (DHC)

作者： Stange, Peter Matthees, Anja Sander, Thomas Tech Univ Dresden Inst Power Engn Chair Bldg Energy Syst & Heat Supply Helmholtzstr 14 D-01069 Dresden Germany

The operation of complex energy systems for the supply of heat and electricity leads to several questions regarding their optimal control, e.g. when to use which generator, when to load or unload energy storages or when to buy or sell energy. Usually it is a complex task to answer these questions with the aim of optimizing a specific objective and respecting all arising physical, technical and economic constraints. Since 25 years we are solving this problem for an energy provider of a medium-sized city with the aim of minimizing the operational costs. For this purpose, an own modelled mixed-integer linear optimization problem (MILP) has to be solved in association to the continuous operation of the energy system. The model includes but is not limited to several combined heat and power generators, heat accumulators, steam generators and auxiliary coolers. In this presentation we will give an outline about the wide range of given conditions that are successfully implemented for this application. Further we show our approach to generate realistic heat demand and power consumption forecasts which are both essential preconditions for obtaining reliable optimization results. In addition to the well established MILP model in this specific use case we will outline some further promising applications of mathematical optimization in the context of energy systems. This includes the more precise modelling of energy storages, the computation of the optimal design of energy systems and the consideration of different or multiple targets in optimization. Moreover, we outline the problem of uncertain boundary conditions due to the growing amount of temporally hard to predict energy production and demand. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： optimization of operation mixed-integer linear programming energy supply district heating demand forecast

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Comparison of optimization solvers in the model predictive control of a PV-battery-heat pump system 12

Comparison of optimization solvers in the model predictive c...

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12th International Renewable Energy Storage Conference (IRES)

作者： Gelleschus, Ronny Boettiger, Michael Stange, Peter Bocklisch, Thilo Tech Univ Dresden Chair Energy Storage Syst Helmholtzstr 9 D-01069 Dresden Germany Tech Univ Dresden Chair Bldg Energy Syst & Heat Supply Helmholtzstr 14 D-01069 Dresden Germany

This paper presents a model predictive control approach for a home energy system with a heat pump, a thermal storage, a photovoltaic system and a battery. The modeling of the system in the mixed-integer linear programming framework is demonstrated and results of a one-year simulation with real measured PV and electric load data are shown. Different solution strategies for the underlying optimization problem are presented. The strategies are compared with respect to their performance and reliability. In this rather complex case branch & cut-based algorithms performed best in solving the optimization problem. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Energy Management System Model Predivtive Control Energy Storage Sector Coupling mixed-integer linear programming Genetic Algorithm Dynamic programming

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SDP Based Unit Commitment Via Moment Relaxation

SDP Based Unit Commitment Via Moment Relaxation

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North American Power Symposium (NAPS)

作者： Li, Yikui Wu, Lei Li, Jie Clarkson Univ Dept Elect & Comp Engn Potsdam NY 13699 USA

ISBN: (纸本)9781538671382

Unit commitment (UC) problems are the most fundamental problems that system operators solve every day in both day-ahead and real-time markets to guarantee secure and economic operation. UC problems are usually formulated as a mixed-integer linear programming (MILP) or a mixed-integer quadratic programming (MIQP) model with binary variables representing ON/OFF statuses of generators, and is solved via Branch and bound (B&B) type algorithms. With the prosperity of applying semidefinite programing (SDP) in the power system field, researchers attempt to solve UC under the SDP framework by relaxing integrality requirements, seeking an optimal solution or a better lower bound than the traditional linear programming (LP) relaxation. This paper uses 2-order moment relaxation technique to reformulate UC problems as an SDP model, which can be solved by an interior point method. Considering significant computational burden by 2-order moment relaxation, a variable reduction strategy and two refined moment relaxation based UC models are further applied. The relationship among the proposed models in terms of their tightness is studied. In addition, a sufficient condition under which a solution is exact is stated. Numerical studies illustrate effect of the proposed models and potential applicability of the proposed models is also discussed.

关键词： Unit commitment Semidefinite programming Moment relaxation mixed-integer linear programming

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A Continuous-Time MILP Formulation for the Multi-Mode Resource-Constrained Project Scheduling Problem

A Continuous-Time MILP Formulation for the Multi-Mode Resour...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Gnagi, M. Rihm, T. Trautmann, N. Univ Bern Dept Business Adm Bern Switzerland

ISBN: (纸本)9781538667866

The well-known multi-mode resource-constrained project scheduling problem aims at selecting for each project task a start time and an execution mode to obtain a precedence-and resource-feasible schedule with minimal project duration. The available execution modes for the tasks differ in their durations and demands for some scarce resources. Numerous problem-specific solution methods and several mixed-integer linear programming (MILP) formulations have been described in the literature. We introduce a new continuous-time MILP formulation that employs continuous start-time variables and three types of binary variables: mode-selection, resource-assignment and sequencing variables. The results of our computational analysis indicate that the proposed formulation achieves superior performance than two formulations from the literature when the range of the tasks' durations is relatively high.

关键词： Operations Research Project Management Multi-Mode Resource-Constrained Project Scheduling Problem mixed-integer linear programming

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Ground stations networks for Free-Space Optical communications: maximizing the data transfer

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Electronic Notes in Discrete Mathematics 2018年 64卷 255-264页

作者： Capelle, Mikaël Huguet, Marie-José Jozefowiez, Nicolas Olive, Xavier Thales Alenia Space France Toulouse France LAAS-CNRS Université de Toulouse CNRS INSA Toulouse France

Free-space optical communications are becoming a mature technology, but unlike current radio-frequency technologies, they are strongly impacted by clouds. In this paper, we aim to find a network of optical ground stations maximizing the amount of data that can be sent from a low-earth orbiting satellite to the Earth during its missions, taking into consideration cloud information. We present a mixed integer linear Program (MILP) and a hierarchical method based on an exhaustive enumeration of the sets of ground stations and on a dynamic programming algorithm and compare them on real scenarios based on archived years of cloud data. Even if the MILP can solve scenarios over small time horizons in less than one hour, experiments show that the hierarchical approach outperforms it in term of CPU time while achieving optimality. © 2018 Elsevier B.V.

关键词： Dynamic programming Free-Space Optical Communications mixed-integer linear programming Optical Ground Station Network Optimisation

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HEURISTIC AND EXACT METHODS FOR THE LARGE-SCALE DISCRETE TIME-COST TRADE-OFF PROBLEMS

HEURISTIC AND EXACT METHODS FOR THE LARGE-SCALE DISCRETE TIM...

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作者： SAMAN AMINBAKHSH MIDDLE EAST TECHNICAL UNIVERSITY

学位级别：博士

Construction industry necessitates formulating impeccable plans by decision makers for securing optimal outcomes. Managers often face the challenge of compromising between diverse and usually conflicting objectives. Particularly, accurate decisions on the time and cost must be made in every construction project since project success is chiefly related to these objectives. This is realized by addressing the time-cost trade-off problem (TCTP) which is an optimization problem and its objective is to identify the set of time-cost alternatives that provide the optimal schedule(s). Due to discreteness of many resources in realistic projects, discrete version of this problem (DTCTP) is of great practical relevance. The Pareto front extension of DTCTP is a multi-objective optimization problem that facilities preference articulation of decision makers by providing them with a set of mutually non-dominated solutions of same quality. Due to the complex nature of DTCTP, the literature on large-scale problems is virtually void; besides, most of the existing methods do not suit actual practices and popular commercial planning software lack tools for solution of DTCTP. The main focus of this thesis relates to providing means for optimization of real- life-scale Pareto oriented DTCTPs and it aims to contribute to both researchers and practitioners by tightening the gap between the literature and the real-world requirements of the projects. The results of the comparative studies reveal that the proposed methods are successful for solving large-scale DTCTPs and provide the management with a quantitative basis for decisions on selection of the proper alternatives for the real-life-scale construction projects.

关键词： Discrete Time-Cost Trade-Off Problem Heuristic mixed-integer linear programming Pareto Front Particle Swarm Optimization

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Modeling Demand Propagation in Guaranteed Service Models

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IFAC-PapersOnLine 2018年第2期51卷 284-289页

作者： Löhnert M. Rambau J. DATEV eG Nürnberg Germany University of Bayreuth Bayreuth 95440 Germany

In this paper, the class of guaranteed service models for multi-echelon inventory management is enhanced with explicit demand propagation. More specifically, the known mixed integer linear programming formulation for the guaranteed service model is refined by new variables and restrictions so that it describes the internal demand propagation exactly for linear demand bound functions. With this feature, aspects like outsourcing as well as decision-dependent stochastic demands at internal stock points can be expressed exactly. The relevance of the new model is shown in an illustrative example, where the new model is able to find a solution with almost 40% lower actual cost compared to the existing approximative model without explicit demand propagation. © 2018

关键词： demand propagation lost demand mixed-integer linear programming multi-echelon inventory management outsourcing

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Applications of train routing selection methods for real-time railway traffic management 21

Applications of train routing selection methods for real-tim...

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21st IEEE International Conference on Intelligent Transportation Systems (ITSC)

作者： Sama, Marcella D'Ariano, Andrea Pacciarelli, Dario Pellegrini, Paola Rodriguez, Joaquin Roma Tre Univ Dept Engn Rome Italy Univ Lille Nord France IFSTTAR COSYS LEOSTESTAS Villeneuve Dascq France

ISBN: (纸本)9781728103235

The real-time railway traffic management problem (rtRTMP) aims to solve time-overlapping conflicting track requests due to traffic disturbances. The size of the problem and the time required to solve it are affected by the number of routing alternatives available to each train. The real-time train routing selection problem (rtTRSP) chooses a feasible routing subset for each train to use as input for the rtRTMP. Recently, a computational analysis has been performed via Ant Colony Optimization and the RECIFE-MILP solver. This paper generalizes such analysis by considering a different rtRTMP model, objective function and solution approach. We adopt the AGLIBRARY solver, which is based an alternative graph model of the problem and minimizes the maximum consecutive delay. The aim is to develop real-time disturbance response strategies and to quantify the advantages of the selection of a subset of routings when using different solvers. We analyze how changes in the rtRTMP model are reflected in the rtTRSP and which modifications are required. The computational analysis is performed on two French infrastructures: the line around the city of Rouen and the Lille terminal station area. The analysis shows that solving the rtTRSP helps both solvers significantly, even if they are based on different models, objectives and algorithms.

关键词： Public Transport Train Rescheduling Ant Colony Optimization mixed-integer linear programming

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