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This book is dedicated to the latest findings on the design and optimization of production lines. The “Fourth Industrial Revolution” (alternatively known as “Industry 4.0”) supports innovative models for energy consumption and fault tolerance in automated lines, and this drives changes in the design and optimization models of production lines. The goal is to collect a series of works that can summarize the latest trends in the field of production line optimization models in order to improve the responsiveness of automated lines to failures, reduce energy consumption and peak electricity demand, and develop other methods to support robust and sustainable production lines.
This book describes a vision of manufacturing in the twenty-first century that maximizes efficiencies and improvements by exploiting the full power of information and provides a research agenda for information technology and manufacturing that is necessary for success in achieving such a vision. Research on information technology to support product and process design, shop-floor operations, and flexible manufacturing is described. Roles for virtual manufacturing and the information infrastructure are also addressed. A final chapter is devoted to nontechnical research issues.
Much academic energy has been invested in the study of optimizing assembly or production lines. The Assembly Line Balancing Problem design problem is an artifact of that work. Theory of Constraints purports that an assembly line that is purposely and strategically unbalanced provides superior performance in terms of predictability and throughput over the traditional balanced line. This study articulates a custom production line model based on Theory of Constraints and compares its performance to the traditional operations management paradigm, a balanced line. Results show that a purposely unbalanced line provides superior flow of material and greater throughput than the traditional balanced line configuration. Additionally the simplified model and approach may be more appealing with respect to the design, development, and computational costs than those required of the conventional line balancing methodologies.
Assembly line is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. Any assembly line comprises a finite set of work elements called tasks. Each task is characterized by an operation processing time and a set of precedence relationships, which specify the allowable orderings of the tasks. To develop a good assembly system, good observation is required. However, observing real manufacturing system is very expensive and sometimes cumbersome. The rapid rate at which the whole process takes place, the interaction between workers, and the different transition times between workers make it increasingly more difficult for a human being to make correct decisions regarding how fast each operator should work in order to continue the process while at the same time keeping productivity high and throughput at an acceptable level. However with a simulation models, we can explore how an existing system might perform if altered, or how a new system might behave before the prototype is even completed. Thus modeling and simulation technique help managers in decision making by saving on costs and lead times
This book provides a complete overview of production systems and describes the best approaches to analyze their performance. Written by experts in the field, this work also presents numerous techniques that can be used to describe, model, and optimize the performance of various types of production lines. The book is intended for researchers, production managers, and graduate students in industrial, mechanical, and systems engineering.
Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essentials of the Monte Carlo discrete-event simulation methodology, and does so in the context of a popular Arena simulation environment. It treats simulation modeling as an in-vitro laboratory that facilitates the understanding of complex systems and experimentation with what-if scenarios in order to estimate their performance metrics. The book contains chapters on the simulation modeling methodology and the underpinnings of discrete-event systems, as well as the relevant underlying probability, statistics, stochastic processes, input analysis, model validation and output analysis. All simulation-related concepts are illustrated in numerous Arena examples, encompassing production lines, manufacturing and inventory systems, transportation systems, and computer information systems in networked settings. Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling
We present a model and analysis of a production line that processes different part types on unreliable machines which operate according to a priority rule. The production line consists of machines separated by storage areas in which parts flow in a fixed sequence. A machine operates on the highest priority part whenever possible, and only operates on lower priority parts when unable to produce the higher priority parts. Part priorities are static and are a function only of part type. The purpose of this thesis is to present mathematical formulations and algorithms for estimating production rates and average inventory levels for each part type in a flexible production line. The qualitative behavior of the multiple-part-type line under different supply and demand scenarios is described.
Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.
"This is an excellent and well-written text on discrete event simulation with a focus on applications in Operations Research. There is substantial attention to programming, output analysis, pseudo-random number generation and modelling and these sections are quite thorough. Methods are provided for generating pseudo-random numbers (including combining such streams) and for generating random numbers from most standard statistical distributions." --ISI Short Book Reviews, 22:2, August 2002
Advanced modeling techniques are a necessary tool in order to design and manage manufacturing systems effectively. This book contains a set of tutorial chapters on topics ranging from aggregate production planning to real time control, including predictive and reactive scheduling, flow management in assembly systems, simulation of robotic cells, design of manufacturing systems under uncertainty and a historical perspective on production management philosophies. The book will be of interest both to researchers and practitioners, including graduate students in Manufacturing Engineering and Operations Research.