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An exact decomposition algorithm for a chance-constrained new product risk model

OPERATIONS RESEARCH LETTERS 2019年第4期47卷 250-256页

作者： Fontem, Belleh Keskin, Burcu Melouk, Sharif Vaughn, Carley Univ Massachusetts Manning Sch Business Lowell MA 01826 USA Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL 35487 USA Univ Mary Washington Coll Business 1301 Coll Ave Fredericksburg VA 22401 USA

We use the generalized two-sided Chebyshev inequality to reformulate a certain nonlinear, chance constrained new product risk model. The problem has a linear cost objective and a constraint set featuring a probabilistic lower bound on an event which depends on a collection of mutually independent, uniform random parameters. Our reformulation permits a reduction of the problem to a sequence of second-order cone programs. We, therefore, identify a new family of non-convex programs whose members are amenable to convex programming solution techniques. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Chance-constrained optimization Risk management Second-order cone programming Chebyshev's inequality decomposition algorithm

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Maximum likelihood in statistical estimation of dynamic systems: decomposition algorithm and simulation results

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SIGNAL PROCESSING 1997年第1期57卷 65-79页

作者： Deng, L Shen, XM Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada N2L 3G1

In this paper, we describe an efficient decomposition algorithm for parameter estimation of linear dynamical systems with the state-space formulation which contain a ''drive'' term as a free, unknown system parameter. The dynamical system can be viewed as a natural extension from the discrete-state hidden Markov model to its continuous-state counterpart. The focus of this paper is on unified techniques for efficient estimation of the parameters of such a model. The Expectation-Maximization (EM) algorithm is developed, in conjunction with the conventional Kalman smoothing estimators, for estimating the system parameters by maximum likelihood. The algorithm developed is applicable to either stationary or non-stationary version of the dynamic system. In particular, a decomposition technique is described in detail and is shown to reduce effectively the computational load in parameter estimation for high-dimensional systems. Simulation results are presented which demonstrate the accuracy of the proposed parameter estimation technique. (C) 1997 Elsevier Science B.V.

关键词： maximum likelihood decomposition algorithm Kalman filter dynamical system

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An NN-Based SRD decomposition algorithm and Its Application in Nonlinear Compensation

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SENSORS 2014年第9期14卷 17353-17375页

作者： Yan, Honghang Deng, Fang Sun, Jian Chen, Jie Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China

In this study, a neural network-based square root of descending (SRD) order decomposition algorithm for compensating for nonlinear data generated by sensors is presented. The study aims at exploring the optimized decomposition of data 1.00,0.00,0.00 and minimizing the computational complexity and memory space of the training process. A linear decomposition algorithm, which automatically finds the optimal decomposition of N subparts and reduces the training time to 1/root N and memory cost to 1/N, has been implemented on nonlinear data obtained from an encoder. Particular focus is given to the theoretical access of estimating the numbers of hidden nodes and the precision of varying the decomposition method. Numerical experiments are designed to evaluate the effect of this algorithm. Moreover, a designed device for angular sensor calibration is presented. We conduct an experiment that samples the data of an encoder and compensates for the nonlinearity of the encoder to testify this novel algorithm.

关键词： decomposition algorithm data amount Fourier neural network nonlinear errors compensation

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Multi-objective energy consumption scheduling based on decomposition algorithm with the non-uniform weight vector

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APPLIED SOFT COMPUTING 2016年 39卷 223-239页

作者： Lu, Hui Zhang, Mengmeng Fei, Zongming Mao, Kefei Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

The multi-objective energy consumption scheduling problem based on the third-party management is one essential issue of smart grid. The minimal energy cost and the maximal utility are two optimization objectives. One characteristic of the multi-objective energy consumption scheduling problem is that the magnitude difference between the two objectives increases as the number of users increases. The difference affects the quality of the solution set in the sense that it is harder to obtain the uniformly distributed solutions with good convergence and diversity for problems with a larger difference. In this paper, we propose a decomposition algorithm based on the non-uniform weight vector. The weight vector is designed based on the relationship of the magnitude difference between the two optimization objectives. The weight vector consists of two parts. One is the balance factor that can maintain the diversity of the solutions. The other is the scale factor that further enhances the searching ability with a large number of users. With the non-uniform weight vector, our algorithm can effectively deal with the magnitude difference between the two optimization objectives. The simulation illustrates that the proposed algorithm is very useful for solving large-scale energy consumption scheduling problems. In addition, the modified standard ZDT problems with apparent magnitude differences between two objectives are used to illustrate the versatility and stability of the proposed algorithms based on the non-uniform weight vector. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization decomposition algorithm Energy consumption scheduling problem Non-uniform weight vector Objective function

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A risk-averse location-protection problem under intentional facility disruptions: A modified hybrid decomposition algorithm

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2018年 114卷 196-219页

作者： Jalali, Sajjad Seifbarghy, Mehdi Niaki, Seyed Taghi Akhavan Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran Sharif Univ Technol Dept Ind Engn POB 11155-9414Azadi Ave Tehran *** Iran

The rising disruptions of interdictors force supply chains' designers to embody the protection decisions when locating the facilities. In the presence of variability in the intensity of disruptions, a risk measure is incorporated into the decision-making. The designer-interdictor bi-level problem, therefore, optimizes the joint location and protection decisions with respect to the conditional value-at-risk. This configuration has been absent from the literature. An accelerated modified Benders decomposition algorithm is developed and enhanced by being hybridized with a sample average approximation-based genetic algorithm. We examine how this new configuration influences the optimal solutions and assess the effectiveness of the proposed method.

关键词： Facility location-protection problem Conditional value-at-risk Bi-level programming decomposition algorithm Sample average approximation Game theory

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A decomposition algorithm of fuzzy Petri net using an index function and incidence matrix

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第8期42卷 3980-3990页

作者： Zhou, Kai-Qing Zain, Azlan Mohd Mo, Li-Ping Univ Teknol Malaysia Fac Comp Soft Comp Res Grp Skudai 81310 Johor Malaysia Jishou Univ Coll Informat Sci & Engn Jishou 416000 Hunan Peoples R China

As with Petri nets (PNs), the state space explosion has limited further studies of fuzzy Petri net (FPN), and with the rising scale of FPN, the algorithm complexity for related applications using FPN has also rapidly increased. To overcome this challenge, we propose a decomposition algorithm that includes a backwards search stage and forward strategy for further decomposition, one that divides a large-scale FPN model into a set of sub-FPN models using both a presented index function and incidence matrix. In the backward phase, according to different output places, various completed inference paths are recognized automatically. An additional decomposition operation is then executed if the "OR" rule exists for each inference path. After analysing the proposed algorithm to confirm its rigor, a proven theorem is presented that calculates the number of inference paths in any given FPN model. A case study is used to illustrate the feasibility and robust advantages of the proposed decomposition algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Fuzzy Petri net decomposition algorithm Index function Incidence matrix Inference path

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Distribution planning problem for a high-speed rail catering service considering time-varying demands and pedestrian congestion: A lot-sizing-based model and decomposition algorithm

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2019年 123卷 61-89页

作者： Wu, Xin Nie, Lei Xu, Meng Zhao, Lili Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85281 USA Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Foshan Railway Investment Construct Grp Co Ltd Beijing 100044 Peoples R China

A non-linear mixed integer programming problem is developed to establish a distribution plan for catering services of high-speed railways (CSHRs) based on lot-sizing networks. The model attempts to determine the locations for producing different types of food products and controlling the inventory levels of trains during their trips, considering the storage capacities and time-varying demands. A Lagrangian substitution-based solution approach is applied to decompose the model into a mixed integer linear programming model and a sequence of univariate concave models. Computational experiments indicate that the approach performs satisfactorily in terms of computational time and solution quality in a real-world situation.

关键词： Catering service Lot-sizing Time-varying demand In-station congestion decomposition algorithm

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Petroleum supply planning: reformulations and a novel decomposition algorithm

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OPTIMIZATION AND ENGINEERING 2017年第1期18卷 215-240页

作者： Rocha, Roger Grossmann, Ignacio E. de Arago, Marcus V. S. Poggi PETROBRAS Operat Unit Espirito Santo Prod & Engn Dept Ave Nossa Senhora da Penha 1648 Vitoria ES Brazil Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Informat Rua Marques Sao Vicente225Gavea Rio De Janeiro Brazil

The petroleum supply planning activity is an important link for the integration of the petroleum supply chain at PETROBRAS as it is responsible for refining the strategic supply planning information to be used at the operational level. In this work we present the best strategies to solve this challenging problem. It is important to note that although the solvers in the last decade have evolved enormously, for this particular application we cannot get solutions with satisfactory quality in reasonable computational time with only the initial proposed model in Rocha et al. (Comput Chem Eng 33(12):2123-2133, 2013). To efficiently solve this problem we propose a novel decomposition algorithm and reformulations based on a cascading knapsack structure that turn out to be applicable in a wide range of problems. We show that the novel decomposition algorithm is the most appropriate method to solve the petroleum supply planning problem if we consider more than two tankers to offload each platform. In the case of one or two tankers to offload each platform, the hull relaxation formulation based on the cascading knapsack structure introduced after an inventory reformulation at platforms is the best option if one is to solve this problem. For the real application, these solution alternatives allow us to implement a general algorithm that automatically switches to the best solution option depending on the structure of the problem. This model is being tested at PETROBRAS and is proving an effective tool to help integrate its petroleum supply chain as well as to do what-if analysis to look for alternative solutions never before thought of.

关键词： Petroleum supply planning Mixed integer programming decomposition algorithm Disjunctive programming Reformulations

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Perturbed decomposition algorithm applied to the multi-objective Traveling Salesman Problem

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COMPUTERS & OPERATIONS RESEARCH 2017年 79卷 314-330页

作者： Cornu, Marek Cazenave, Tristan Vanderpooten, Daniel PSL Res Univ Univ Paris Dauphine CNRS UMR 7243LAMSADE F-75016 Paris France

Dealing with multi-objective combinatorial optimization, this article proposes a new multi-objective set based meta-heuristic named Perturbed decomposition algorithm (PDA). Combining ideas from decomposition methods, local search and data perturbation, PDA provides a 2-phase modular framework for finding an approximation of the Pareto front. The first phase decomposes the search into a number of linearly aggregated problems of the original multi-objective problem. The second phase conducts an iterative process: aggregated problems are first perturbed then selected and optimized by an efficient single-objective local search solver. Resulting solutions will serve as a starting point of a multi-objective local search procedure, called Pareto Local Search. After presenting a literature review of meta-heuristics on the multi-objective symmetric Traveling Salesman Problem (TSP), we conduct experiments on several instances of the bi-objective and tri-objective TSP. The experiments show that our proposed algorithm outperforms the best current methods on this problem. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multi-objective combinatorial optimization Multi-objective Traveling Salesman Problem Meta-heuristics Pareto Local search decomposition algorithm Data perturbation

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Real-time train regulation in the metro system with energy storage devices: An efficient decomposition algorithm with bound contraction

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 159卷

作者： Li, Shukai Yuan, Yin Chen, Zebin Yang, Lixing Yu, Chengpu Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Focusing on the energy -conservation train operation issues, this paper proposes an effective real-time train regulation scheme for metro systems with energy storage devices. Specifically, to minimize train timetable deviation, passenger waiting and energy consumption, we formulate a mixed -integer nonlinear programming model to generate energy -efficient train regulation strategies. This model explicitly considers the train traffic, passenger load and storage, immediate and delayed uses of regenerative energy. Carefully tailored to the proposed model, we devise an efficient decomposition algorithm to split the original problem into small-scale subproblems. In the algorithm, specific values of binary variables, passenger -flow estimates and logic -based cuts are consecutively identified and updated. Besides, bound contraction and bilinear -specific warming start procedures are particularly designed for further acceleration. Numerical experiments are conducted to validate the proposed model and method. Our energyefficient train regulation strategies can improve train departure punctuality, headway regularity, reduce passenger waiting times, and achieve energy savings. Furthermore, the solution algorithm exhibits promising computational efficiency in real -world experiments, thereby facilitating an online implementation.

关键词： Metro system Real-time train regulation Energy storage devices decomposition algorithm Mixed-integer nonlinear programming

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