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Resource Allocation Optimization Based on mixed integer linear programming in the Multi-Cloudlet Environment

IEEE ACCESS 2018年 6卷 24533-24542页

作者： Liu, Li Fan, Qi Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

Cloudlet provides services with low latency and high bandwidth. Some research addresses the resource allocation problem in the cloudlet-based mobile cloud computing environment. However, there are few works considering how to optimize resource allocation while satisfying users' requirements in multicloudlet situations. To solve this problem, a two-stage optimization strategy is proposed. First, a cloudlet selection model based on mixed integer linear programming (MILP) is proposed to obtain the cloudlet for mobile users by optimizing latency and mean reward. Second, a resource allocation model based on MILP is presented to allocate resources in the selected cloudlet by optimizing reward and mean resource usage. A comparison of resource allocation is analyzed with a cloudlet selection model based on MILP and an existing cloudlet selection strategy in the multi-cloudlet environment. From the simulation, our proposed strategy has better performance in terms of the access latency, the system reward and the resource usage.

关键词： Cloudlet selection mixed integer linear programming multi-cloudlet resource allocation

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Multi-parametric mixed integer linear programming under global uncertainty

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COMPUTERS & CHEMICAL ENGINEERING 2018年 116卷 279-295页

作者： Charitopoulos, Vassilis M. Papageorgiou, Lazaros G. Dua, Vivek UCL Ctr Proc Syst Engn Dept Chem Engn Torrington Pl London WC1E 7JE England

Major application areas of the process systems engineering, such as hybrid control, scheduling and synthesis can be formulated as mixed integer linear programming (MILP) problems and are naturally susceptible to uncertainty. Multi-parametric programming theory forms an active field of research and has proven to provide invaluable tools for decision making under uncertainty. While uncertainty in the righthand side (RHS) and in the objective function's coefficients (OFC) have been thoroughly studied in the literature, the case of left-hand side (LHS) uncertainty has attracted significantly less attention mainly because of the computational implications that arise in such a problem. In the present work, we propose a novel algorithm for the analytical solution of multi-parametric MILP (mp-MILP) problems under global uncertainty, i.e. RHS, OFC and LHS. The exact explicit solutions and the corresponding regions of the parametric space are computed while a number of case studies illustrates the merits of the proposed algorithm. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Optimisation under uncertainty Multi-parametric programming mixed integer linear programming Cylindrical algebraic decomposition Grobner bases Process scheduling

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Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming

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APPLIED ENERGY 2018年 210卷 944-963页

作者： Nemati, Mohsen Braun, Martin Tenbohlen, Stefan Siemens AG Humboldt St 59 D-90443 Nurnberg Germany Univ Kassel Fraunhofer IWES Kassel Germany Univ Stuttgart IEH Stuttgart Germany

Energy Management System (EMS) applications of modern power networks like microgrids have to respond to a number of stringent challenges due to current energy revolution. Optimal resource dispatch tasks must be handled with specific regard to the addition of new resource types and the adoption of novel modeling considerations. In addition, due to the comprehensive changes concerning the multi cell grid structure, new policies should be fulfilled via microgrids' EMS. At the same time achieving a variety of (conflicting) goals in different microgrids requires a universal and a multi criteria optimization tool. Few of recent works in this area have considered the different perspectives of network operation with high amount of constraints and decision criteria. In this paper two dispatch-optimizers for a centralized EMS (CEMS) as a universal tool are introduced. An improved real-coded genetic algorithm and an enhanced mixed integer linear programming (MILP) based method have been developed to schedule the unit commitment and economic dispatch of microgrid units. In the proposed methods, network restrictions like voltages and equipment loadings and unit constraints have been considered. The adopted genetic algorithm features a highly flexible set of sub-functions, intelligent convergence behavior, as well as diversified searching approaches and penalty methods for constraint violations. Moreover, a novel method has been introduced to deal with the limitations of the MILP algorithm for handling the non-linear network topology constraints. A new aging model of a Lithium-Ion battery based on an event driven aging behavior has been introduced. Ultimately, the developed GA-based and MILP-based optimizers have been applied to a test microgrid model under different operation policies, and the functionality of each method has been evaluated and compared together. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Microgrids Unit commitment Economic dispatch Genetic algorithm mixed integer linear programming

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mixed-integer linear programming models for the paint waste management problem

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 151卷 102343-102343页

作者： Wang, Juyoung Cevik, Mucahit Amin, Saman Hassanzadeh Parsaee, Amir Ali Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada

Hazardous wastes have significant negative impacts on environment and people, which make their management a prominent task. A general hazardous waste reverse logistics network consists of sources, collection centers, treatment centers, processing/recycling facilities, and disposal facilities. We study a reverse logistics network specifically for household hazardous wastes and examine its difference from a non-hazardous and industrial waste network. We consider multiobjective mixed-integer deterministic and stochastic mathematical models that are designed to answer the following questions: which facilities or centers should be opened, which routes should be utilized, and how much waste should be transported to each location in order to minimize the transportation cost, transportation/site risk, and to maximize household convenience for the purpose of participation increase at collection stage. Specifically, we propose an optimization framework for the management of hazardous household wastes, and consider the waste paint network in the City of Toronto, Ontario, Canada as a test bed for our analysis. Finally, we provide easy-to-interpret visualization tools for the problem, which help interpreting the model outcomes and identifying policy insights.

关键词： Reverse logistics Household hazardous waste mixed integer linear programming Paint Stochastic programming Multi-objective optimization

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mixed-integer-linear-programming Model for the Charging Scheduling of Electric Vehicle Fleets 10

Mixed-Integer-Linear-Programming Model for the Charging Sche...

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10th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Bodenschatz, Nicki Eider, Markus Berl, Andreas Tech Hsch Deggendorf Fac Appl Comp Sci Deggendorf Germany

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167602

The number of electric vehicles is steadily increasing of the past few years. This transition to electric vehicles bears the challenge, to integrate the charging processes into the grid without overstressing it. To prevent this, research has tackled lately the scheduling of electric vehicle charging. Especially the charging of electric vehicle fleets is in the focus of research. There are already different solution approaches to increase the grid stability, to increase the intake of locally produced renewable energy or simply to reduce the cost. However, all these solution approaches use different mathematical models with different parameters to represent the charging scheduling problem. This results in the problem that each model is applicable for a special use case only, other use cases might need other parameters for the scheduling of the electric vehicle fleet. To ease this problem, this paper provides a detailed mathematical model for the cost minimization of a general electric fleet in the form of a mixed-integer-linear-program. In order to do this, the paper shows that different research approaches use different parameters in their solutions. Afterwards, the paper presents a general overview of technical limitations for the electric fleets. On foundation of these limitations a mixed-integer-linear-program model for a wide range of electric fleets is established. Also, the paper provides options to extend the model in order to improve the result of an optimal schedule.

关键词： Mathematical model Electric vehicle fleet charging Scheduling mixed integer linear programming

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Grossone Methodology for Lexicographic mixed-integer linear programming Problems 3rd

Grossone Methodology for Lexicographic Mixed-Integer Linear ...

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3rd International Conference on Numerical Computations - Theory and Algorithms (NUMTA)

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

ISBN: (纸本)9783030406165

In this work we have addressed lexicographic multi-objective linear programming problems where some of the variables are constrained to be integer. We have called this class of problems LMILP, which stands for Lexicographic mixed integer linear programming. Following one of the approach used to solve mixed integer linear programming problems, the branch and bound technique, we have extended it to work with infinitesimal/infinite numbers, exploiting the Grossone Methodology. The new algorithm, called GrossBB, is able to solve this new class of problems, by using internally the GrossSimplex algorithm (a recently introduced Grossone extension of the well-known simplex algorithm, to solve lexicographic LP problems without integer constraints). Finally we have illustrated the working principles of the GrossBB on a test problem.

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

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Comparative Assessment of Priority Listing and mixed integer linear programming Unit Commitment Methods for Non-Interconnected Island Systems

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ENERGIES 2019年第4期12卷 657-657页

作者： Psarros, Georgios N. Papathanassiou, Stavros A. NTUA Sch Elect & Comp Engn Athens 15780 Greece

The generation management concept for non-interconnected island (NII) systems is traditionally based on simple, semi-empirical operating rules dating back to the era before the massive deployment of renewable energy sources (RES), which do not achieve maximum RES penetration, optimal dispatch of thermal units and satisfaction of system security criteria. Nowadays, more advanced unit commitment (UC) and economic-dispatch (ED) approaches based on optimization techniques are gradually introduced to safeguard system operation against severe disturbances, to prioritize RES participation and to optimize dispatch of the thermal generation fleet. The main objective of this paper is to comparatively assess the traditionally applied priority listing (PL) UC method and a more sophisticated mixed integer linear programming (MILP) UC optimization approach, dedicated to NII power systems. Additionally, to facilitate the comparison of the UC approaches and quantify their impact on systems security, a first attempt is made to relate the primary reserves capability of each unit to the maximum acceptable frequency deviation at steady state conditions after a severe disturbance and the droop characteristic of the unit's speed governor. The fundamental differences between the two approaches are presented and discussed, while daily and annual simulations are performed and the results obtained are further analyzed.

关键词： autonomous power systems frequency constraints islands isolated grids mixed integer linear programming priority listing unit commitment

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mixed integer linear programming Based Approaches for Medium-Term Planning and Scheduling in Multiproduct Multistage Continuous Plants

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2017年第19期56卷 5636-5651页

作者： Aguirre, Adrian M. Liu, Songsong Papageorgiou, Lazaros G. UCL Dept Chem Engn Ctr Proc Syst Engn Torrington Pl London WC1E 7JE England Swansea Univ Sch Management Bay CampusFabian Way Swansea SA1 8EN W Glam Wales

This paper addresses the planning and scheduling problem of a multiproduct multistage continuous plant by three novel MILP-based (mixed integer linear programming) models. These models combine a TSP (traveling salesman problem) formulation with the main ideas of general precedence and unit-specific general precedence concepts to provide hybrid discrete/continuous time representations of the system. Also, an efficient solution approach involving rolling horizon and iterative-improvement algorithm is derived for solving medium-size instances of the problem. Results analyses for different model's parameters demonstrate the benefits of the new formulations and the effectiveness of the solution approach presented in this work.

关键词： mixed integer linear programming ANALYTICAL samples (Chemistry) ITERATIVE methods (Mathematics) TRAVELING salesman problem (Mathematics) COMBINATORIAL optimization MATHEMATICAL programming

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Target setting with minimum improving costs in data envelopment analysis: A mixed integer linear programming approach

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EXPERT SYSTEMS 2019年第4期36卷

作者： Ang, Sheng An, Qingxian Yang, Feng Ji, Xiang Univ Sci & Technol China Sch Management Hefei Anhui Peoples R China Cent South Univ Sch Business Changsha 410083 Hunan Peoples R China

This study addresses a problem called cost-minimizing target setting in data envelopment analysis (DEA) methodology. The problem is how to make an inefficient decision-making unit efficient by allocating to it as few organizational resources as possible, assuming that the marginal costs of reducing inputs or increasing outputs are known and available, which is different from previous furthest and closest DEA targets setting methods. In this study, an existed cost minimizing target setting heuristics approach based on input-oriented model is examined to show that there exist some limitations. This study develops a simple mixed integer linear programming to determine the desired targets on the strongly efficient frontier based on non-oriented DEA model considering the situation in the presence of known marginal costs of reducing inputs and increasing outputs simultaneously. Some experiments with the simulated datasets are conducted, and results show that the proposed model can obtain more accurate projections with lower costs compared with those from furthest and closest target setting approaches. Besides, the proposed model can be realistic and efficient in solving cost-minimizing target setting problem.

关键词： closest targets data envelopment analysis mixed integer linear programming non-oriented model

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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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