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检索条件"任意字段=13th International Conference on Integer Programming and Combinatorial Optimization"

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On Idle Energy Consumption Minimization in Production: Industrial Example and Mathematical Model 9th

On Idle Energy Consumption Minimization in Production: Indus...

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9th international conference on Operations Research and Enterprise Systems (ICORES)

作者： Benedikt, Ondrej Sucha, Premysl Hanzalek, Zdenek Czech Tech Univ Czech Inst Informat Robot & Cybernet Jugoslavskych Partyzanu 1580-3 Prague Czech Republic Czech Tech Univ Fac Elect Engn Dept Control Engn Karlovo Namesti 13 Prague Czech Republic

ISBN: (纸本)9789897583964

this paper, inspired by a real production process of steel hardening, investigates a scheduling problem to minimize the idle energy consumption of machines. the energy minimization is achieved by switching a machine to some power-saving mode when it is idle. For the steel hardening process, the mode of the machine (i.e., furnace) can be associated with its inner temperature. Contrary to the recent methods, which consider only a small number of machine modes, the temperature in the furnace can be changed continuously, and so an infinite number of the power-saving modes must be considered to achieve the highest possible savings. To model the machine modes efficiently, we use the concept of the energy function, which was originally introduced in the domain of embedded systems but has yet to take roots in the domain of production research. the energy function is illustrated with several application examples from the literature. Afterward, it is integrated into a mathematical model of a scheduling problem with parallel identical machines and jobs characterized by release times, deadlines, and processing times. Numerical experiments show that the proposed model outperforms a reference model adapted from the literature.

关键词： Scheduling Energy optimization Operation Modes Mixed integer Linear programming Parallel Machines

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Operations research and voting theory 6th

Operations research and voting theory

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6th international conference on Operations Research and Enterprise Systems, ICORES 2017

作者： Hudry, Olivier Télécom ParisTech 46 rue Barrault Paris Cedex 1375634 France

ISBN: (纸本)9783319947662

One main concern of voting theory is to determine a procedure for choosing a winner from among a set of candidates, based on the preferences of the voters or, more ambitiously, for ranking all the candidates or a part of them. In this presentation, we pay attention to some contributions of operations research to the design and the study of some voting procedures. First, we show through an easy example that the voting procedure plays an important role in the determination of the winner: for an election with four candidates, the choice of the voting procedure allows electing anyone of the four candidates with the same individual preferences of the voters. this provides also the opportunity to recall some main procedures, including Condorcet’s procedure, and leads to the statement of Arrow’s theorem. In a second step, more devoted to a mathematical approach, we detail a voting procedure based on the concept of Condorcet winner, namely the so-called median procedure. In this procedure, the aim is to rank the candidates in order to minimize the number of disagreements with respect to the voters’ preferences. thus we obtain a combinatorial optimization problem. We show how to state it as a linear programming problem with binary variables. We specify the complexity of this median procedure. Last, we show, once again through easy examples, that the lack of some desirable properties for the considered voting procedure may involve some "paradoxes". © Springer international Publishing AG, part of Springer Nature 2018.

关键词： combinatorial optimization

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A Novel Approach for Solving Large-Scale Bike Sharing Station Planning Problems 1

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13th international conference on Learning and Intelligent optimization (LION)

作者： Kloimuellner, Christian Raidl, Guenther R. TU Wien Inst Log & Computat Favoritenstr 9-11-19201 A-1040 Vienna Austria

ISBN: (数字)9783030386290

ISBN: (纸本)9783030386290;9783030386283

In large cities all around the world, individual and motorized traffic is still prevalent. this circumstance compromises the quality of living, and moreover, space inside cities for parking individual vehicles for movement is scarce and is becoming even scarcer. thus, the need for a greener means of transportation and less individual vehicles inside the cities is demanded and rising. An already accepted and established solution possibility to these problems are public bike sharing systems (PBS). Such systems are often freely available to people for commuting within the city and utilize the available space in the city more efficiently than individual vehicles. When building or extending a PBS, a certain optimization goal is to place stations inside a city or a part of it, such that the number of bike trips per time unit is maximized under certain budget constraints. In this context, it is also important to consider rebalancing and maintenance costs as they introduce substantial supplementary costs in addition to the fixed and variable costs when building or extending a PBS. In contrast to the literature, this work introduces a novel approach which is particularly designed to scale well to large real-world instances. Based on our previous work, we propose a multilevel refinement heuristic operating on hierarchically clustered input data. this way, the problem is coarsened until a manageable input size is reached, a solution is derived, and then step by step extended and refined until a valid solution for the whole original problem instance is obtained. As an enhancement to our previous work, we introduce the following extensions. Instead of considering an arbitrary integral number of slots for stations, we now use sets of predefined station configurations. Moreover, a local search is implemented as refinement step in the multilevel refinement heuristic and we now consider real-world input data for the city of Vienna.

关键词： Bike-Sharing Station Planning Problem Multilevel refinement Facility location problem Mixed integer linear programming

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Real-World Airline Crew Pairing optimization: Customized Genetic Algorithm Versus Column Generation Method 12th

Real-World Airline Crew Pairing Optimization: Customized Gen...

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12th international conference on Evolutionary Multi-Criterion optimization, EMO 2023

作者： Aggarwal, Divyam Saxena, Dhish Kumar Bäck, thomas Emmerich, Michael Optym Whitefield Karnataka Bengaluru560048 India Roorkee Uttarakhand Roorkee247667 India Leiden University Leiden2333 CA Netherlands

ISBN: (纸本)9783031272493

Airline crew pairing optimization problem (CPOP) aims to find a set of flight sequences (crew pairings) that cover all flights in an airline’s highly constrained flight schedule at minimum cost. Since crew cost is second only to the fuel cost, CPOP solutioning is critically important for an airline. However, CPOP is NP-hard, and tackling it is quite challenging. the literature suggests, that when the CPOP’s scale and complexity is reasonably limited, and an enumeration of all crew pairings is possible, then Metaheuristics are used, predominantly Genetic Algorithms (GAs). Else, Column Generation (CG) based Mixed integer programming techniques are used. Notably, as per the literature, a maximum of 45,000 crew pairings have been tackled by GAs. In a significant departure, this paper considers over 800 flights of a US-based large airline (with a monthly network of over 33,000 flights), and tests the efficacy of GAs by enumerating all 400,000+ crew pairings, apriori. Towards it, this paper proposes a domain-knowledge-driven customized-GA. the utility of incorporating domain-knowledge in GA operations, particularly initialization and crossover, is highlighted through suitable experiments. Finally, the proposed GA’s performance is compared with a CG-based approach (developed in-house by the authors). though the latter is found to perform better in terms of solution’s cost-quality and run time, it is hoped that this paper will help in better understanding the strengths and limitations of domain-knowledge-driven customizations in GAs, for solving combinatorial optimization problems, including CPOPs. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： combinatorial optimization

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Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3-D Deployment optimization of Directional Sensor Networks 5

Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3...

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5th international conference on Data-Driven optimization of Complex Systems, DOCS 2023

作者： Zhang, Yuntian Chen, Chen Wu, Tongyu Miao, Changhao Ding, Shuxin School of Automation Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Natl. Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Traffic Management Laboratory for High-Speed Railway Beijing100081 China

ISBN: (纸本)9798350393521

A major concern in designing sensor networks is the deployment problem. However, establishing an efficient algorithm for the real-world deployment problem is challenging due to three issues, which are 1) the realistic mixed-integer nonlinear programming problem (MINLP) with mixed-variable;2) the combinatorial subset selection problem;and 3) the expensive computational cost for fitness evaluation in the 3-D coverage problem. therefore, this paper addresses these challenges and proposes a surrogate-assisted hybrid metaheuristic for mixed-variable 3-D deployment optimization of directional sensor networks (DSNs). First, an MINLP with flexible coordinate transformation technique and an efficient mixed-variable encoding scheme are introduced to model and represent the problem. We propose hybrid metaheuristic which applies two reproduction methods respectively for discrete and continuous variables. Second, we design sparse population-based incremental learning (s-PBIL) to handle inherent subset selection problem. s-PBIL could accurately learn the required information, and automatically learn a sparse distribution. third, a mixed-variable surrogate with unifying space under Bayesian model management is incorporated to reduce the expensive computational cost. Experiment results on real-world deployment scenarios scaling from small-size to large-size show the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Sensor networks

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A genetic algorithm approach for the multidimensional two-way number partitioning problem 1

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7th international conference on Learning and Intelligent optimization, LION 7

作者： Pop, P.C. Matei, O. Department of Mathematics and Informatics Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Electrical Engineering Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9783642449734

ISBN: (纸本)9783642449727

this paper addresses the problem of partitioning a set of vectors into two subsets such that the sums per every coordinate should be exactly or approximately equal. this problem, introduced by Kojic [8], is called the multidimensional two-way number partitioning problem (MDTWNPP) and generalizes the classical two-way number partitioning problem. We propose an efficient genetic algorithm based heuristic for solving the multidimensional two-way number partitioning problem. the performances of our genetic algorithm have been compared with the existing numerical results obtained by CPLEX based on an integer linear programming formulation of the problem. the obtained preliminary results, in the case of medium and large instances, reveal that our proposed methodology performs very well in terms of both quality of the solutions and the computational times compared with the previous method of solving the MDTWNPP. © 2013 Springer-Verlag.

关键词： combinatorial optimization

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A polyhedral study of the cardinality constrained knapsack problem

A polyhedral study of the cardinality constrained knapsack p...

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9th international integer programming and combinatorial optimization conference, IPCO 2002

作者： de Farias, Ismael R. Nemhauser, George L. CORE 34 Voie du Roman Pays Louvain-la-Neuve1348 Belgium School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9783540478676

A cardinality constrained knapsack problem is a continuous knapsack problem in which no more than a specified number of nonnegative variables are allowed to be positive. this structure occurs, for example, in areas such as finance, location, and scheduling. Traditionally, cardinality constraints are modeled by introducing auxiliary 0-1 variables and additional constraints that relate the continuous and the 0-1 variables. We use an alternative approach, in which we keep in the model only the continuous variables, and we enforce the cardinality constraint through a specialized branching scheme and the use of strong inequalities valid for the convex hull of the feasible set in the space of the continuous variables. To derive the valid inequalities, we extend the concepts of cover and cover inequality, commonly used in 0-1 programming, to this class of problems, and we show how cover inequalities can be lifted to derive facet-defining inequalities. We present three families of non-trivial facet-defining inequalities that are lifted cover inequalities. Finally, we report computational results that demonstrate the effectiveness of lifted cover inequalities and the superiority of the approach of not introducing auxiliary 0-1 variables over the traditional MIP approach for this class of problems. © Springer-Verlag Berlin Heidelberg 2002.

关键词： combinatorial optimization

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Robust optimization for the hazardous materials transportation network design problem

Robust optimization for the hazardous materials transportati...

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7th international conference on combinatorial optimization and Applications, COCOA 2013

作者： Xin, Chunlin Letu, Qingge Bai, Yin School of Economics and Management Beijing University of Chemical Technology 100029 China Research Center for Operations Management and Strategy Decision China Data Mining Group NEC Laboratories 100084 China

ISBN: (纸本)9783319037790

We consider the problem of designing a transportation network for hazardous materials (HTNDP). For HTNDP, it was shown that deciding whether there exists an optimal path of risk 0 is NP-hard. A natural way to handle NP-hard problems is approximation solutions or FPT algorithms. We prove that HTNDP does not admit any approximation, neither any FPT algorithm, unless P=NP. the hazmat network design problem faces significant uncertainty in conflicting numbers of edge risk reported by different researchers and many factors affecting edge risk could induce different results since the edge risk is often difficult to characterize. In this paper, we use maximum regret criterion robust optimization to model the problem as a bi-level integer programming problem under edge risk uncertainty where an interval of possible risk values is associated with each arc. We present a heuristic approach that always finds a robust and stable hazmat network. At the end, we test our method on a random instance on a network of Guangdong province in China to illustrate the efficiency of our model and algorithm. Our experimental tests illustrate that the robust interval risk scenario network performs very well, and can handle the risk change better compared with the deterministic scenario network. Overall, the numerical analysis reveals that the maximum regret criterion robust optimization used in HTNDP is more conservative but has the merit of robustness. © Springer international Publishing Switzerland 2013.

关键词： Heuristic methods

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Advanced Energy Storage Management in Distribution Network 49

Advanced Energy Storage Management in Distribution Network

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49th Hawaii international conference on System Sciences (HICSS)

作者： Liu, Guodong Ceylan, Oguzhan Xiao, Bailu Starke, Michael Ollis, Ben King, Daniel Irminger, Philip Tomsovic, Kevin Oak Ridge Natl Lab Power & Energy Syst Grp Oak Ridge TN 37381 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9780769556703

With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. the proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. the optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. the proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.

关键词： Voltage regulation sensitivity coefficients linearization distributed generation (DG) multiobjective optimization unbalanced distribution network

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Optimal design of engineering structures

Optimal design of engineering structures

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international conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Aleksander Muc Institute of Machine Design Cracow University of Technology Kraków Poland

the basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discussed in the present paper. the effectiveness of the considered method is demonstrated by the possibility of the use of different form of objective functions, various and numerous nonlinear constraints and different types of design variables (continuous, discrete, real, integer). the sensitivity of the algorithm to the choice of the evolution strategy parameters is also discussed herein. the generality of the evolution strategy is illustrated by the analysis of three examples dealing with: the design of helical springs, the buckling of cylindrical composite panels and the buckling of pressure vessels with domed heads.

关键词： optimization Evolutionary computation Genetic algorithms Springs Linear programming Search problems Classification algorithms

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