A Genetic Algorithm-based Scheduling System for Dynamic Job Shop Scheduling Problems PDF Download

Author: Shyh-Chang Lin
Publisher:
ISBN:
Category : Production control
Languages : en
Pages : 300

View

Book Description

Author: Mitsuo Gen
Publisher: Springer Science & Business Media
ISBN: 3540959777
Category : Computers
Languages : en
Pages : 218

View

Book Description
This book offers fourteen select papers presented at the recent Asia-Pacific Symposia on Intelligent and Evolutionary Systems. They illustrate the breadth of research in the field with applications ranging from business to medicine to network optimization.

Author: Duc T. Pham
Publisher: Springer Science & Business Media
ISBN: 1447114981
Category : Technology & Engineering
Languages : en
Pages : 212

View

Book Description
Although the tenn quality does not have a precise and universally accepted definition, its meaning is generally well understood: quality is what makes the difference between success and failure in a competitive world. Given the importance of quality, there is a need for effective quality systems to ensure that the highest quality is achieved within given constraints on human, material or financial resources. This book discusses Intelligent Quality Systems, that is quality systems employing techniques from the field of Artificial Intelligence (AI). The book focuses on two popular AI techniques, expert or knowledge-based systems and neural networks. Expert systems encapsulate human expertise for solving difficult problems. Neural networks have the ability to learn problem solving from examples. The aim of the book is to illustrate applications of these techniques to the design and operation of effective quality systems. The book comprises 8 chapters. Chapter 1 provides an introduction to quality control and a general discussion of possible AI-based quality systems. Chapter 2 gives technical information on the key AI techniques of expert systems and neural networks. The use of these techniques, singly and in a combined hybrid fonn, to realise intelligent Statistical Process Control (SPC) systems for quality improvement is the subject of Chapters 3-5. Chapter 6 covers experimental design and the Taguchi method which is an effective technique for designing quality into a product or process. The application of expert systems and neural networks to facilitate experimental design is described in this chapter.

Author: Hsiao-Lan Fang
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 9

View

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."

Author: Rajkumar Roy
Publisher: Springer Science & Business Media
ISBN: 1447101235
Category : Technology & Engineering
Languages : en
Pages : 862

View

Book Description
Soft computing embraces various methodologies for the development of intelligent systems that have been successfully applied to a large number of real-world problems. Soft Computing in Industry contains a collection of papers that were presented at the 6th On-line World Conference on Soft Computing in Industrial Applications that was held in September 2001. It provides a comprehensive overview of recent theoretical developments in soft computing as well as of successful industrial applications. It is divided into seven parts covering material on: keynote papers on various subjects ranging from computing with autopoietic systems to the effects of the Internet on education; intelligent control; classification, clustering and optimization; image and signal processing; agents, multimedia and Internet; theoretical advances; prediction, design and diagnosis. The book is aimed at researchers and professional engineers who develop and apply intelligent systems in computer engineering.

Author: Tapan P. Bagchi
Publisher: Springer Science & Business Media
ISBN: 1461552370
Category : Business & Economics
Languages : en
Pages : 369

View

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.

Author: Uday K. Chakraborty
Publisher: Springer
ISBN: 3642028365
Category : Technology & Engineering
Languages : en
Pages : 348

View

Book Description
For over fifty years now, the famous problem of flow shop and job shop scheduling has been receiving the attention of researchers in operations research, engineering, and computer science. Over the past several years, there has been a spurt of interest in computational intelligence heuristics and metaheuristics for solving this problem. This book seeks to present a study of the state of the art in this field and also directions for future research.

Author: F. Pezzella
Publisher:
ISBN:
Category :
Languages : en
Pages : 11

View

Book Description

Author: Haruhiko Suwa
Publisher: Springer Science & Business Media
ISBN: 1447145615
Category : Technology & Engineering
Languages : en
Pages : 157

View

Book Description
Online scheduling is recognized as the crucial decision-making process of production control at a phase of “being in production" according to the released shop floor schedule. Online scheduling can be also considered as one of key enablers to realize prompt capable-to-promise as well as available-to-promise to customers along with reducing production lead times under recent globalized competitive markets. Online Scheduling in Manufacturing introduces new approaches to online scheduling based on a concept of cumulative delay. The cumulative delay is regarded as consolidated information of uncertainties under a dynamic environment in manufacturing and can be collected constantly without much effort at any points in time during a schedule execution. In this approach, the cumulative delay of the schedule has the important role of a criterion for making a decision whether or not a schedule revision is carried out. The cumulative delay approach to trigger schedule revisions has the following capabilities for the practical decision-making: 1. To reduce frequent schedule revisions which do not necessarily improve a current situation with much expense for its operation; 2. To avoid overreacting to disturbances dependent on strongly an individual shop floor circumstance; and 3. To simplify the monitoring process of a schedule status. Online Scheduling in Manufacturing will be of interest to both practitioners and researchers who work in planning and scheduling in manufacturing. Readers will find the importance of when-to-revise policies during a schedule execution and their influences on scheduling results.

Author: Amirabbas Tabatabaei
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

View

Book Description
Scheduling of machines has been interested in flexible manufacturing systems due to the flexibility and simultaneous task process capability of such production systems. Advanced material handling systems such as automated guided vehicles are being used in flexible manufacturing systems. Automated guided vehicles are flexible and easy to utilize in flexible manufacturing systems. Therefore, dynamic simultaneous scheduling and sequencing of flexible manufacturing systems and automated guided vehicles, are considered as the main focus of this study. Scheduling of machines and automated guided vehicles, if considered separately, are NP-Hard problems. Similarly, simultaneous scheduling of machines and automated vehicles are NP-Hard. Although there are some studies on static scheduling of machines and vehicles, dynamic scheduling has not been studied thoroughly in the literature. Population based search algorithms have been used to solve complex problems and in order to find solution sets. In this study, a genetic algorithm is designed to propose solutions for simultaneous scheduling and sequencing of machines and automated guided vehicles in flexible manufacturing systems environments in dynamic situation. In order to assist designers of manufacturing systems, two design variables were added to mathematical model presented in this study. These two design variables were battery charge capacity and number of active automated guided vehicles in the system. A specific time frame was assumed for each job set to schedule machines and automated guided vehicles dynamically. The genetic algorithm was initially validated by bench mark problems from previous studies. Ten job sets previously scheduled by other researchers using heuristic ii and meta heuristic approaches were used. Precedence constrain connects tasks by a network. There are 4 layouts which define machine locations and automated guided vehicle paths. Through solving the static scheduling problem of previous studies, the genetic algorithm was validated. Thereafter, a set of dynamic scheduling problem was developed from previous bench mark problems and solved using the modified model proposed and validated in this study. Dynamic scheduling problem results show that the genetic algorithm is not limited by the number of components to schedule in the mathematical model. In addition, it was found that as the number of active automated guided vehicles increase in the system, the total completion time decreases. This fact is due to the availability of automated guided vehicle to travel demand. Although having more vehicles in the system may increase the costs of production, one could argue the advantages and disadvantages. According to the results, the total completion time for the first static and dynamic scheduling problems (Job set 1, Layout 1) for one to four vehicles are 161 to 76 and 481 to 186 respectively. Another observation was finding best vehicle type based on the battery charge capacity. Automated guided vehicles maintenance and costs are related to the type and their ability to run with a single charge. Therefore, it is important to choose vehicles which fulfill the needs and requirements of the system. The battery charge capacities were from 25 to 100 which affects the total completion times for static and dynamic scheduling problems relatively. The total completion times for static scheduling problems are 100 to 96 for battery capacities 25 to 100. Likewise dynamic scheduling problem completion times are 291.5 to 272.5.