5,460 research outputs found

    Advances and Novel Approaches in Discrete Optimization

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    Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    Network automation: challenges, enablers, and benefits

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    Communication infrastructures are evolving towards an ad-hoc service provisioning scenario where programmability and flexibility are fundamental concepts. Network automation is expected to play a vital role in streamlining all aspects of the service provisioning process (i.e., deployment, maintenance, and tear down). However, to fully realize this autonomous operation vision, closed-loop automation procedures need to be developed.This tutorial will present the main motivations and challenges behind designing and operating closed-loop autonomous decision-making processes, including a brief overview of current standardization initiatives. The tutorial will then address several use cases showcasing how network automation can alleviate the complexity of the service provisioning processes and the benefits brought in by the introduction of network automation

    Editorial: Sweet sixteen

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    This is the traditional triennial note used by the editors to give the readers of 4OR information on the state of the journal and its future. In the 3years that have passed since the last editorial note (Liberti et al. in Q J Oper 13:1–13, 2015), three volumes (each containing four issues) of the journal have been published: vol. 13 (2015), vol. 14 (2016), and vol. 15 (2017)

    A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

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    Increasing energy price and requirements to reduce emission are new chal-lenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop envi-ronment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness. Keywords: Energy efficient production plannin

    Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population

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    Independent Job Scheduling is one of the most useful versions of scheduling in grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the grid resources. Besides traditional restrictions, mapping of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of the system. Because of the complexity of the problem, the heuristic and meta-heuristic approaches are the most feasible methods of scheduling in grids due to their ability to deliver high quality solutions in reasonable computing time. One class of such meta-heuristics is Hierarchic Genetic Strategy (HGS). It is defined as a variant of Genetic Algorithms (GAs) which differs from the other genetic methods by its capability of concurrent search of the solution space. In this work, we present an implementation of HGS for Independent Job Scheduling in dynamic grid environments. We consider the bi-objective version of the problem in which makespan and flowtime are simultaneously optimized. Based on our previous work, we improve the HGS scheduling strategy by enhancing its main branching operations. The resulting HGS-based scheduler is evaluated under the heterogeneity, the large scale and dynamics conditions using a grid simulator. The experimental study showed that the HGS implementation outperforms existing GA-based schedulers proposed in the literature.Peer ReviewedPostprint (author's final draft
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