Download Free Metaheuristics For Scheduling In Industrial And Manufacturing Applications Book in PDF and EPUB Free Download. You can read online Metaheuristics For Scheduling In Industrial And Manufacturing Applications and write the review.

During the past decades scheduling has been among the most studied op- mization problemsanditisstillanactiveareaofresearch!Schedulingappears in many areas of science, engineering and industry and takes di?erent forms depending on the restrictions and optimization criteria of the operating en- ronments [8]. For instance, in optimization and computer science, scheduling has been de?ned as “the allocation of tasks to resources over time in order to achieve optimality in one or more objective criteria in an e?cient way” and in production as “production schedule, i. e. , the planning of the production or the sequence of operations according to which jobs pass through machines and is optimal with respect to certain optimization criteria. ” Although there is a standardized form of stating any scheduling problem, namely “e?cient allocation ofn jobs onm machines –which can process no more than one activity at a time– with the objective to optimize some - jective function of the job completion times”, scheduling is in fact a family of problems. Indeed, several parameters intervene in the problem de?nition: (a) job characteristics (preemptive or not, precedence constraints, release dates, etc. ); (b) resource environment (single vs. parallel machines, un- lated machines, identical or uniform machines, etc. ); (c) optimization criteria (minimize total tardiness, the number of late jobs, makespan, ?owtime, etc. ; maximize resource utilization, etc. ); and, (d) scheduling environment (static vs. dynamic,intheformerthenumberofjobstobeconsideredandtheirready times are available while in the later the number of jobs and their charact- istics change over time).
This book discusses the main techniques and newest trends to manage and optimize the production and service systems. The book begins by examining the three main levels of decision systems in production: the long term (strategic), the middle term (tactical) and short term (operational). It also considers online management as a new level (a sub level of the short term). As each level encounters specific problems, appropriate approaches to deal with these are introduced and explained. These problems include the line design, the line balancing optimization, the physical layout of the production or service system, the forecasting optimization, the inventory management, the scheduling etc. Metaheuristics for Production Systems then explores logistic optimization from two different perspectives: internal (production management), addressing issues of scheduling, layout and line designs, and external (supply chain management) focusing on transportation optimization, supply chain evaluation, and location of production. The book also looks at NP-hard problems that are common in production management. These complex configurations may mean that optimal solutions may not be reached due to variables, but the authors help provide a good solution for such problems. The effective new results and solutions offered in this book should appeal to researchers, managers, and engineers in the production and service industries.
Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book “Variants of Evolutionary Algorithms for Real-World Applications” aims to promote the practitioner’s view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation.
This book constitutes the refereed post-conference proceedings of the 11th and the 12th International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022, held in December 2021 and 2022. Due to COVID-19 pandemic both conferences were held virtually. The 23 full papers of BDTA 2021 and BDTA 2022 were selected from 61 submissions and present all big data technologies, such as big data collection and storage, big data management and retrieval, big data mining approaches, big data visualization, and new domains and novel applications related to these technologies.
This book constitutes the refereed proceedings of the 8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2017, held in Lyon, France, in August 2017. The 19 revised full papers presented were carefully reviewed and selected from 27 submissions. The papers are organized in the following topical sections: scheduling; knowledge engineering; modeling, simulation and reconfiguration; energy systems;and MAS in various areas.
This book comprises selected papers from the 16th International Conference on Intelligent Systems Design and Applications (ISDA’16), which was held in Porto, Portugal from December 1 to16, 2016. ISDA 2016 was jointly organized by the Portugual-based Instituto Superior de Engenharia do Porto and the US-based Machine Intelligence Research Labs (MIR Labs) to serve as a forum for the dissemination of state-of-the-art research and development of intelligent systems, intelligent technologies, and applications. The papers included address a wide variety of themes ranging from theories to applications of intelligent systems and computational intelligence area and provide a valuable resource for students and researchers in academia and industry alike.
Across a variety of disciplines, data and statistics form the backbone of knowledge. To ensure the reliability and validity of data, appropriate measures must be taken in conducting studies and reporting findings. Research Methods: Concepts, Methodologies, Tools, and Applications compiles chapters on key considerations in the management, development, and distribution of data. With its focus on both fundamental concepts and advanced topics, this multi-volume reference work will be a valuable addition to researchers, scholars, and students of science, mathematics, and engineering.
The 6th ACIS International Conference on Software Engineering, Research, Management and Applications (SERA 2008) was held in Prague in the Czech Republic on August 20 – 22. SERA ’08 featured excellent theoretical and practical contributions in the areas of formal methods and tools, requirements engineering, software process models, communication systems and networks, software quality and evaluation, software engineering, networks and mobile computing, parallel/distributed computing, software testing, reuse and metrics, database retrieval, computer security, software architectures and modeling. Our conference officers selected the best 17 papers from those papers accepted for presentation at the conference in order to publish them in this volume. The papers were chosen based on review scores submitted by members or the program committee, and underwent further rounds of rigorous review.
Most swarm intelligence algorithms were devised for continuous optimization problems. However, they have been adapted for discrete optimization as well with applications in different domains. This survey aims at providing an updated review of research of swarm intelligence algorithms for discrete optimization problems, comprising combinatorial or binary. The biological inspiration that motivated the creation of each swarm algorithm is introduced, and later, the discretization and encoding methods are used to adapt each algorithm for discrete problems. Methods are compared for different classes of problems and a critical analysis is provided, pointing to future trends.
Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.