Author: Prakarn UnachakPublisher:
ISBN:
Category : Business logistics
Languages : en
Pages : 204
Book Description
An Adaptive Representation for a Genetic Algorithm in Solving Flexible Job-shop Scheduling and Rescheduling Problems
Author: Prakarn UnachakPublisher:
ISBN:
Category : Business logistics
Languages : en
Pages : 204
Book Description
A Promising Genetic Algorithm Approach to Job-shop Scheduling, Rescheduling, and Open-shop Scheduling Problems
Author: Hsiao-Lan FangPublisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 9
Book Description
Abstract: "The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in different parts of the genome. Our approach also promises to effectively address the open-shop scheduling problem and the job-shop rescheduling problem."
Evolutionary Search and the Job Shop
Author: Dirk C. MattfeldPublisher: Springer Science & Business Media
ISBN: 3662117126
Category : Business & Economics
Languages : en
Pages : 162
Book Description
Production scheduling dictates highly constrained mathematical models with complex and often contradicting objectives. Evolutionary algorithms can be formulated almost independently of the detailed shaping of the problems under consideration. As one would expect, a weak formulation of the problem in the algorithm comes along with a quite inefficient search. This book discusses the suitability of genetic algorithms for production scheduling and presents an approach which produces results comparable with those of more tailored optimization techniques.
A Genetic Algorithm for the Flexible Job-shop Scheduling Problem
Author: F. PezzellaMultiobjective Scheduling by Genetic Algorithms
Author: Tapan P. BagchiPublisher: Springer Science & Business Media
ISBN: 1461552370
Category : Business & Economics
Languages : en
Pages : 369
Book Description
Multiobjective Scheduling by Genetic Algorithms describes methods for developing multiobjective solutions to common production scheduling equations modeling in the literature as flowshops, job shops and open shops. The methodology is metaheuristic, one inspired by how nature has evolved a multitude of coexisting species of living beings on earth. Multiobjective flowshops, job shops and open shops are each highly relevant models in manufacturing, classroom scheduling or automotive assembly, yet for want of sound methods they have remained almost untouched to date. This text shows how methods such as Elitist Nondominated Sorting Genetic Algorithm (ENGA) can find a bevy of Pareto optimal solutions for them. Also it accents the value of hybridizing Gas with both solution-generating and solution-improvement methods. It envisions fundamental research into such methods, greatly strengthening the growing reach of metaheuristic methods. This book is therefore intended for students of industrial engineering, operations research, operations management and computer science, as well as practitioners. It may also assist in the development of efficient shop management software tools for schedulers and production planners who face multiple planning and operating objectives as a matter of course.
Genetic Programming for Production Scheduling
Author: Fangfang ZhangPublisher: Springer Nature
ISBN: 981164859X
Category : Computers
Languages : en
Pages : 357
Book Description
This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP’s performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
A Genetic Algorithm-based Scheduling System for Dynamic Job Shop Scheduling Problems
Author: Shyh-Chang LinPublisher:
ISBN:
Category : Production control
Languages : en
Pages : 300
Book Description
An Optimal Genetic Algorithm for Flexible Job-shop Scheduling Problem
Author: 林峻良OmeGA
Author: Dimitri KnjazewPublisher: Springer Science & Business Media
ISBN: 146150807X
Category : Computers
Languages : en
Pages : 165
Book Description
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems addresses two increasingly important areas in GA implementation and practice. OmeGA, or the ordering messy genetic algorithm, combines some of the latest in competent GA technology to solve scheduling and other permutation problems. Competent GAs are those designed for principled solutions of hard problems, quickly, reliably, and accurately. Permutation and scheduling problems are difficult combinatorial optimization problems with commercial import across a variety of industries. This book approaches both subjects systematically and clearly. The first part of the book presents the clearest description of messy GAs written to date along with an innovative adaptation of the method to ordering problems. The second part of the book investigates the algorithm on boundedly difficult test functions, showing principled scale up as problems become harder and longer. Finally, the book applies the algorithm to a test function drawn from the literature of scheduling.
Service Orientation in Holonic and Multi-Agent Manufacturing
Author: Theodor BorangiuPublisher: Springer
ISBN: 3319737511
Category : Technology & Engineering
Languages : en
Pages : 498
Book Description
This book gathers the peer-reviewed papers presented at the seventh edition of the international workshop "Service Orientation in Holonic and Multi-Agent Manufacturing - SOHOMA'17", held on October 19-20, 2017 and organized by the University of Nantes, France in collaboration with the CIMR Research Centre in Computer Integrated Manufacturing and Robotics at the University Politehnica of Bucharest, Romania, the LAMIH Laboratory of Industrial and Human Automation Control, Mechanical Engineering and Computer Science at the University of Valenciennes and Hainaut-Cambrésis, France and the CRAN Research Centre for Automatic Control, Nancy at the University of Lorraine, France. The main objective of SOHOMA'17 was to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions addressing trends in service orientation of agent-based control technologies with distributed intelligence. The book is organized in eight parts, each with a number of chapters describing research in current domains of the digital transformation in manufacturing and trends in future service and computing oriented manufacturing control: Part 1: Advanced Manufacturing Control, Part 2: Big Data Management, Part 3: Cyber-Physical Production Systems, Part 4: Cloud- and Cyber-Physical Systems for Smart and Sustainable Manufacturing, Part 5: Simulation for Physical Internet and Intelligent & Sustainable Logistics Systems, Part 6: Formal Methods and Advanced Scheduling for Future Industrial Systems, Part 7: Applications and Demonstrators, Part 8: Production and Logistic Control Systems. The contributions focus on how the digital transformation, such as the one advocated by "Industry 4.0" or "Industry of the future" concepts, can improve the maintainability and the sustainability of manufacturing processes, products, and logistics. Digital transformation relates to the interaction between the physical and informational worlds and is realized by virtualization of products, processes and resources managed as services.