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Offline segmentation of spatio-temporal order trajectories by mixed-integer linear programming for determining process times in production systems 55

Offline segmentation of spatio-temporal order trajectories b...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Volk, Maximilian Mieth, Carina Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany TRUMPF Machine Tools Inc. Johann-Maus-Straße 2 Ditzingen 71254 Germany TU Dortmund University Emil-Figge-Straße 61 Dortmund44227 Germany

Real-time indoor positioning systems in manufacturing systems are used to track production orders. This generates spatio-temporal trajectories which can be segmented to determine process times. We present formulations of the offline segmentation problem as mixed-integer linear programs (MILPs) that utilize the sequence of processing steps from ERP systems. The MILP formulations are compared with online heuristics in terms of their accuracy and computational effort on data generated with features from a real job shop. We show that in terms of accuracy our offline segmentation formulations outperform the online heuristics with increasing measurement errors, justifying their higher computational effort. © 2022 The Authors. Published by Elsevier B.V.

关键词： indoor positiong systems mixed-integer linear programming process time estimation spatio-temporal production data trajectory segmentation

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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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Automated reinforcement trim waste optimization in RC frame structures using building information modeling and mixed-integer linear programming

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AUTOMATION IN CONSTRUCTION 2021年 124卷 103599-103599页

作者： Khondoker, Md. Tareq Hossain Khulna Univ Engn & Technol Dept Bldg Engn & Construct Management Khulna 9203 Bangladesh

Construction of RC frame requires a considerable amount of reinforcement steel bars. Shaping market length rebars to get design lengths leads to the production of waste. The purpose of this research is to optimize the use of available market length rebars in such a way that will generate the least possible waste. Accordingly, Building Information Modeling (BIM) based automated framework integrated with mixed-integer linear programming (MILP) has been presented in this article. This methodology involves extracting reinforcement detailing data automatically during the design phase using BIM. Then, implementing the column generation algorithm to generate only necessary cutting patterns. Then, optimizing the use of market length rebars by applying the MILP approach. An RC frame of a three-storied building has been analyzed to evaluate the efficiency of the proposed framework. The results show that a substantial amount of trim waste reduction can be achieved quickly and efficiently by the suggested method.

关键词： Building information modeling mixed-integer linear programming Trim waste Optimization Reinforcement steel bars

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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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Support vector machines within a bivariate mixed-integer linear programming framework

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Warwicker, John Alasdair Rebennack, Steffen Karlsruhe Inst Technol Inst Operat Res IOR D-76185 Karlsruhe Baden-wurttembe Germany

Support vector machines (SVMs) are a powerful machine learning paradigm, performing supervised learning for classification and regression analysis. A number of SVM models in the literature have made use of advances in mixed-integer linear programming (MILP) techniques in order to perform this task efficiently. In this work, we present three new models for SVMs that make use of piecewise linear (PWL) functions. This allows effective separation of data points where a simple linear SVM model may not be sufficient. The models we present make use of binary variables to assign data points to SVM segments, and hence fit within a recently presented framework for machine learning MILP models. Alongside presenting an inbuilt feature selection operator, we show that the models can benefit from robust inbuilt outlier detection. Experimental results show when each of the presented models is effective, and we present guidelines on which of the models are preferable in different scenarios.

关键词： Support vector machine Optimisation mixed-integer linear programming Outlier detection Feature selection

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Solving the Lexicographic Multi-Objective mixed-integer linear programming Problem using branch-and-bound and grossone methodology

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COMMUNICATIONS IN NONlinear SCIENCE AND NUMERICAL SIMULATION 2020年 84卷 105177-105177页

作者： Cococcioni, Marco Cudazzo, Alessandro Pappalardo, Massimo Sergeyev, Yaroslav D. Univ Pisa Pisa Italy Univ Calabria Arcavacata Di Rende Italy Lobachevsky State Univ Nizhni Novgorod Nizhnii Novgorod Russia

In the previous work (see [1]) the authors have shown how to solve a Lexicographic Multi-Objective linear programming (LMOLP) problem using the Grossone methodology described in [2]. That algorithm, called GrossSimplex, was a generalization of the well-known simplex algorithm, able to deal numerically with infinitesimal/infinite quantities. The aim of this work is to provide an algorithm able to solve a similar problem, with the addition of the constraint that some of the decision variables have to be integer. We have called this problem LMOMILP (Lexicographic Multi-Objective mixed-integer linear programming). This new problem is solved by introducing the GrossBB algorithm, which is a generalization of the Branch-and-Bound (BB) algorithm. The new method is able to deal with lower-bound and upper-bound estimates which involve infinite and infinitesimal numbers (namely, Grossone-based numbers). After providing theoretical conditions for its correctness, it is shown how the new method can be coupled with the GrossSimplex algorithm described in [1], to solve the original LMOMILP problem. To illustrate how the proposed algorithm finds the optimal solution, a series of LMOMILP benchmarks having a known solution is introduced. In particular, it is shown that the GrossBB combined with the GrossSimplex is able solve the proposed LMOMILP test problems with up to 200 objectives. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Lexicographic optimization mixed-integer linear programming Numerical infinitesimals Grossone methodology

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Optimal Bidding Scheduling of Virtual Power Plants Using a Dual-MILP (mixed-integer linear programming) Approach under a Real-Time Energy Market

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ENERGIES 2024年第15期17卷 3773页

作者： Yoon, Seung-Jin Ryu, Kyung-Sang Kim, Chansoo Nam, Yang-Hyun Kim, Dae-Jin Kim, Byungki Korea Inst Energy Res Power Elect Syst Res Lab Jeju 63357 South Korea

In recent years, the energy industry has increased the proportion of renewable energy sources, which are sustainable and carbon-free. However, the increase in renewable energy sources has led to grid instability due to factors such as the intermittent power generation of renewable sources, forecasting inaccuracies, and the lack of metering for small-scale power sources. Various studies have been carried out to address these issues. Among these, research on Virtual Power Plants (VPP) has focused on integrating unmanaged renewable energy sources into a unified system to improve their visibility. This research is now being applied in the energy trading market. However, the purpose of VPP aggregators has been to maximize profits. As a result, they have not considered the impact on distribution networks and have bid all available distributed resources into the energy market. While this approach has increased the visibility of renewables, an additional method is needed to deal with the grid instability caused by the increase in renewables. Consequently, grid operators have tried to address these issues by diversifying the energy market. As regulatory method, they have introduced real-time energy markets, imbalance penalty fees, and limitations on the output of distributed energy resources (DERs), in addition to the existing day-ahead market. In response, this paper proposes an optimal scheduling method for VPP aggregators that adapts to the diversifying energy market and enhances the operational benefits of VPPs by using two mixed-integer linear programming (MILP) models. The validity of the proposed model and algorithm is verified through a case study analysis.

关键词： optimal scheduling mixed-integer linear programming multi-energy market over-generation uncertainty of renewable energy sources virtual power plants

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Adaptive mixed-integer linear programming-Based Energy Management System of Fast Charging Station with Nuclear-Renewable Hybrid Energy System

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ENERGIES 2023年第2期16卷 685页

作者： Siddique, Abu Bakar Gabbar, Hossam A. Ontario Tech Univ UOIT Fac Energy Syst & Nucl Sci Oshawa ON L1H 7K4 Canada Ontario Tech Univ UOIT Fac Engn & Appl Sci Oshawa ON L1H 7K4 Canada

The concept of transportation electrification is proliferating due to its high impact on emission reduction. However, the increased usage of electric vehicles strains the power grid's charging infrastructure. As a result, to reduce demand on the power grid, lower the emissions, and solve the intermittency problem of Renewable Energy Sources (RESs), a Nuclear-renewable Hybrid Energy System (N-R HES) is proposed in this research to support the load demand of a Fast Charging Station (FCS). Fulfilling the power demand of the FCS while reducing the generation cost and waste of energy is a vital issue, and hence, energy management with optimization is a must for the hybrid energy system. To address this issue, a model reference adaptive control with a mixed-integer linear programming-based energy management method was modelled to accomplish the charging station's extensive performance. MATLAB/Simulink software has been used to model and simulate the proposed system, and the results are analyzed. The assessment shows that the proposed energy management system offers an optimized performance of the fast charging station integrating with nuclear and renewable energy.

关键词： nuclear-renewable hybrid energy system fast charging station energy management system optimization adaptive control mixed-integer linear programming

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Modified mixed-integer linear programming Formulation Implemented in Microsoft Excel to Synthesize a Heat Exchanger Network with Multiple Utilities to Compare Process Flowsheets

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PROCESSES 2023年第10期11卷 2840页

作者： Revelo, Maria F. Tuza, Pablo V. Minist Educ Unidad Educ Rafael Leon Carvajal Dist Educat Intercultural & Bilingue 10D03 Cotacac Coordinac Zonal 1 Cotacachi 100306 Ecuador Univ Tecn Ambato Fac Ciencia Ingn Alimentos & Biotecnol Ambato 180216 Ecuador

In the present work, a modified mixed-integer linear programming model was implemented in Microsoft Excel (R) and minimized using the Solver tool to obtain information to devise a heat exchanger network with multiple utilities from a set of hot and cold streams and selected utilities by hand. Regarding the mixed-integer linear programming problem, the summation of utility energy was added to the model, and this energy was equal to that from the Temperature Interval method and the Grand Composite Curve. Moreover, feasible temperature ranges for heat exchange were considered according to the second law of thermodynamics. Also, the last two temperature intervals from the rank-ordered ones were assigned for water energy balances. An energy balance was introduced into the algorithm for each interval between its temperature and the process pinch temperature in the case of the boiler-feed water. Seven stream sets collected from the literature were used for the mixed-integer linear programming formulation testing, and six of them are presented in this article. Because of boiler-feed water generation and the low cost of utilities, the annualized cost of a heat exchanger network with multiple utilities can be lower than that of a network without multiple utilities.

关键词： pinch heat exchanger network multiple utilities mixed-integer linear programming Microsoft Excel

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A comparison between mixed-integer linear programming and dynamic programming with state prediction as novelty for solving unit commitment

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 125卷 106426-106426页

作者： Putz, Dominik Schwabeneder, Daniel Auer, Hans Fina, Bernadette Tech Univ Wien Energy Econ Grp EEG Gusshausstr 25-29E370-3 A-1040 Vienna Austria Siemens AG Austria Siemensstr 90 A-1210 Vienna Austria AIT Austrian Inst Technol Giefinggasse 4 A-1210 Vienna Austria

Recently, the increasing prevalence of renewable energies has faced the challenge of operating power supply systems to efficiently plan electricity generation on a daily basis, since renewable energies are generated intermittently and the decisions of the individual generation units are discrete. The Unit Commitment (UC) problem, which determines the dispatch of generation units, is one of the critical problems in the operation of power supply systems. A long list of formulation proposals have been made that claim to solve this problem. For this purpose, two established approaches, mixed-integer linear programming (MILP) and backward dynamic programming (DP), are used as basis for a deterministic single-generator unit with general convex cost function in this paper. The DP algorithm is enhanced by a so-called state prediction, which reduces the time to find the optimal solution. The proposed formulation is tested empirically on the basis of existing formulations at long term profit based UC instance derived from real data. Finally, the calculation results show that the derived approach significantly shortens the computation time, which confirms the effectiveness of state prediction. The comparison of the approaches shows that the DP algorithm with state prediction delivers a satisfying solution in significantly less time than DP and MILP. Furthermore, the given linearity of the dependence of the computation time on number of steps is a superior advantage of the DP strategy. This superiority becomes even more evident when the planning horizon extends over a longer period of time.

关键词： Unit commitment Optimisation approach mixed-integer linear programming Dynamic programming Prediction algorithms State optimisation

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