Agent-based material transportation scheduling of AGV systems and its manufacturing applications

制度:新 ; 報告番号:甲3743号 ; 学位の種類:博士(工学) ; 授与年月日:2012/9/10 ; 早大学位記番号:新6114Waseda Universit

Automated control of hierarchical systems using value-driven methods

An introduction is given to the Value-driven methodology, which has been successfully applied to solve a variety of difficult decision, control, and optimization problems. Many real-world decision processes (e.g., those encountered in scheduling, allocation, and command and control) involve a hierarchy of complex planning considerations. For such problems it is virtually impossible to define a fixed set of rules that will operate satisfactorily over the full range of probable contingencies. Decision Science Applications' value-driven methodology offers a systematic way of automating the intuitive, common-sense approach used by human planners. The inherent responsiveness of value-driven systems to user-controlled priorities makes them particularly suitable for semi-automated applications in which the user must remain in command of the systems operation. Three examples of the practical application of the approach in the automation of hierarchical decision processes are discussed: the TAC Brawler air-to-air combat simulation is a four-level computerized hierarchy; the autonomous underwater vehicle mission planning system is a three-level control system; and the Space Station Freedom electrical power control and scheduling system is designed as a two-level hierarchy. The methodology is compared with rule-based systems and with other more widely-known optimization techniques

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles

Internationalization of markets and climate change introduce multifaceted challenges for modern supply chain (SC) management in the today's digitalisation era. On the other hand, Automated Guided Vehicle (AGV) systems have reached an age of maturity that allows for their utilization towards tackling dynamic market conditions and aligning SC management focus with sustainability considerations. However, extant research only myopically tackles the sustainability potential of AGVs, focusing more on addressing network optimization problems and less on developing integrated and systematic methodological approaches for promoting economic, environmental and social sustainability. To that end, the present study provides a critical taxonomy of key decisions for facilitating the adoption of AGV systems into SC design and planning, as these are mapped on the relevant strategic, tactical and operational levels of the natural hierarchy. We then propose the Sustainable Supply Chain Cube (S2C2), a conceptual tool that integrates sustainable SC management with the proposed hierarchical decision-making framework for AGVs. Market opportunities and the potential of integrating AGVs into a SC context with the use of the S2C2 tool are further discussed

Platooning-based control techniques in transportation and logistic

This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results. Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency. In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes. Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested