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Effects of binary variables in mixed integer linear programming based unit commitment in large-scale electricity markets

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ELECTRIC POWER SYSTEMS RESEARCH 2018年 160卷 429-438页

作者： Alemany, Juan Kasprzyk, Leszek Magnago, Fernando Rio Cuarto Natl Univ Power Syst Anal Grp Rio Cuarto Argentina Poznan Univ Tech Inst Elect & Elect Ind Poznan Poland Nexant Inc Chandler AZ USA

mixed integer linear programming is one of the main approaches used to solve unit commitment problems. Due to the computational complexity of unit commitment problems, several researches remark the benefits of using less binary variables or relaxing them for the branch-and-cut algorithm. However, integrality constraints relaxation seems to be case dependent because there are many instances where applying it may not improve the computational burden. In addition, there is a lack of extensive numerical experiments evaluating the effects of the relaxation of binary variables in mixed integer linear programming based unit commitment. Therefore, the primary purpose of this work is to analyze the effects of binary variables and compare different relaxations, supported by extensive computational experiments. To accomplish this objective, two power systems are used for the numerical tests: the IEEE118 test system and a very large scale real system. The results suggest that a direct link between the relaxation of binary variables and computational burden cannot be easily assured in the general case. Therefore, relaxing binary variables should not be used as a general rule-of-practice to improve computational burden, at least, until each particular model is tested under different load scenarios and formulations to quantify the final effects of binary variables on the specific UC implementation. The secondary aim of this work is to give some preliminary insight into the reasons that could be supporting the binary relaxation in some UC instances. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Binary variables relaxation Branch and cut algorithm Day-ahead electricity market clearing mixed integer linear programming Unit commitment

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mixed integer linear programming models for optimal crop selection

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COMPUTERS & OPERATIONS RESEARCH 2017年 81卷 26-39页

作者： Filippi, Carlo Mansini, Renata Stevanato, Elisa Univ Brescia Dept Econ & Management Contrada S Chiara 50 I-25122 Brescia Italy Univ Brescia Dept Informat Engn Via Branze 38 I-25123 Brescia Italy

In this paper, we propose the modeling of a real-case problem where a farmer has to optimize the use of his/her land by selecting the best mix of crops to cultivate. Complexity of the problem is due to the several factors that have to be considered simultaneously. These include the market prices variability of harvested products, the specific resource requests for each crop, the restrictions caused by limited machines availability, and the timing of operations required to complete each crop cultivation. We provide two different mathematical formulations for the analyzed problem. The first one represents a natural integer programming formulation looking for the crop-mix that maximizes the farmer's expected profit measured as the difference between revenues obtained by selling the harvested products and the production costs. Since the revenue of each crop depends on the price as quoted at the exchange market and the yield per hectare of harvested product, we define it as a random variable. Then, the second model uses the maximization of the Conditional Value-at-Risk (CVaR) as objective function and looks for the crop-mix that allows to maximize the average expected profit under a predefined quantile of worst realizations. To test and compare the proposed models with the cultivation choice made by the farmer, we use Italian historical data represented by monthly returns of different crops over a time period of 16 years. Computational results emphasize the advantage of using the CVaR model for a risk-averse farmer and provide interesting insights for farmers involved in similar problems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Agriculture Crop selection mixed integer linear programming Conditional Value-at-Risk

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Disassembly line balancing with sequencing decisions: A mixed integer linear programming model and extensions

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JOURNAL OF CLEANER PRODUCTION 2019年 238卷

作者： Edis, Emrah B. Ilgin, Mehmet Ali Edis, Rahime Sancar Manisa Celal Bayar Univ Dept Ind Engn Sehit Prof Dr Ilhan Varank Kampusu TR-45140 Yunusemre Manisa Turkey

Due to the acceleration of technological developments and shortening of product life cycles, product recovery has gained great importance in recent years. Disassembly line balancing (DLB) problem is one of the most important problems encountered during disassembly operations in product recovery. In this study, a single model and complete DLB problem with balancing issues, hazardousness of parts, demand quantities and direction changes is considered. Majority of DLB studies in the literature solve this problem using heuristics or metaheuristics which do not guarantee the optimality. Although a few studies present mathematical formulations for some variants of this problem, they prefer to solve the problem by using heuristics or metaheuristics due to the non-linear structure and combinatorial nature of the problem. In this study, a generic mixed integer linear programming (MILP) model is developed for the investigated problem and its performance is tested through a series of benchmark instances. The computational results demonstrate that the proposed MILP model is able to solve test instances with up to 30 tasks. Hence, it can effectively be utilized to evaluate the optimality performance of DLB approaches. Moreover, several extensions on the MILP model regarding to line balancing, hazardousness and demand of parts and direction changes are proposed and their effects are analyzed through computational studies. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Disassembly line balancing Sequencing mixed integer linear programming

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mixed integer linear programming for quality of service optimization in Clouds

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 71卷 1-17页

作者： Guerout, Tom Gaoua, Yacine Artigues, Christian Da Costa, Georges Lopez, Pierre Monteil, Thierry Univ Toulouse LAAS CNRS INSA Toulouse France IRIT Toulouse Univ 118 Route Narbonne F-31062 Toulouse 9 France

The analysis of the Quality of Service (QoS) level in a Cloud Computing environment becomes an attractive research domain as the utilization rate is daily higher and higher. Its management has a huge impact on the performance of both services and global Cloud infrastructures. Thus, in order to find a good trade-off, a Cloud provider has to take into account many QoS objectives, and also the manner to optimize them during the virtual machines allocation process. To tackle this complex challenge, this article proposed a multiobjective optimization of four relevant Cloud QoS objectives, using two different optimization methods: a Genetic Algorithm (GA) and a mixed integer linear programming (MILP) approach. The complexity of the virtual machine allocation problem is increased by the modeling of Dynamic Voltage and Frequency Scaling (DVFS) for energy saving on hosts. A global mixed-integer non linear programming formulation is presented and a MILP formulation is derived by linearization. A heuristic decomposition method, which uses the MILP to optimize intermediate objectives, is proposed. Numerous experimental results show the complementarity of the two heuristics to obtain various trade-offs between the different QoS objectives. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cloud Computing Quality of Service Multiobjective optimization mixed integer linear programming Genetic Algorithm DVFS

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A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第1期57卷 61-82页

作者： Chansombat, Sirikarn Pongcharoen, Pupong Hicks, Christian Naresuan Univ Ctr Operat Res & Ind Applicat Fac Engn Phitsanulok Thailand Newcastle Univ Business Sch Newcastle Upon Tyne Tyne & Wear England

The scheduling literature is extensive, but much of this work is theoretical and does not capture the complexity of real world systems. Capital goods companies produce products with deep and complex product structures, each of which requires the coordination of jobbing, batch, flow and assembly processes. Many components require numerous operations on multiple machines. Integrated scheduling problems simultaneously consider two or more simultaneous decisions. Previous production scheduling research in the capital goods industry has neglected maintenance scheduling and used metaheuristics with stochastic search that cannot guarantee an optimal solution. This paper presents a novel mixed integer linear programming model for simultaneously solving the integrated production and preventive maintenance scheduling problem in the capital goods industry, which was tested using data from a collaborating company. The objective was to minimise total costs including: tardiness and earliness penalty costs;component and assembly holding costs;preventive maintenance costs;and set-up, production, transfer and production idle time costs. Thus, the objective function and problem formulation were more extensive than previous research. The tool was successfully tested using data obtained from a collaborating company. It was found that the company's total cost could be reduced by up to 63.5%.

关键词： manufacturing and maintenance integrated scheduling capital goods mixed integer linear programming

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An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2019年第4期27卷 887-904页

作者： Toloo, Mehdi Tavana, Madjid Santos-Arteaga, Francisco J. VSB Tech Univ Ostrava Fac Econ Dept Syst Engn Ostrava Czech Republic La Salle Univ Business Syst & Analyt Dept Business Analyt Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Free Univ Bolzano Fac Econ & Management Bolzano Italy

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.

关键词： Data envelopment analysis Common set of weights mixed integer linear programming Most efficient DMU

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Long-term Planning Model for a CHP System Using Particle Swarm Optimization algorithm and mixed integer linear programming

Long-term Planning Model for a CHP System Using Particle Swa...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Takada, Koji Ikegami, Takashi Tokyo Univ Agr & Technol Dept Ind Technol & Innovat Tokyo Japan Tokyo Univ Agr & Technol Dept Biofunct & Syst Sci Tokyo Japan

ISBN: (纸本)9781538653265

The use of distributed energy system (DES), such as combined heat and power (CHP) and district heating and cooling system, is spreading. DES is also indispensable for introducing renewable energy. Although introduction cost of renewable energy is high, the generation cost is low. Hence, there is a possibility that marginal fuel cost will be affected an electricity charge. Some external factors can influence on the capacity planning of DES. Therefore, we developed the model to optimize equipment capacities and long-term operations of DES. This planning approach is used two methods, using particle swarm optimization (PSO) and mixed integer linear programming (MILP). The long-term optimization results show that there are changes in the installed capacity and operation.

关键词： distributed energy system combined heat and power long-term optimal operation particle swarm optimization mixed integer linear programming

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A mixed integer linear programming based optimal home energy management scheme considering grid-connected microgrids 14

A mixed integer linear programming based optimal home energy...

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14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Aslam, Sheraz Javaid, Nadeem Asif, Muhammad Iqbal, Umar Iqbal, Zafar Sarwar, Mian Ahmer COMSATS Inst Informat Technol Islamabad Pakistan Virtual Univ Pakistan Lahore Pakistan PMAS Arid Agr Univ Rawalpindi 46000 Pakistan

ISBN: (纸本)9781538620700

In this paper, we propose a home energy management (HEM) scheme in the residential area for electricity cost and peak to average ratio (PAR) reduction. Furthermore, reduction in imported electricity from the external grid is also the objective of this study. Our proposed scheme schedules smart appliances as well as electrical vehicles (EVs) charging/discharging optimally according to the consumer preferences. Each consumer has its own grid-connected microgrid for electricity generation;which consists of wind turbine, solar panel, micro gas turbine (MGT) and energy storage system (ESS). Furthermore, the scheduling problem is mathematically formulated and solved by mixed integer linear programming (MILP). We also provide the comparison of the optimal solutions, while considering EVs with and without discharging capabilities. Findings from simulations affirm our proposed scheme in terms of above-mentioned objectives.

关键词： Demand side management home energy management mixed integer linear programming

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Temporal Pattern Localization using mixed integer linear programming 24

Temporal Pattern Localization using Mixed Integer Linear Pro...

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24th International Conference on Pattern Recognition (ICPR)

作者： Zhao, Rui Schalk, Gerwin Ji, Qiang RPI ECSE Dept Troy NY 12180 USA NYS Dept Hlth Wadsworth Ctr Albany NY USA

ISBN: (纸本)9781538637883

In this paper, we consider the problem of localizing the subsequence in time series which contains the dynamic pattern of interest. This is motivated by brain computer interface application where we need to analyze the dynamic pattern of brain signals in response to external stimulus. We treat the localization as a binary label assignment problem and formalize a mixed integer linear programming (MILP) problem. The optimal solution is obtained by minimizing a cost function associated with label assignment subject to empirical constraints induced by data acquisition process. We first experiment with synthetic data to evaluate the effectiveness of the proposed MILP formulation and achieve 10.8 % improvement on F-1-score. We then experiment with electrocorticographic (ECoG) data for a classification task and achieve 8.8 % improvement on accuracy using subsequences localized by our method compared to other methods.

关键词： Time series analysis Hidden Markov models Data collection Optimization Probabilistic logic mixed integer linear programming Pattern recognition

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Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed mixed integer linear programming

Multi-Intersection Traffic Management for Autonomous Vehicle...

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American Control Conference

作者： Ashtiani, Faraz Fayazi, S. Alireza Vahidi, Ardalan Clemson Univ Dept Mech Engn Clemson SC 29634 USA

ISBN: (纸本)9781538654286

This paper extends our previous work in I and [2] on optimal scheduling of autonomous vehicle arrivals at intersections, from one to a grid of intersections. A scalable distributed mixed integer linear Program (MILP) is devised that solves the scheduling problem for a grid of intersections. A computational control node is allocated to each intersection and regularly receives position and velocity information from subscribed vehicles. Each node assigns an intersection access time to every subscribed vehicle by solving a local MILP. Neighboring intersections will coordinate with each other in real-time by sharing their solutions for vehicles' access times with each other. Our proposed approach is applied to a grid of intersections and its positive impact on traffic flow and vehicles' fuel economy is demonstrated in comparison to conventional intersection control scenarios.

关键词： Safety Autonomous vehicles Real-time systems Acceleration mixed integer linear programming Optimal scheduling"

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