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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.
Patricia Shiroma explores the possibility of combining genetic algorithms with simulation studies in order to generate efficient production schedules for parallel manufacturing processes. The result is a flexible, highly effective production scheduling system.
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.
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.
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 dissertation, "A Genetic Algorithm Approach in Distributed Scheduling in Multi-factory Production Networks" by Sai-ho, Chung, 鍾世豪, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled A Genetic Algorithm Approach in Distributed Scheduling in Multi-Factory Production Networks Submitted by CHUNG Sai Ho For the Degree of Doctor of Philosophy at the University of Hong Kong in December 2006 In recent years, many companies have switched from traditional single-factory to multi-factory production in order to increase their international competitiveness because of globalization. These factories may be geographically distributed in different locations. This allows them to be closer to their customers, to comply with the local laws, to focus on a few product types, to produce and market their products more effectively, and to be more responsive to market changes. Production scheduling in multi-factory environments can be classified as a Distributed Scheduling (DS) problem. Indeed, production scheduling problems in single factories have been widely studied by many researchers for many years. However, little attention has so far been paid to DS. DS problems are much more complicated than classical scheduling problems because they involve not only the scheduling problems in each factory, but also the problems in the upper level of how to allocate the jobs to suitable factories. In general, DS problems focus on solving two issues simultaneously: (i) allocation of jobs to suitable factories, and (ii) determination of the corresponding i production schedules in each factory. The objective is to maximize system efficiency by finding an optimal plan for a better collaboration among various processes. In previous studies of DS problems, many researchers have assumed that each job has only one operation and can only be processed on a fixed machine in their models. In fact, each job generally consists of more than one operation. In addition, Flexible Manufacturing Systems (FMS) have recently been implemented in many factories, enabling each operation to be processed on more than one suitable machine. Furthermore, machine maintenance has usually been ignored during production scheduling, even though in reality, every machine requires maintenance and the maintenance policy applied will directly influence the machine's availability, and consequently the production scheduling. In the light of these problems, an innovative approach, named Genetic Algorithm with Dominant Genes (GADG) is proposed to deal with DS problems in multi-factory environments in which FMS production is implemented. This approach can simultaneously determine the scheduling of maintenance during DS. In addition, the proposed GADG reduces the difficulties of controlling the genetic parameters during the implementation of GA. Meanwhile, it improves the performance of genetic search and the quality of the solutions obtained. In this thesis, a number of problems are discussed and solved. The proposed GADG is compared with other proposed approaches and found to be more reliable and robust. Second, the makespan obtained from the simultaneous maintenance scheduling during DS is compared with that from the separate scheduling, and the simultaneous scheduling approach is shown to be better. Lastly, it is demonstrated that the shape of the maintenance curve will not influence the performance of the simultaneous scheduling approach. ii DOI: 10.5353/th_b3782677 Subjects: Genetic algorithms F
This thesis describes the production planning problem of a printed circuit board assembly plant. The problem is a generalization of the so-called flexible flow line scheduling problem. The process engineer recognizes it as a dynamic sequence of updates in the production plan.