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About this book

This book provides readers with extensive information on path planning optimization for both single and multiple Autonomous Guided Vehicles (AGVs), and discusses practical issues involved in advanced industrial applications of AGVs. After discussing previously published research in the field and highlighting the current gaps, it introduces new models developed by the authors with the goal of reducing costs and increasing productivity and effectiveness in the manufacturing industry. The new models address the increasing complexity of manufacturing networks, due for example to the adoption of flexible manufacturing systems that involve automated material handling systems, robots, numerically controlled machine tools, and automated inspection stations, while also considering the uncertainty and stochastic nature of automated equipment such as AGVs. The book discusses and provides solutions to important issues concerning the use of AGVs in the manufacturing industry, including material flow optimization with AGVs, programming manufacturing systems equipped with AGVs, reliability models, the reliability of AGVs, routing under uncertainty, and risks involved in AGV-based transportation. The clear style and straightforward descriptions of problems and their solutions make the book an excellent resource for graduate students. Moreover, thanks to its practice-oriented approach, the novelty of the findings and the contemporary topic it reports on, the book offers new stimulus for researchers and practitioners in the broad field of production engineering.

Table of Contents

Frontmatter

Models for AGVs’ Scheduling and Routing

Abstract
An automated guided vehicle (AGV) is a driverless material handling system used for horizontal movement of materials. AGVs were introduced in 1955 (Muller, 1983). The use of AGVs has grown enormously since their introduction. The number of areas of application and variation in types has increased significantly. AGVs can be used in inside and outside environments, such as manufacturing, distribution, transshipment and (external) transportation areas. At manufacturing areas, AGVs are used to transport all types of materials related to the manufacturing process. According to Gotting (2000) over 20,000 AGVs were used in industrial applications. The author states that the usage of AGVs will pay off for environments with repeating transportation patterns. Examples of these environments are distribution, transshipment and transportation systems. Warehouses and cross docking centers are examples of distribution areas. AGVs are used in these areas for the internal transport of, for example, pallets between the various departments, such as receiving, storage, sorting and shipment areas. At transshipment systems, such as container terminals, AGVs take care of the transport of products between the various modes of transport. Gotting (2000) presented an overview of available technology for automation in container terminals. Furthermore, navigation and vehicle guidance systems applicable in various indoor/outdoor environments are described. Haefner and Bieschke (1998) stated that AGV systems can provide benefits to both the port and its customers by executing transportation requests between vessels and inland transportation.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Analytical Material Flow Model for AGV System

Abstract
Manufacturing automation has become increasingly important as the need to remain productive increases. In manufacturing of a product, many processes may be needed. For example, semiconductor manufacturing may include over 400 processing steps involving more than 100 different tools. Furthermore, the process route can include a high level of reentrance in which the same tool or tool types are used. An important aspect in manufacturing automation is material handling. To facilitate material handling, automated transport systems are employed.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Nonlinear Stochastic Model for AGV System

Abstract
Advanced automated manufacturing systems are widely used in industrial companies where productivity objectives have to be met. These systems often being costly, they must be designed to be as efficient as possible. Here, we focus on automated manufacturing systems in a job shop layout considering automated guided vehicle as a material handling resource. The key issue in manufacturing operations is how to produce high quality products at low costs in such a way that the diversified demand are met. Hence, modern manufacturing companies should become as responsive as possible in order to satisfy customer demands.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Reliability Model for AGV

Abstract
The Material Handling System (MHS) in a manufacturing setting plays an important role in the performance of the entire system. Inadequately designed MHSs can interfere with the overall performance of the manufacturing system and lead to substantial losses in productivity and competitiveness, and to unacceptably long lead times. Among the advanced technologies available for MHSs, Automated Guided Vehicles (AGVs) have found increasing applications because of their capability to transport a variety of part types from point to point without human intervention.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Uncertain Optimal Path for AGV System

Abstract
Discrete event systems are characterized by changes in state over time, based on current state and state transition rules, where each state is separated from its neighbor by a step rather than a continuum of intermediate infinitesimal states. Examples of such systems are information systems, operating systems, networking protocols, banking systems, business processes and telecommunications systems, and flexible manufacturing systems. Traditional manufacturing has relied on dedicated mass-production systems to achieve high production volumes at low costs. As living standards improve and the demands for new consumer goods rise, manufacturing flexibility gains prominence as a strategic tool for rapidly changing markets. Flexibility, however, cannot be properly incorporated in the decision-making process if it is not well defined and measured in a quantitative manner. Today, manufacturing flexibility remains an elusive notion because of its inherent complexity and generality, in spite of a large body of published research work. There exist more than 50 definitions of (Sethi and Sethi, 1990) and six different approaches for obtaining a quantitative flexibility measure (Gupta and Goyal, 1989). Flexibility in its most rudimentary essence is the ability of a manufacturing system to respond to changes and uncertainties associated with the production process (Zelenovic, 1982; Buzacott, 1982; Gerwin, 1982). A comprehensive classification of eight flexibility types was proposed in Browne et al. (1984). Resource and system flexibilities were examined in Slack (1987), whereas global measures for flexible manufacturing systems (FMSs) were defined in Gupta and Buzacott (1989). Routing flexibility based on information theoretic concepts was examined in Yao and Pei (1990) and Kumar (1987). Flexibility measures for one machine, a group of machines, and whole industry were presented in Brill and Mandelbaum (1989), involving appropriate weights and machine efficiencies in carrying out sets of tasks.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Cross Entropy Model for AGV Routing Time

Abstract
Today, in order to survive in the rapid challenging environment of the modern manufacturing era, manufacturers are forced to adopt new technologies, especially for products that are made in small batch production. Automated Guided Vehicles (AGV) has been applied for the automated manufacturing system. In industrial application, manufacturing factory is brought the mobile vehicle to incorporate working with other machine in order to being the automated manufacturing system (Arai et al., 2002).Advanced automated manufacturing systems are widely used in industrial companies where productivity objectives have to be met. These systems often being costly, they must be designed to be as efficient as possible. Here, an automated manufacturing system in a job shop layout considering AGV as a material handling resource is focused. The key issue in manufacturing operations is how to produce high quality products at low costs in such a way that the diversified demand is met. Hence, modern manufacturing companies should become as responsive as possible in order to satisfy customer demands. Material handling accounts for 30–75% of the total cost of a product, and efficient material handling can result in reducing the manufacturing system operations cost by 15–30% (Sule, 1994). These points underscore the importance of material handling costs reduction as a key element in improving the cost structure of a product. The determination of a material handling system involves both the selection of suitable material handling equipment and the assignment of material handling operations to each individual piece of equipment. Hence, material handling system selection can be defined as the selection of material handling equipment to perform material handling operations within a working area considering all aspects of the products to be handled.The material handling system plays a crucial role in automated manufacturing systems. When inadequately designed, the material handling system indeed can adversely affect the overall performance of the system and lead to substantial losses in productivity and competitiveness, and to unacceptably long lead times. Thus, to avoid such pitfalls, material handling system design must be integrated into the overall design of the manufacturing system centering on the selection of machines and the allocation of operations to the machines (Butdee and Suebsomran, 2006; Shirazi et al., 2010).Automated guided vehicle is an intelligent machine that has ‘intelligence’ to determine its motion status according to the environmental conditions systems. AGVs are advanced material handling devices extensively used in automated manufacturing systems (AMS) to transport materials among workstations (Vis, 2006).
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Neuro-Fuzzy-Regression Expert System for AGV Optimal Path

Abstract
According to Turban and Aronson (1998), a decision support system (DSS) is a computer-based information system combining models and data in an attempt to solve non-structured problems with extensive user involvement. They advocated that an expert system (ES) was a computer system that applied reasoning methodologies on knowledge to render advice or recommendations much like a human expert. When expert system technology was first applied to decision-making problems, it fell short in several respects. Early expert systems were rule-based and thus were not capable of handling the classical DSS functions being more computational than logical. Recently, artificial intelligence researchers have noted the necessity for using statistical techniques to build intelligent decision support systems (Nolan, 1998; Weiss and Kulikowski, 1991). Examples of such statistical techniques include fuzzy logic, neural networks, rule induction and various Bayesian techniques. Turban and Aronson (1998) believe that although uncertainty is widespread in the real world, but practical treatment in artificial intelligence is very limited.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Optimal Path for AGV System with Intelligent Agents

Abstract
An AGV is a material handling equipment that travels on a network of guide paths. The guide path is composed of aisle segments on which the vehicles are assumed to travel at a constant speed. The vehicles can travel forward or backward. As many vehicles travel on the guide path simultaneously, collisions must be avoided. AGV systems are implemented in various industrial contexts: container terminals, part transportation in heavy industry, flexible manufacturing systems.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Earliness/Tardiness for a Multiple AGV System

Abstract
Flexible manufacturing systems (FMSs), container terminals, warehousing systems, and service industries including hospital transportations are employing automated guided vehicle systems (AGVs) for the material handling to maintain flexibility and efficiency of production and distribution. For the efficient operation, it is requested to realize the synchronized operations for the simultaneous scheduling of production systems and transportation systems. The main issue treated in this chapter is the simultaneous optimization problems for penalized earliness and tardiness for the AGVs in the manufacturing system. The production scheduling problems asks an optimal production sequence and starting time of operations for jobs at machines for multi-stages with respect to a specified technical precedence relation. The vehicle management problems are classified into:
(1) dispatching, which is to assign tasks to vehicles;
(2) routing, which is to select specific paths taken by vehicles;
(3) scheduling, which is to determine the arrival and departure times.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Markovian Model for Multiple AGV System

Abstract
Traditional manufacturing has relied on dedicated mass-production systems to achieve high production volumes at low costs. As living standards improve and the demands for new consumer goods rise, manufacturing flexibility gains prominence as a strategic tool for rapidly changing markets. Flexibility, however, cannot be properly incorporated in the decision-making process if it is not well defined and measured in a quantitative manner. Flexibility in its most rudimentary sense is the ability of a manufacturing system to respond to changes and uncertainties associated with the production process (Miettinenet al., 2010; Kumar and Sridharan, 2009; Das et al., 2009). A comprehensive classification of eight flexibility types was proposed in Browne et al. (1984).
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Producer’s Behavior Analysis for AGV System

Abstract
Traditional manufacturing has relied on dedicated mass-production systems to achieve high production volumes at low costs. As living standards improve and the demands for new consumer goods rise, manufacturing flexibility gains prominence as a strategic tool for rapidly changing markets. Flexibility, however, cannot be properly incorporated in the decision-making process if it is not well defined and measured in a quantitative manner. Flexibility in its most rudimentary sense is the ability of a manufacturing system to respond to changes and uncertainties associated with the production process (Miettinen et al., 2010; Kumar and Sridharan, 2009; Das et al., 2009). A comprehensive classification of eight flexibility types was proposed in Browne et al. (1984). Resource and system flexibilities were examined in Slack (1987), whereas global measures for flexible manufacturing systems (FMSs) were defined in Gupta and Buzacott (1989). Routing flexibility based on information theoretic concepts was examined by Yao and Pei (1990) and Kumar (1987). Flexibility measures for one machine, a group of machines, and the whole industry were presented in Brill and Mandelbaum (1989), involving appropriate weights and machine efficiencies in carrying out sets of tasks.
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad

Risk for Multiple AGV System

Abstract
A standard formula for the quantitative definition of risk is, Risk=P(loss)× L(loss) , where risk is the function of the probability (P) of loss and the significance of its consequences (L) (Manuj and Mentzer, 2008). Hetland (2003) and Diekmann et al. (1988), on the other hand, view risk as the implication of an uncertain phenomenon. Waters (2007) explains the difference: risk occurs because there is uncertainty about the future, which means that unexpected events may occur. Knight’s (1921) distinction between certainty, risk and uncertainty is probably the best known and most used typology of uncertainty for risk management. In his definition of risk Knight coined the terms (quantitative) “measurable” uncertainty and (non-quantitative) “un-measurable” uncertainty when there is only partial knowledge of outcomes in the form of beliefs and opinions (Vilko and Hallikas, 2012).
Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad
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