检索结果-内蒙古大学图书馆

12th International Conference on Computational Logistics (ICCL)

作者： Damm, Ricardo de Brito Ronconi, Debora P. Univ Sao Paulo Polytech Sch Sao Paulo Brazil

ISBN: (纸本)9783030876722;9783030876715

Every day many service companies need to plan the tasks that will be carried out by its field staff. Maintenance service technicians have to perform a set of jobs at different locations in a city or state. This problem can be defined as the Service Technician Routing and Scheduling Problem in which tasks have different priorities and time windows, and technicians have different skills and working hours. Scheduling must account for technicians' lunch breaks, which must be respected. Each task is performed by only one technician. To ensure quality customer service and consumer rights are upheld, a novel approach is proposed: to address the problem in a multi-objective context aiming to execute the priority tasks and, simultaneously, to serve the customers at the beginning of their time windows. A Multi-objective biased random-key genetic algorithm (BRKGA) was customized to tackle this NP-hard optimization problem and then compared with the Non-dominated Sorting genetic algorithm II (NSGA-II). The analyzed methods showed similar performance for small instances, but for medium- and large-sized instances the proposed method presented superior performance and more robust results.

关键词： Multiple objective programming Routing and scheduling technicians Time windows biased random-key genetic algorithm NSGA-II

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for Regression Test Case Prioritization

A Biased Random-Key Genetic Algorithm for Regression Test Ca...

引用

5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Carballo, Pablo Perera, Pablo Rama, Santiago Pedemonte, Martin Univ Republica Inst Computac Montevideo Uruguay

ISBN: (纸本)9781538646267

The Test Case Prioritization Problem (TCPP) is a real-world problem that arises in regression testing. It lies in finding an ordering of the test cases of a test suite, such that test cases ordered first should be run first. The classical approach to solve this problem employing GAs is to use permutational encoding, but it requires specific operators to keep the feasibility of the solutions. The biased random-key genetic algorithm (BRKGA) follows a different philosophy for dealing with permutations, using a string of real numbers and a decoder for computing the permutation. In this paper, we propose a BRKGA for solving the TCPP. The experimental evaluation on eleven instances of seven real-world programs shows that BRKGA is able to outperform two different permutational encoding based GAs (with order and cycle crossover operators), and that it has at least a similar performance than another permutational encoding based GA (with partially-mapped crossover) and a GA from a previous work specially conceived for tackling the TCPP.

关键词： Regression testing Search-based software engineering biased random-key genetic algorithm Test case prioritization problem

来源：评论

学校读者我要写书评

暂无评论

A Hybrid biased random key genetic algorithm for a Production and Cutting Problem 15

A Hybrid Biased Random Key Genetic Algorithm for a Productio...

引用

15th IFAC Symposium on Information Control Problems in Manufacturing

作者： Goncalves, Jose Fernando Univ Porto Fac Econ INESC TEC LIAAD Rua Campo Alegre 823 P-4100 Oporto Portugal

This paper deals with a very common problem in the home-textile industry. Given a set of orders of small rectangles of fabric the problem consists of determining the lengths and widths of a set of large rectangles of fabric to be produced and the corresponding cutting patterns. The objective is to minimize the total quantity of fabric necessary to satisfy all orders. The approach proposed uses a biased random-key genetic algorithm for generating sets of cutting patterns which are the input to a sequential heuristic procedure which generates a solution. Experimental tests based on a set of 100 random generated problems with known optimal solution validate quality of the approach. (C) 2013, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved,

关键词： biased random-key genetic algorithm Cutting pattern Cutting problem Sequential heuristic procedure random-keys

来源：评论

学校读者我要写书评

暂无评论

biased random-key genetic algorithms with applications in telecommunications

引用

TOP 2012年第1期20卷 130-153页

作者： Resende, Mauricio G. C. AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA

This paper surveys several applications of biased random-key genetic algorithms (BRKGA) in optimization problems that arise in telecommunications. We first review the basic concepts of BRKGA. This is followed by a description of BRKGA-based heuristics for routing in IP networks, design of survivable IP networks, redundant server location for content distribution, regenerator location in optical networks, and routing and wavelength assignment in optical networks.

关键词： Optimization in telecommunications genetic algorithm biased random-key genetic algorithm random keys Combinatorial optimization Heuristics Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing

引用

COMPUTERS & OPERATIONS RESEARCH 2025年 176卷

作者： Bierbuesse, Jan Moench, Lars Biele, Alexander Univ Hagen Dept Math & Comp Sci D-58097 Hagen Germany Airbus Operat GmbH Programme & Integrated Planning D-21129 Hamburg Germany

A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.

关键词： Multi-mode Time-constrained Project Scheduling Problem Generalized Temporal Constraints biased random-key genetic algorithm Aircraft Manufacturing Problem Instances

来源：评论

学校读者我要写书评

暂无评论

Hybrid algorithms for placement of virtual machines across geo-separated data centers

引用

JOURNAL OF COMBINATORIAL OPTIMIZATION 2019年第3期38卷 748-793页

作者： Stefanello, Fernando Aggarwal, Vaneet Buriol, Luciana S. Resende, Mauricio G. C. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Purdue Univ Sch Ind Engn Sch Elect & Comp Engn W Lafayette IN 47907 USA Amazon Com Seattle WA 98109 USA Univ Washington Ind & Syst Engn Seattle WA 98105 USA

Cloud computing has emerged as a new paradigm for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers (VMPlacement), meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard generalized quadratic assignment problem (GQAP). In this paper, we present a greedy randomized adaptive search procedure and a biased random-key genetic algorithm, both hybridized with a path-relinking strategy and a local search based on variable neighborhood descent for solving this problem. The hybrid heuristics are also tested on instances of the GQAP. We show that both algorithms are effective in quickly solving small and large instances of VMPlacement problem, especially when the path-relinking is used. For GQAP, the results outperform the previous state-of-the-art algorithms.

关键词： Combinatorial optimization Cloud computing biased random-key genetic algorithm GRASP Path-relinking

来源：评论

学校读者我要写书评

暂无评论

A cloud brokerage approach for solving the resource management problem in multi-cloud environments

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2016年 95卷 16-26页

作者： Heilig, Leonard Lalla-Ruiz, Eduardo Voss, Stefan Univ Hamburg Inst Informat Syst IWI Hamburg Germany Pontificia Univ Catolica Valparaiso Escuela Ingn Ind Valparaiso Chile

Cloud computing is increasingly becoming a mainstream technology-delivery model from which companies and research aim to gain value. As different cloud providers offer cloud services in various forms, there is a huge potential of optimizing the selection of those services to better fulfill user-, i.e., consumer- and application-related requirements. Recently, multi-cloud environments have been introduced thus making it possible to execute applications not only on single-provider resources, but also by using resources from multiple cloud providers. Due to the growing complexity in cloud marketplaces, a cloud brokerage mechanism, interacting on behalf of the consumers with various cloud providers, can be used to provide decision support for consumers. In this paper, we address the Cloud Resource Management Problem in multi-cloud environments that is a recent optimization problem aimed at reducing the monetary cost and the execution time of consumer applications using Infrastructure as a Service of multiple cloud providers. Due to the fact that consumers require real-time and high-quality solutions to economically automate cloud resource management and corresponding deployment processes, we propose an efficient biased random-key genetic algorithm. The computational experiments over a large benchmark suite generated based on real cloud market resources indicate that the performance of our approach outperforms the approaches proposed in the literature. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cloud computing Multi-cloud Cloud brokerage IaaS biased random-key genetic algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

randomized heuristics for the family traveling salesperson problem

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2014年第1期21卷 41-57页

作者： Moran-Mirabal, L. F. Gonzalez-Velarde, J. L. Resende, M. G. C. Tecnol Monterrey Qual & Mfg Ctr Monterrey Mexico AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA

This paper introduces the family traveling salesperson problem (FTSP), a variant of the generalized traveling salesman problem. In the FTSP, a subset of nodes must be visited for each node cluster in the graph. The objective is to minimize the distance traveled. We describe an integer programming formulation for the FTSP and show that the commercial grade integer programming solver CPLEX11 can only solve small instances of the problem in reasonable running time. We propose two randomized heuristics for finding optimal and near-optimal solutions of this problem. These heuristics are a biased random-key genetic algorithm and a GRASP with evolutionary path-relinking. Computational results comparing both heuristics are presented in this study.

关键词： family traveling salesperson problem distance minimization randomized heuristics biased random-key genetic algorithm GRASP

来源：评论

学校读者我要写书评

暂无评论

The capacitated family traveling salesperson problem

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第4期31卷 2123-2153页

作者： Dominguez-Casasola, Saul Gonzalez-Velarde, Jose Luis Rios-Solis, Yasmin a. Reyes-Vega, Kevin Alain Tecnol Monterrey Sch Engn & Sci Monterrey Mexico

The capacitated family traveling salesperson problem (CFTSP) is about a graph in which nodes are partitioned into disjoint and differently weighted families. The objective is to find the shortest route that visits a given number of nodes in each family with a set of capacitated agents. The CFTSP is a new variant of the family traveling salesman problem (FTSP). Nevertheless, existing exact and metaheuristic methods for the FSTP cannot be straightforwardly applied due to the feasibility condition that requires agents to stay within their capacity. Thus, we propose integer linear programming formulations with five different subtour inequalities sets that are tested and compared with the classical approaches. In addition, we propose a biased random-key genetic algorithm with four decoder algorithms that find high-quality solutions in short computational times. Based on our experiments, we have found that our methods are effective in addressing subtour avoidance and ensuring feasible assignments.

关键词： metaheuristics integer programming capacitated family traveling salesperson problem traveling salesman problem biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A flow-based model for the multivehicle covering tour problem with route balancing

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第5期31卷 2930-2955页

作者： Ota, Cristina Teruko Fiorotto, Diego Jacinto Ghidini, Carla Taviane Lucke da Silva de Oliveira, Washington Alves Univ Estadual Campinas UNICAMP Fac Ciencias Aplicadas R Pedro Zaccaria 1300 BR-13484350 Limeira SP Brazil

This paper introduces a new flow-based mathematical formulation for the multivehicle covering tour problem. This problem determines a set of vehicle routes for the available fleet;therefore, the total distance traveled is minimal. In the proposed mathematical formulation, each vertex represents a location of interest, and these vertices belong to three sets: vertices that can be visited;vertices that must be visited;and vertices that must be covered, in the sense that each vertex must be close to a visited vertex in a route. The proposed formulation aims to find solutions with balanced routes (all the routes with an equal or similar number of visited nodes) and extends the previous formulations proposed in the literature by considering a two-index vehicle flow formulation with a decision variable to find the balanced routes. It constitutes an NP-hard integer programming problem based on a two-commodity flow model, which is solved by applying the branch-and-cut and biased random-key genetic algorithms. This paper reports computational results for some adapted instances from TSPLIB to evaluate the efficiency of the proposed model. The computational results validate the branch-and-cut and genetic algorithms, whereby the latter performs better in large-size instances with reduced computational runtime. Finally, the used approaches obtained competitive solutions compared to the literature.

关键词： vehicle routing covering tour problem branch-and-cut biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：