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

共 731 条记录，以下是161-170 订阅

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Continuous Facility Location on Graphs 1

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21st international conference on integer programming and combinatorial optimization (IPCO)

作者： Hartmann, Tim A. Lendl, Stefan Woeginger, Gerhard J. Rhein Westfal TH Aachen Dept Comp Sci Aachen Germany Graz Univ Technol Dept Math Graz Austria

ISBN: (数字)9783030457716

ISBN: (纸本)9783030457716;9783030457709

We study a continuous facility location problem on undirected graphs where all edges have unit length and where the facilities may be positioned at the vertices as well as at interior points of the edges. the goal is to cover the entire graph with a minimum number of facilities with covering range (delta > 0. In other words, we want to position as few facilities as possible subject to the condition that every point on every edge is at distance at most delta from one of these facilities. We investigate this covering problem in terms of the rational parameter delta. We prove that the problem is polynomially solvable whenever delta is a unit fraction, and that the problem is NP-hard for all non unit fractions delta. We also analyze the parametrized complexity with the solution size as parameter: the resulting problem is fixed parameter tractable for all delta < 3/2, and it is W[2]-hard for all delta >= 3/2.

关键词： Location theory Graph theory Parametrized complexity

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New Symmetry-less ILP Formulation for the Classical One Dimensional Bin-Packing Problem 26th

New Symmetry-less ILP Formulation for the Classical One Dime...

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26th international Computing and Combinatorics conference (COCOON)

作者： Salem, Khadija Hadj Kieffer, Yann Univ Tours CNRS LIFAT EA 6300 ROOT ERL CNRS 7002 64 Ave Jean Portalis F-37200 Tours France Univ Grenoble Alpes LCIS Grenoble INP F-26000 Valence France

ISBN: (纸本)9783030581503;9783030581497

In this article, we address the classical One-Dimensional Bin Packing Problem (1D-BPP), an NP-hard combinatorial optimization problem. We propose a new formulation of integer linear programming for the problem, which reduces the search space compared to those described in the literature, as well as two families of cutting planes. Computational experiments are conducted on the data-set found in BPPLib and the results show that it is possible to solve more instances and to decrease the computation time by using our new formulation.

关键词： Bin packing integer linear programming Cutting plane

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Cost optimization for the Allocation, Production, and Distribution of a Plastic Manufacturing Company Using integer Linear programming

Cost Optimization for the Allocation, Production, and Distri...

引用

international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Alezander Mikhail O. Galindo Elmer P. Dadios Robert Kerwin C. Billones Ira C. Valenzuela De La Salle University Manila Philippines

ISBN: (纸本)9781665401685

Optimizing the allocation of raw materials, production of raw materials to finished products, and the distribution of finished products can be quite difficult especially if the network of suppliers, production plants, and customers is vast. Linear programming (LP) can be utilized to ensure optimality. Specifically, integer linear programming (ILP) is used in this paper. An ILP model was built to determine from which suppliers and how many raw materials from these suppliers should be acquired, how many raw materials each production plant of the company should acquire, how many of the finished goods each production plant should produce, and from which production plants the finished products should be distributed to the customers. the aim of the model is to minimize total cost incurred which is composed of raw material purchase costs, production costs, and transportation costs. Data such as raw material supply, raw material costs, raw material requirements, production capabilities, production costs, raw material and finished product conversions, customer demand, and transportation costs were gathered from the plastic manufacturing company. the software MATLAB was used to determine the optimal solution. It was then determined that the total cost would be minimized from ₱23,615,400 to ₱21,940,000. through the use of ILP, the total cost was reduced by ₱1,675,400 or 7.09%. (Conversion rate: ₱50 ~ ${\$}$1).

关键词： Costs Transportation Production Companies integer linear programming Linear programming Raw materials

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Realtime Gray-Box Algorithm Configuration 16th

Realtime Gray-Box Algorithm Configuration

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16th international conference on Learning and Intelligent optimization, LION 16 2022

作者： Weiss, Dimitri Tierney, Kevin Decision and Operation Technologies Group Bielefeld University Bielefeld Germany

ISBN: (纸本)9783031248658

A solver’s runtime and the quality of the solutions it generates are strongly influenced by its parameter settings. Finding good parameter configurations is a formidable challenge, even for fixed problem instance distributions. However, when the instance distribution can change over time, a once effective configuration may no longer provide adequate performance. Realtime algorithm configuration (RAC) offers assistance in finding high-quality configurations for such distributions by automatically adjusting the configurations it recommends based on instances seen so far. Existing RAC methods treat the solver to be configured as a black box, meaning the solver is given a configuration as input, and it outputs either a solution or runtime as an objective function for the configurator. However, analyzing intermediate output from the solver can enable configurators to avoid wasting time on poorly performing configurations. To this end, we propose a gray-box approach that utilizes intermediate output during evaluation and implement it within *** pairwise comparisons to determine whether ongoing evaluations can be terminated to free resources. We compare our realtime gray-box configurator to a black-box equivalent on several experimental settings and show that our approach reduces the total solving time in several scenarios.

关键词： integer programming

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An Approximate Solution of a GNSS Satellite Selection Problem Using Semidefinite programming 1

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10th international conference on optimization and Applications (OPTIMA)

作者： Rapoport, Lev Tormagov, Timofey Russian Acad Sci VA Trapeznikov Inst Control Sci Moscow Russia Moscow Inst Phys & Technol Dolgoprudnyi Moscow Region Russia

ISBN: (数字)9783030386030

ISBN: (纸本)9783030386030;9783030386023

When processing multiple navigation satellite systems, including GPS, GLONASS, Galileo, Beidou, QZSS, the overall number of the pseudorange and carrier phase signals can exceed several tens. On the other hand, a much smaller number of them is usually sufficient to achieve necessary precision of positioning. Also, some parts of precise positioning algorithms, like carrier phase ambiguity resolution, are very sensitive to the problem dimension as they include the integer search. To reduce computational cost of positioning, the optimal choice of signals involved in computations should be performed. optimization is constrained by a given number of satellite signals to be chosen for processing. this optimization problem falls into the class of binary optimization problems which are hard for precise solution. In this paper, we present approaches to an approximate solution of the optimal selection problem. After the linear relaxation of binary constraints, the relaxed problem is convex and can be transformed to semidefinite programming or second-order cone programming problems. the optimal solution of the relaxed problem can be considered as a lower bound of a combinatorial optimization problem. After rounding non-integer variables the approximate solution is obtained. As a result, two-sided bounds of the optimum are obtained. In practice, the approximate solution is very close to precise solution for most real world cases. Because the relaxed problem is convex, it can be solved efficiently.

关键词： GNSS navigation Second-order cone programming Semidefinite programming Satellite selection GDOP

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Continuous Cartesian Genetic programming with Particle Swarm optimization 18th

Continuous Cartesian Genetic Programming with Particle Swarm...

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Joint conferences on 18th international conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018

作者： Loebl, Jaroslav Rozinajová, Viera Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava 4842 16 Slovakia

ISBN: (纸本)9783030166595

Cartesian Genetic programming (CGP) is a type of Genetic programming, which uses a sequence of integers to represent an executable graph structure. the most common way of optimizing the CGP is to use a simple evolutionary strategy with mutations, which randomly changes the integer values of integer sequence. We propose an alternative genotype-phenotype mapping procedure for CGP allowing usage of real-valued numbers in genotype. Novel representation allows continuous transition between various functions and inputs of each given node (hence the name, Continuous CGP), which means, that the optimization of CGP individual is transformed from combinatorial optimization problem to continuous optimization problem. this allows leveraging various metaheuristic optimization algorithms. In this paper, we present results obtained by Particle Swarm optimization algorithm, showing that continuous representation is able to outperform classic CGP in some benchmarks and provides competitive results with one of the best performing symbolic regression systems in literature. © 2020, Springer Nature Switzerland AG.

关键词： Genetic programming

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Optimizing Student Course Preferences in School Timetabling 17th

Optimizing Student Course Preferences in School Timetabling

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17th international conference on the Integration of Constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Hoshino, Richard Fabris, Irene Quest Univ Canada Squamish BC Canada

ISBN: (纸本)9783030589424;9783030589417

School timetabling is a complex problem in combinatorial optimization, requiring the best possible assignment of course sections to teachers, timeslots, and classrooms. there exist standard techniques for generating a school timetable, especially in cohort-based programs where students take the same set of required courses, along with several electives. However, in small interdisciplinary institutions where there are only one or two sections of each course, and there is much diversity in course preferences among individual students, it is very difficult to create an optimal timetable that enables each student to take their desired set of courses while satisfying all of the required constraints. In this paper, we present a two-part school timetabling algorithm that was applied to generate the optimal Master Timetable for a Canadian all-girls high school, enrolling students in 100% of their core courses and 94% of their most desired electives. We conclude the paper by explaining how this algorithm, combining graph coloring with integer linear programming, can benefit other institutions that need to consider student course preferences in their timetabling.

关键词： School timetabling Post Enrollment Course Timetabling Problem integer programming Graph coloring optimization

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Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine 1

引用

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

作者： Runarsson, thomas Philip Univ Iceland Sch Engn & Nat Sci Reykjavik Iceland

ISBN: (数字)9783030386290

ISBN: (纸本)9783030386290;9783030386283

the problem of generating surgery schedules is formulated as a mathematical model with probabilistic constraints. the approach presented is a new method for tackling probabilistic constraints using machine learning. the technique is inspired by models that use slacks in capacity planning. Essentially support vector classification is used to learn a linear constraint that will replace the probabilistic constraint. the data used to learn this constraint is labeled using Monte Carlo simulations. this data is iteratively discovered, during the optimization procedure, and augmented to the training set. the linear support vector classifier is then updated during search, until a feasible solution is discovered. the stochastic surgery model presented is inspired by real challenges faced by many hospitals today and tested on real-life data.

关键词： Support vector classification Surgery scheduling Stochastic mixed integer programming Monte Carlo simulations

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Optimal Selective Maintenance for Systems Operating Under Random Environments 7

Optimal Selective Maintenance for Systems Operating Under Ra...

引用

7th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Khatab, A. Diallo, C. Venkatadri, U. Aghezzaf, E-H Univ Lorraine LGIPM F-57000 Metz France Dalhousie Univ Dept Ind Engn Halifax NS Canada Univ Ghent Dept Ind Syst Engn & Prod Design Ghent Belgium Flanders Make Ind Syst Engn ISyE Lommel Belgium

ISBN: (纸本)9781728159539

this paper proposes a new variant of the joint selective maintenance and repairperson assignment problem (SM&RAP) in multicomponent systems. the system carry out missions under random operating environments (OE). Between consecutive missions there are scheduled breaks of finite length within which maintenance actions are performed. Current selective maintenance (SM) models assume that the OE is static and does not change over the course of a mission. However, many mission critical systems are subjected to harsh and varying OE which impacts the mission success probability. this paper develops a new SM optimization model in systems under a random OE. the OE randomness is modeled as a random shock process, and the occurrence of a shock directly impacts the failure process of the components. A nonlinear SM optimization model along with its binary integer programming version are developed and solved to optimality. Numerical experiments are provided where the results obtained demonstrate the added value and the benefit of including the impact of the OE when dealing with SM&RAP.

关键词： combinatorial optimization

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MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines 1

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20th European conference on Evolutionary Computation in combinatorial optimization (EvoCOP)

作者： Saber, Takfarinas Brevet, David Botterweck, Goetz Ventresque, Anthony Univ Coll Dublin Sch Comp Sci Dublin Ireland Lab Informat Modelisat & Optimisat Syst Aubiere France Univ Limerick Limerick Ireland

ISBN: (数字)9783030436803

ISBN: (纸本)9783030436797;9783030436803

Software Product Lines Engineering (SPLE) proposes techniques to model, create and improve groups of related software systems in a systematic way, with different alternatives formally expressed, e.g., as Feature Models. Selecting the 'best' software system(s) turns into a problem of improving the quality of selected subsets of software features (components) from feature models, or as it is widely known, Feature Configuration. When there are different independent dimensions to assess how good a software product is, the problem becomes even more challenging - it is then a multi-objective optimisation problem. Another big issue for software systems is evolution where software components change. this is common in the industry but, as far as we know, there is no algorithm designed to the particular case of multi-objective optimisation of evolving software product lines. In this paper we present MILPIBEA, a novel hybrid algorithm which combines the scalability of a genetic algorithm (IBEA) with the accuracy of a mixed-integer linear programming solver (IBM ILOG CPLEX). We also study the behaviour of our solution (MILPIBEA) in contrast with SATIBEA, a state-of-the-art algorithm in static software product lines. We demonstrate that MILPIBEA outperforms SATIBEA on average, especially for the most challenging problem instances, and that MILPIBEA is the one that continues to improve the quality of the solutions when SATIBEA stagnates (in the evolving context).

关键词： Software product line Feature selection Multi-objective optimisation Evolutionary algorithm Mixed-integer linear programming

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