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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Trpovski, Andrej Hamacher, Thomas TUMCREATE 1 CREATE Way10-02 CREATE Tower Singapore 138602 Singapore Tech Univ Munich Renewable & Sustainable Energy Syst D-85748 Garching Germany

ISBN: (纸本)9781728119816

This paper presents a scenario based mixed integer programming method to solve the transmission expansion planning problem with security constraints. To consider the N-1 line outages, the scenarios are introduced as a predefined set of parameters in the mathematical model. A DC power flow model is used as a simplified approach to represent the electrical system. The proposed expansion planning model is applied to the well known Garver 6 bus test system and a realistic 46 bus Brazilian test system. The versatility of the proposed method gives the freedom to test the expansion planning as a complete one-stage approach and as the common two-stage approach. The simulation results are presented to draw a conclusion of the performance of the proposed method.

关键词： Power System Planning Power Transmission mixed integer programming Optimization DC Power Flow Security Constraints

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A TWO LAYER mixed integer programming MODEL FOR DYNAMIC COMPOSITE WEBSERVICE SELECTION IN VIRTUAL ORGANIZATIONS INSPIRED BY LAYERING AS OPTIMIZATION DECOMPOSITION

A TWO LAYER MIXED INTEGER PROGRAMMING MODEL FOR DYNAMIC COMP...

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作者： ABIUD WAKHANU MULONGO University Of Nairobi

学位级别：博士

The key motivation for virtual organizations (VOs) is the need for business agility against a highly volatile and globally competitive market. The agility includes the ability to dynamically and efficiently package and deliver highly customized services that maximally satisfy the utility of service consumer demands over the Internet. Dynamic webservice composition (DWSC) is an essential Information Communication Technology (ICT) enabler of this form of agility in VOs. However, dynamic webservice composition remains a multiple criteria decision making (MCDM) nondeterministic polynomial (NP) hard optimization problem despite more than 10 years of extensive research. This makes the applicability of DWSC to problems of industrial relevance currently limited. mixed integer programming (MIP) is the most widely used technique in efficiently modelling the problem. There are two MIP models for the DWSC problem: a local planning strategy, herein L-MIP and a global planning strategy hereafter S-MIP. L-MIP is provably polynomial time and practically multiple times faster than S-MIP. However L-MIP lacks the ability to capture global inter workflow task webservice Quality of Service (QoS) constraints and generally is less optimal relative to S-MIP. It has been demonstrated that L-MIP generates composite webservices that are 20% to 30% worse in quality with respect to S-MIP. S-MIP on the other hand guarantees global optimality but is susceptible to exponential state space explosion, making the strategy limited to problems in which the number of service providers per business workflow task n is small. This thesis aimed to design a DWSC MIP global planning strategy that is more efficient than S- MIP. The second objective was to evaluate the performance of the proposed strategy versus S-MIP and L-MIP in terms of runtime efficiency and solution quality. The study proposed a two layer MIP model dubbed SLUM: Service Layered Utility Maximization. SLUM is inspired by the theory of Layerin

关键词： Dynamic Composite Webservice Selection mixed integer programming Layering Optimization Decomposition Virtual Organizations

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mixed integer programming Based Nested Partition Algorithm for Facility Location Optimization Problems

Mixed Integer Programming Based Nested Partition Algorithm f...

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2008 IEEE International Conference on Service Operations and Logistics, and Informatics(IEEE/SOLI’2008)(IEEE服务运作、物流与信息年会)

作者： Ming Xie Li Xia Jinyan Shao Yanjia Zhao Jin Dong IBM China Research LaboratoryZhongguancun Software ParkBeijing 100193 P.R.China Department of Automation Tsinghua UniversityBeijing 100084P.R.China IBM China Research Laboratory Zhongguancun Software Park Beijing 100193 P.R.China

Facility location optimization is very important for many retail industries,such as banking network,chain stores,and so *** covering location problem (MCLP) is one of the well-known models for these facility location optimization problems,which has earned extensive research ***,various practical requirements limit the application of the traditional formulation of MCLP,and the NP-hard characteristic makes effective approaches for large scale problems extremely *** paper focuses on a facility location problem motivated by a practical project of bank *** traditional MCLP formulation is generalized as a mixed integer programming (MIP) with considerations of various costs and revenues,multitype of facilities,and flexible coverage functions.A CPLEX-based hybrid nested partition algorithm is developed for large scale problems,and heuristic-based extensions are introduced to deal with extremely large *** formulation and algorithm are embedded into an asset called *** results demonstrate the effectiveness and efficiency of our approach.

关键词： mixed integer programming nested partition facility location optimization maximal covering location problem

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Exploiting structure in mixed integer programming: Branching, cutting planes, and complexity

Exploiting structure in mixed integer programming: Branching...

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作者： Yang, Yu Georgia Institute of Technology

学位级别：博士

As a powerful mathematical modeling framework, mixed integer programming (MIP) has seen many industrial applications in areas such as logistics, scheduling, capital budgeting, etc. Tremendous algorithmic advances have been achieved and state-of-the-art solvers, such as CPLEX and Gurobi, can now solve previously unsolvable instances in just seconds. However, many instances, especially if the underlying problem has a complex structure and the instances are large, can still take a long time to solve. In this dissertation, we take on the challenge of solving MIPs faster through novel branching schemes and novel cutting planes. Via computational complexity analysis, we also justify the usage of MIPs for approaching challenging problems and inspire the design of primal heuristics. The first part of the thesis focuses on novel branching schemes. We explore the benefits of multi-variable branching schemes in achieving node efficiency, i.e., producing small sized branch and bound search trees. Furthermore, we show that machine learning (ML) techniques can significantly accelerate the selection of sets of variables to branch on and thus turn multi-variable branching schemes into computationally efficient methods. In Chapter 2, we use the 0-1 knapsack problem as an illustrative example. We present examples where multi-variable branching has advantages over single-variable branching, and partially characterize situations in which this happens. For a special class of 0-1 knapsack instances from [Chv 80], we show an LP based branch-and-bound algorithm employing an appropriately chosen multi-variable branching scheme explores either three or seven nodes while it's proved in [Chv 80] that a single-variable branching counterpart must explore exponentially many nodes to arrive at an optimal solution. Furthermore, we investigate the performance of various multi-variable branching schemes for 0-1 knapsack instances computationally and demonstrate their potential. In Chapter 3, we int

关键词： mixed integer programming Branching Cutting planes Complexity

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Integrating local search techniques into mixed integer programming

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4OR 2004年第4期2卷 321-324页

作者： Danna, Emilie ILOG 94253 Gentilly Cedex 9 rue de Verdun France Laboratoire d'Informatique d'Avignon CNRS FRE 2487 84911 Avignon Cedex 9 339 Chemin des Meinajariés BP 1228 France

We survey the main results of the author's PhD thesis that was supervised by Claude Le Pape (ILOG, France) and Philippe Michelon (Université d'Avignon, France) and has been defended in June 2004. The dissertation is written in French and is available from the author. It introduces several strategies for integrating local search techniques into mixed integer programming, with an emphasis on generic algorithms. © 2004 Springer-Verlag Berlin/Heidelberg.

关键词： branch-and-price hybrid algorithms local search mixed integer programming scheduling vehicle routing

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A mixed integer programming Framework for the Fuel Optimal Guidance of Complex Spacecraft Rendezvous and Proximity Operation Missions

A Mixed Integer Programming Framework for the Fuel Optimal G...

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作者： Andrew S. LeValley Air Force Institute of Technology

学位级别：硕士

Space is a contested, congested, and competitive environment where space situational awareness (SSA) is a key factor in the long term sustainability of space as a national interest. Space-based SSA conducted by inspector satellites is critical to the detecting, tracking, and attribution of actions in space. Thus, space-based fuel-optimal maneuvers are essential to increasing mission life and improving the capability of inspector satellites working to characterize resident space objects (RSOs) in geosynchronous orbit (GEO). Additionally, on-orbit inspection missions can be characterized by multiple waypoint visits where an inspector is accomplishing a set of proximity operation mission objectives through the visit of multiple waypoints signifying viewing angles, natural motion circumnavigation (NMC) injection states, and rendezvous locations. Traditionally, the combinatorial and trajectory optimization aspects of these space-based multiple waypoint visits have been solved in a segregated manner. This thesis presents a mixed integer programming (MIP) framework, in which the combinatorial and trajectory optimization nature of these problems are coupled resulting in the fuel-optimal guidance for complex rendezvous and proximity operation missions. First, a mixed integer Linear programming (MILP) formulation is used to solve for the fuel optimal guidance of an inspector visiting multiple viewing angles, defined by waypoints, around a single RSO. This mission is subject to keep-out-zones (KOZ) and mission time constraints. Additionally, the initial MILP problem is extended to a linear cooperative control formulation where two inspectors are working together to accomplish the mission objectives. Both MILP problems are solved to global optimality using a commercial MIP solver.

关键词： Optimal-control mixed integer programming On-orbit inspection Space situational awareness

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A mixed integer programming Model for Improving Theater Distribution Force Flow Analysis

A Mixed Integer Programming Model for Improving Theater Dist...

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作者： Micah J. Hafich Air Force Institute of Technology

学位级别：硕士

Obtaining insight into potential vehicle mixtures that will support theater distribution, the final leg of military distribution, can be a challenging and time-consuming process for United States Transportation Command (USTRANSCOM) force flow analysts. The current process of testing numerous different vehicle mixtures until separate simulation tools demonstrate feasibility is iterative and overly burdensome. Improving on existing research, a mixed integer programming model was developed to allocate specific vehicle types to delivery items, or requirements, in a manner that would minimize both operational costs and late deliveries. This gives insight into the types and amounts of vehicles necessary for feasible delivery and identifies possible bottlenecks in the physical network. Further solution post-processing yields potential vehicle beddowns which can then be used as approximate baselines for further distribution analysis. A multimodal, heterogeneous set of vehicles is used to model the pickup and delivery of requirements within given time windows. To ensure large-scale problems do not become intractable, precise set notation is utilized within the mixed integer program to ensure only necessary variables and constraints are generated.

关键词： mixed integer programming Theater Distribution Math programming Pickup and Delivery with Time Windows

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Exploring mixed integer programming reformulations for virtual machine placement with disk anti-colocation constraints

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PERFORMANCE EVALUATION 2019年 135卷 102035-102035页

作者： Zheng, Xiaoying Xia, Ye Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

One of the important problems for datacenter resource management is to place virtual machines (VMs) to physical machines (PMs) such that certain cost, profit or performance objective is optimized, subject to various constraints. In this paper, we consider an interesting and difficult VM placement problem with disk anti-colocation constraints: a VM's virtual disks should be spread out across the physical disks of its assigned PM. For solutions, we use the mixed integer programming (MIP) formulations and algorithms. However, a challenge is the potentially long computation time of the MIP algorithms. In this paper, we explore how reformulation of the problem can help to reduce the computation time. We develop two reformulations, by redefining the variables, for our VM placement problem and evaluate the computation time of all three formulations. We show that they have vastly different computation time. All three formulations can be useful, but for different problem instances. They all should be kept in the toolbox for tackling the problem. Out of the three, formulation COMB is especially flexible and versatile, and it can solve large problem instances. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Datacenter Cloud computing Virtual machine placement Resource management mixed integer programming

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A mixed integer programming formulation for the stochastic lot sizing problem with controllable processing times

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COMPUTERS & OPERATIONS RESEARCH 2021年 132卷 105302-105302页

作者： Tunc, Huseyin Social Sci Univ Ankara Dept Management Ankara Turkey

In this study, we address the capacitated stochastic lot-sizing problem under alpha service level constraints. We assume that processing times can be decreased in return for compression cost that follows a convex function. We consider this problem under the static uncertainty strategy suggesting to determine replenishment plans at the beginning of the planning horizon. We develop an extended mixed integer programming (MIP) formulation built on a predefined piecewise linear approximation. Then, we adopt the so-called dynamic cut generation approach to be able to use the proposed MIP formulation with no prior approximation of the cost function. Also, we demonstrate how to extend the dynamic cut generation approach to consider the exact inventory cost in the objective function. We show the computational performance of the proposed MIP model with the dynamic cut generation approach in an extensive numerical study where second order cone programming formulations developed in the literature are used as benchmark. The results reveal that the proposed MIP model deployed with the dynamic cut generation yields a superior computational performance as compared to the benchmark formulations especially when the order of compression cost function is higher.

关键词： Inventory Stochastic lot-sizing Controllable processing time mixed integer programming

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An Optimal Generation Scheduling Approach Based on Linear Relaxation and mixed integer programming

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IEEE ACCESS 2020年 8卷 168625-168630页

作者： Lei, Yunkai Liu, Fang Li, Ao Su, Yunche Yang, Xinting Zheng, Jiehui State Grid Sichuan Econ Res Inst Chengdu 610000 Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China

This paper proposes an optimal generation scheduling approach based on linear relaxation and mixed integer programming, which is used to solve the generation dispatch problem. The quadratic transmission loss constraint of each transmission line is converted into linear constraints by using the linear relaxation and mixed integer programming technique. Consequently, the original optimal generation scheduling problem is formulated as a quadratic programming or mixed integer quadratic programming problem that can be solved by commercial optimization solver. In order to improve the efficiency of algorithm, this paper further analyses the generation scheduling model and deletes the redundant variables and constraints. Three test systems, including IEEE 30-node system, IEEE 118-node system, and Polish 2746-node system, are employed to examine the effectiveness of the proposed method. The comparative results obtained by the proposed method, quadratically constrained quadratic programming method (QCQP), and solving constraint integer programs solver (SCIP) verify the effectiveness of the proposed method in solving the optimal generation scheduling problem.

关键词： Propagation losses Power transmission lines Load modeling Job shop scheduling Linear programming Generators Quadratic programming Optimal generation scheduling transmission losses quadratic programming mixed integer programming linear relaxation prosumer energy management

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