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作者： Siddharth Bhela Virginia Polytechnic Institute and State University

学位级别：博士

With the advent of distributed energy resources (DERs), electric vehicles, and demand-response programs, grid operators are in dire need of new monitoring and design tools that help improve efficiency, reliability, and stability of modern power networks. To this end, the work in this thesis explores a generalized modeling and analysis framework for two pertinent tasks: i) learning loads via grid probing, and; ii) optimizing power grid topologies for stability. Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar panels and energy storage units are interfaced via inverters with advanced sensing and actuation capabilities. In this context, we first put forth the idea of engaging power electronics to probe an electric grid and record its voltage response at actuated and metered buses to infer non-metered loads. Probing can be accomplished by commanding inverters to momentarily perturb their power injections. Multiple probing actions can be induced within a few tens of seconds. Load inference via grid probing is formulated as an implicit nonlinear system identification task, which is shown to be topologically observable under certain conditions. The analysis holds for single- and multi-phase grids, radial or meshed, and applies to phasor or magnitude-only voltage data. Using probing to learn non-constant-power loads is also analyzed as a special case. Once a probing setup is deemed topologically observable, a methodology for designing probing injections abiding by inverter and network constraints to improve load estimates is provided. The probing task under noisy phasor and non-phasor data is tackled using a semidefinite-program relaxation. As a second contribution, we also study the effect of topology on the linear time-invariant dynamics of power networks. For a variet

关键词： Smart inverters power flow state estimation load learning grid probing generic rank topological observability topology design mixed-integer programming

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Valid Inequalities and Facets for Multi-Module (Survivable) Capacitated Network Design Problem

Valid Inequalities and Facets for Multi-Module (Survivable) ...

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作者： Luo, Haochen Texas A&M University

学位级别：Ph.D., Doctor of Philosophy

In this dissertation, we develop new methodologies and algorithms to solve the multi-module (survivable) network design problem. Many real-world decision-making problems can be modeled as network design problems, especially on networks with capacity requirements on arcs or edges. In most cases, network design problems of this type that have been studied involve different types of capacity sizes (modules), and we call them the multi-module capacitated network design (MMND) problem. MMND problems arise in various industrial applications, such as transportation, telecommunication, power grid, data centers, and oil production, among many others. In the first part of the dissertation, we study the polyhedral structure of the MMND problem. We summarize current literature on polyhedral study of MMND, which generates the family of the so-called cutset inequalities based on the traditional mixed integer rounding (MIR). We then introduce a new family of inequalities for MMND based on the so-called n-step MIR, and show that various classes of cutset inequalities in the literature are special cases of these inequalities. We do so by studying a mixed integer set, the cutset polyhedron, which is closely related to MMND. We also study the strength of this family of inequalities by providing some facet-defining conditions. These inequalities are then tested on MMND instances, and our computational results show that these classes of inequalities are very effective for solving MMND problems. Generalizations of these inequalities for some variants of MMND are also discussed. Network design problems have many generalizations depending on the application. In the second part of the dissertation, we study a highly applicable form of SND, referred to as multi-module SND (MM-SND), in which transmission capacities on edges can be sum of integer multiples of differently sized capacity modules. For the first time, we formulate MM-SND as a mixed integer program (MIP) using preconfigured-cycles

关键词： mixed-integer programming Network design Cutset inequalities Valid inequalities n-step MIR

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Transfer Learning for mixed-integer Resource Allocation Problems in Wireless Networks

Transfer Learning for Mixed-Integer Resource Allocation Prob...

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IEEE International Conference on Communications (IEEE ICC)

作者： Shen, Yifei Shi, Yuanming Zhang, Jun Letaief, Khaled B. Hong Kong Univ Sci & Technol Dept ECE Hong Kong Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Hong Kong Polytech Univ Dept EIE Hong Kong Peoples R China

ISBN: (纸本)9781538680889

Effective resource allocation plays a pivotal role in wireless networks. Unfortunately, typical resource allocation problems are mixed-integer nonlinear programming (MINLP) problems, which are NP-hard. Machine learning based methods recently emerge as a disruptive way to obtain near-optimal performance for MINLP problems with affordable computational complexity. However, they suffer from severe performance deterioration when the network parameters change, which commonly happens in practice and can be characterized as the task mismatch issue. In this paper, we propose a transfer learning method via self-imitation, to address this issue for effective resource allocation in wireless networks. It is based on a general "learning to optimize" framework for solving MINLP problems. A unique advantage of the proposed method is that it can tackle the task mismatch issue with a few additional unlabeled training samples, which is especially important when transferring to large-size problems. Numerical experiments demonstrate that the proposed method, with much less training time, achieves comparable performance with the model trained from scratch based on sufficient labeled samples. To the best of our knowledge, this is the first work that applies transfer learning for resource allocation in wireless networks.

关键词： Resource allocation mixed-integer programming transfer learning imitation learning wireless networks

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The Vehicle Drone Routing Problem with Drone Endurance

The Vehicle Drone Routing Problem with Drone Endurance

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作者： Michael Kwee Vrije Universiteit Amsterdam

学位级别：硕士

Last-mile logistics are under pressure from the growth in urbanization, the increased demand in e- commerce and the customer's desire for speed. An innovative concept to deal with the challenges in last-mile delivery is the use of unmanned aerial vehicles (UAVs) or drones. Companies like UPS, DHL, Amazon and Google, are already testing how UAVs can be used to improve their last-mile logistics. One of the possibilities is that a UAV and a truck collaborate in delivering parcels. One of the disadvantages of using UAVs is the limited flight endurance. An empty UAV can be recharged or the battery can be switched. This research paper focuses on the situation in which the UAV is recharged. We formulate the vehicle drone routing problem with drone endurance (VDRPDE), in which a UAV and a truck collaborate to deliver parcels as fast as possible. A mixed-integer programming model is formulated to solve this problem optimally. Next, an analysis is presented that investigates the effect that a rechargeable battery has on the optimal delivery time in comparison to a policy where the battery is constantly swapped. We find that the improvement from switching batteries is relatively small. However, we find that this improvement increases as scenarios get larger, given that customers are located in close vicinity. Furthermore, a sensitivity analysis is done on the charging rate of the UAV. We find that using a switching policy has a bigger positive impact on the delivery time than increasing the charging ratio. One of the limitations of this research is that the largest scenarios consist of 10 nodes. Future research could focus on creating heuristics to solve larger instances of the VDRPDE.

关键词： vehicle routing problem traveling salesman problem UAV last-mile delivery mixed-integer programming

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Split Cuts From Sparse Disjunctions

Split Cuts From Sparse Disjunctions

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作者： Yang, Shenghao University of Waterloo

学位级别：硕士

Cutting planes are one of the major techniques used in solving mixed-integer Linear programming (MIP) models. Various types of cuts have long been exploited by MIP solvers, leading to state-of-the-art performance in practice. Among them, the class of split cuts, which includes Gomory mixed integer (GMI) and mixed integer Rounding (MIR) cuts from tableaux, are arguably the most effective class of general cutting planes within a branch-and-cut framework. Sparsity, on the other hand, is a common characteristic of real-world MIP problems, and it is an important part of why the simplex method works so well inside branch-and-cut. A natural question, therefore, is to determine how sparsity can be incorporated into split cuts and how effective are split cuts that exploit sparsity. In this thesis, we evaluate the strength of split cuts that arise from sparse split disjunctions. In particular, we implement an approximate separation routine that separates only split cuts whose split disjunctions are sparse. We also present a straightforward way to exploit sparsity structure that is implicit in the MIP formulation. We run computational experiments and conclude that, one possibility to produce good split cuts is to try sparse disjunctions and exploit such structure.

关键词： mixed-integer programming split cuts sparsity decomposition

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A two-phase approach to solve the synchronized bin-forklift scheduling problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第3期29卷 651-657页

作者： El Hachemi, Nizar Saddoune, Mohammed El Hallaoui, Issmail Rousseau, Louis-Martin Univ Mohammed V Agdal Ecole Mohammadia Ingn Dept Genie Ind Rabat Morocco Univ Hassan 2 Fac Sci Tech Mohammedia Casablanca Morocco Ecole Polytech Montreal Dept Math & Genie Ind Montreal PQ Canada Univ Montreal CIRRELT CP 6128Succ Ctr Ville Montreal PQ H3C 3J7 Canada GERAD CP 6128Succ Ctr Ville Montreal PQ H3C 3A7 Canada

In this paper, we propose a two-phase approach to solve a combined routing and scheduling problem that occurs in the textile industry: fabrics are dyed by dye-jets and transported by forklifts. The objective is to minimize the cost of the unproductive activities, i.e., the dye-jet setup times and the forklift waiting time. The first phase solves an integer linear program to assign jobs (fabrics) to dye-jets while minimizing the setup cost;we compare an arc-based and a path-based formulation. The second phase uses a mixed-integer linear program for the dye-jet scheduling and both the routing and scheduling of forklifts. Experiments are performed on real data provided by a major multinational company, and larger test problems are randomly generated to assess the algorithm. The tests were conducted using Cplex 12.6.0 and a column generation solver. The numerical results show that our approach is efficient in terms of both solution quality and computational time.

关键词： Scheduling Column generation mixed-integer programming Textile industry

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Optimal Antibody Puri cation Strategies Using Data-Driven Models

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Engineering 2019年第6期5卷 1077-1092页

作者： Songsong Liu Lazaros GPapageorgiou School of Management Harbin Institute of TechnologyHarbin 150001China School of Management Swansea UniversitySwansea SA18ENUK Centre for Process Systems Engineering Department of Chemical EngineeringUniversity College LondonLondon WC1E 7JEUK

This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.

关键词： Antibody purification Multiscale optimization Antigen-binding fragment mixed-integer programming Data-driven model Piecewise linear regression

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Improving Order Fulfillment Performance through Integrated Inventory Management in a Multi-Item Finished Goods System

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JOURNAL OF BUSINESS LOGISTICS 2020年第1期41卷 54-66页

作者： Yang, Liu Li, Haitao Campbell, James F. Sam Houston State Univ Coll Business Adm Supply Chain Management Huntsville TX 77340 USA Univ Missouri St Louis Coll Business Adm Supply Chain & Analyt Dept St Louis MO USA Univ Missouri St Louis Coll Business Adm Supply Chain & Analyt St Louis MO USA

Effective inventory management is fundamental to order fulfillment excellence and supply chain success. In this paper, we develop a strategic inventory management decision tool that integrates inventory classification and inventory control policy decisions for maximizing order fulfillment performance, while accounting for a constraint on inventory budget and the profit expectation of a firm. This inventory solution tool provides critical enhancements to current inventory planning software, which is developed upon the traditional inventory classification scheme and where practitioners have to balance service levels and safety stock decisions through trial-and-error. The model allows firms to assess whether the current inventory performance is Pareto optimal, quantify the trade-offs between various performance measures, and identify the right inventory level according to the firms' strategic goals. In computational results, we demonstrate the trade-off and positive relationships between key item- and order-based inventory performance measures and short-term profitability under different levels of inventory budget in a multi-item finished goods inventory system.

关键词： order fulfillment optimization inventory classification order fill rate trade-off of profit and service level mixed-integer programming

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Partial objective inequalities for the multi-item capacitated lot-sizing problem

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COMPUTERS & OPERATIONS RESEARCH 2018年 91卷 132-144页

作者： Buyuktahtakin, I. Esra Smith, J. Cole Hartman, Joseph C. New Jersey Inst Technol Dept Mech & Ind Engn Newark NJ 07102 USA Clemson Univ Dept Ind Engn Clemson SC 29631 USA Univ Massachusetts Lowell Coll Engn Lowell MA USA

In this paper, we study a mixed-integer programming model of the single-level multi-item capacitated lot-sizing problem (MCLSP), which incorporates shared capacity on the production of items for each period throughout a planning horizon. We derive valid bounds on the partial objective function of the MCLSP formulation by solving the first t periods of the problem over a subset of all items, using dynamic programming and integer programming techniques. We also develop algorithms for strengthening these valid inequalities by back-lifting techniques. These inequalities can be utilized within a cutting-plane algorithm, in which we perturb the partial objective function coefficients to identify violated inequalities to the MCLSP polytope. Our computational results show that the envelope inequalities are very effective for the MCLSP instances with different capacity and cost characteristics, when compared to the (l, S) inequalities. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： mixed-integer programming Dynamic programming Cutting planes Valid inequalities Partial objective cuts (l, S) inequalities Polyhedral study Branch-and-cut Multi-item Capacitated lot-sizing Production planning

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Ellipsoidal mixed-integer representability

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MATHEMATICAL programming 2018年第1-2期172卷 351-369页

作者： Del Pia, Alberto Poskin, Jeffrey Univ Wisconsin Wisconsin Inst Discovery Dept Ind & Syst Engn Madison WI USA Univ Wisconsin Dept Math Madison WI 53706 USA

Representability results for mixed-integer linear systems play a fundamental role in optimization since they give geometric characterizations of the feasible sets that can be formulated by mixed-integer linear programming. We consider a natural extension of mixed-integer linear systems obtained by adding just one ellipsoidal inequality. The set of points that can be described, possibly using additional variables, by these systems are called ellipsoidal mixed-integer representable. In this work, we give geometric conditions that characterize ellipsoidal mixed-integer representable sets.

关键词： mixed-integer programming Quadratic programming Representability Ellipsoidal constraints

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