Browsing by Keyword "Metaheuristics"
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Item Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives: A Critical Survey, Results, and Perspectives(2022-01-29) Para, Jesus; Del Ser, Javier; Nebro, Antonio J.; IAIn recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.Item jMetalPy: A Python framework for multi-objective optimization with metaheuristics: A Python framework for multi-objective optimization with metaheuristics(2019-12) Benítez-Hidalgo, Antonio; Nebro, Antonio J.; García-Nieto, José; Oregi, Izaskun; Del Ser, Javier; Quantum; IAThis paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.Item Let nature decide its nature: On the design of collaborative hyperheuristics for decentralized ephemeral environments: On the design of collaborative hyperheuristics for decentralized ephemeral environments(2018-11) Martinez, Aritz; Osaba, Eneko; Bilbao, Miren Nekane; Ser, Javier Del; Quantum; IAThe research community has traditionally aimed at the derivation and development of metaheuristic solvers, suited to deal with problems of very diverse characteristics. Unfortunately, it is often the case that new metaheuristic techniques are presented and assessed in a reduced set of cases, mostly due to the lack of computational resources to undertake extensive performance studies over a sufficiently diverse set of optimization benchmarks. This manuscript explores how ephemeral environments could be exploited to efficiently construct metaheuristic algorithms by virtue of a collaborative, distributed nature-inspired hyperheuristic framework specifically designed to be deployed over unreliable, uncoordinated computation nodes. To this end, the designed framework defines two types of nodes (trackers and peers, similarly to peer-to-peer networks), both reacting resiliently to unexpected disconnections of nodes disregarding their type. Peer nodes exchange their populations (i.e. constructed algorithms) asynchronously, so that local optima are avoided at every peer thanks to the contribution by other nodes. Furthermore, the overall platform is fully scalable, allowing its users to implement and share newly derived operators and fitness functions so as to enrich the diversity and universality of the heuristic algorithms found by the framework. Results obtained from in-lab experiments with a reduced number of nodes are discussed to shed light on the evolution of the best solution of the framework with the number of connected peers and the tolerance of the network to node disconnections.Item A Parallel Variable Neighborhood Search for Solving Real-World Production-Scheduling Problems(Springer Science and Business Media Deutschland GmbH, 2021-11-23) Osaba, Eneko; Loizaga, Erlantz; Goenaga, Xabier; Sanchez, Valentin; Camacho, David; Tino, Peter; Allmendinger, Richard; Yin, Hujun; Tallón-Ballesteros, Antonio J.; Tang, Ke; Cho, Sung-Bae; Novais, Paulo; Nascimento, Susana; Quantum; ADV_INTER_PLAT; HPAIn recent years, industry has evolved towards the efficient digitalization and optimization of products and processes. This situation is the consequence of the huge amount of information available in indus trial environments and its efficient management for reaching unprece dented productivity levels. The momentum that enjoys this application field has led to the proposal of advanced methods for the dealing of robotic processes in industrial plants, optimal packaging of goods and the efficient scheduling of production plans, among many others. This paper is focused on the last of these categories. More concretely, we present a Parallel Variable Neighborhood Search for solving an industrial problem in which a fixed amount of materials should be constructed into a limited number of production lines. The construction of these materials has sev eral particularities, such as the need of some specific tools to be correctly produced. It is also relevant to underscore that the problem solved in this research corresponds to a real-world situation, and that it is currently deployed in a production plant in the Basque Country (Spain).