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This book is aimed at both researchers and practitioners, and provides a collection of expert systems in manufacturing and production engineering along with their knowledge base and rules. We believe that inclusion of the knowledge base and associated rules is essential if practitioners are to derive full benefit from these expert systems. This unique book is the result of our belief and the efforts of our distinguished colleagues who subscribe to this philosophy. A total of 15 different expert systems are included in this book. These expert systems are preceded by an introductory chapter written by Kuo, Preface XVll Mital and Anand. The expert system rules are included on a floppy disk in ASCII and can be easily accessed. These rules and the description of the expert system's structure should assist the users in customizing these systems. Overall, the expert systems included in this volume cover a fairly wide variety of manufacturing and production engineering topics.
Expert systems (ES) have produced much excitement in recent years. Because a great deal of experience and knowledge are captured in the ES, there may be no need to consult a specialist for every decision in the given area. A properly designed expert system thus has in its knowledge base a wide collection of facts, relationships, and rules specific to that domain.Expert systems have been published in a number of logistics-related areas. Application domains include inventory management, vehicle routing, warehousing, combined production/distribution systems, and the scheduling of machines and flexible manufacturing cells. General surveys of ES methods are available for decisions in industrial engineering, logistics, and intelligent manufacturing.In this article, we develop an expert system for the selection of material handling equipment. The paper begins by outlining the steps in choosing such equipment for a warehouse or factory. We next discuss their knowledge embedded in our ES in various databases and groups of rules.The article then presents a complete run of the expert system. The user responds to a sequence of questions, resulting in a short-list of feasible equipment. This is ranked according to “distance,” the closeness of each to (nonexistent) ideal equipment for the particular application. The distances are derived from multi-attribute decision making (MADM). Brief appendices deal with MADM and the database concepts used in our ES. Throughout, the presentation is aimed at logisticians who may not be specialist in the technical issues involved.
Material handling is one of the production functions that have been the focus of attention for many manufacturing industries especially discrete parts manufacturing. Generally, the design of material handling system can be divided into four phases namely; conceptual design, detailed design, launching and fully operational. The focus of this research is on the conceptual design phase. Since there are hundreds of equipment types and procedures to choose from, the selection activities are usually complex and difficult. Therefore, quick and appropriate material handling selection is important in a production system, since it can increase productivity, flexibility of the manufacturing time and it also reduces cost. Hence, development of a decision support system to aid in the selection of the material handling equipment is a significant contribution in manufacturing operations. In this research work, the selection system was developed using Kappa-PC expert system shell. It uses forward chaining for inferencing. In the forward reasoning stage, the developed system attempts to deduce a material handling equipment type recommendation based on user specified requirements. The selections of the material handling equipment are based on the move attributes and characteristics of material to be handled, operation requirement and area restriction. The recommendation from the knowledge-based system is refined through application of simulation modeling techniques. The simulation was carried out using Arena 9.0 software package. The material handling selection system assist design engineers in selecting the most suitable and appropriate material handling equipment type for the task being considered. The advantages of the developed system are; it considers both selection and configuration analysis, it can be immediately implemented in factory operations; it is modular and user friendly. The system was successfully validated through a case study. The case study was carried out on Sharp Manufacturing Corporation Sdn. Bhd., in Malaysia. Through the case study, a total of 5.20s of the transfer time and RM 9.00 per cycle of the transportation cost of the current Sharp Manufacturing System will be reduced. This result in 72.22 and 75.00 percent of improvement on the transfer time and transportation cost respectively. By the reduction and improvement on the transfer time and transportation cost, the efficiency of the material flow and productivity of the Sharp Manufacturing system will increase and the production cost will reduce.