Special Issue on Production Planning and Scheduling

• Published in: International journal of automation technology Vol. 9; no. 3; p. 209
• Main Authors: Sugimura, Nobuhiro, Iwamura, Koji, Maeda, Tomohiko
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 05.05.2015
• Subjects: Algorithms; Automotive parts; CAD; CAD/CAM; Computer aided design; Data management; Data processing; Digital computers; Enterprise resource planning; Manufacturing; Multiagent systems; Optimization; Process planning; Product data management; Product design; Production management; Production methods; Production planning; Production scheduling; Scheduling; Steel production; Theory of constraints; Wireless sensor networks
• ISSN: 1881-7629; 1883-8022
• DOI: 10.20965/ijat.2015.p0209

This issue focuses on production planning and scheduling for production system and the related problems that have arisen in these areas in the last half century as digital computer systems developed. These problems relate to production management, production planning, shop floor control, product design and process planning. In the first stage of production planning and scheduling systems R&D, optimization is a key issue that has been widely discussed and many theories and optimization algorithms proposed. Rule-based methods are discussed as potential solutions to these problems. With rapid advances in computer and information processing technologies and performance, tremendous progress has been made in the areas of production systems such as production planning, production scheduling, advances production systems (APS), enterprise resource planning (ERP), just-in time (JIT) processes, the theory of constraint (TOC), product data management (PDM) and computer-aided design / manufacturing / engineering (CAD/CAM/CAE). This special issue addresses the latest research advances, applications, and case studies in production planning and scheduling covering such as decentralized and autonomous production systems, distributed simulation models, robust capacity planning models, wireless sensor networks for production systems and applications to automotive component and steel production. We hope that learning about these advances will enable readers to share their own experience and knowledge in technology, new developments and the potential applications of production planning and scheduling methods and solutions.