Full metadata record
|dc.contributor||Department of Industrial and Systems Engineering||en_US|
|dc.publisher||Hong Kong Polytechnic University||-|
|dc.rights||All rights reserved||en_US|
|dc.title||A real time intelligent resource management system for facilitating inbound operations in manufacturing||en_US|
|dcterms.abstract||In make-to-order (MTO) manufacturing environments, products are customized and production processes are only started upon receiving a customer's order. To satisfy customer requirements and punctually meet the delivery time, it is necessary to handle several customers' orders simultaneously and allocate the appropriate machines and resources before starting production. Production scheduling and planning is an important process for avoiding delay in production and improving manufacturing performance to fulfill customers' needs. Different constraints are considered in formulating the most satisfactory production plan. These constraints are constant and predictable. However, in the actual manufacturing environment, shop floor managers face numerous unpredictable risks in day-to-day operations, such as defects in the supplied components or raw materials, errors, failures, and wastage in various production processes. The unpredictable risks not only entail stringent requirements regarding the replenishment of materials but also increase in the difficulty in preparing material stock. Therefore, it is essential to effectively and efficiently handle such risks to achieve smooth production. Researchers are considering machines and material handling equipment as constraints when addressing production material demand issues in production scheduling. Their studies consider "off-line scheduling problems, in which a schedule is generated within a time period and is not expected to involve any changes. However, these studies are not capable of solving stochastic production material demand problems because existing scheduling approaches solely focus on the allocation of production resources, such as machines and workers. These scheduling approaches consider warehouse resources in the form of forklifts, but neglect manpower. Warehouse resources are important in minimizing risks. They are utilized to pick, transfer, and store production materials between the warehouse and production lines when problems occur during the production process. The existing approaches can be seen as processes of allocating equipment to perform specific production tasks before production starts. Such research does not take into consideration real-time equipment, which is used to facilitate production. The consideration of real-time equipment helps improve the visibility of warehouse operations and enhances productivity. Nevertheless, previous research did not consider the allocation of warehouse resources to facilitate production processes. The objective of this research is to effectively and efficiently allocate warehouse resources for replenishing appropriate production materials between these two facilities to assure that the production process can run smoothly. To efficiently and effectively solve stochastic production material demand problems, a real-time production operations decision support system (R-PODSS) is developed. The proposed system consists of three modules: Real-time Data Collection, Data Storage and Exchange, and Formulation Module of Optimal Pickup and Delivery Route. Real-time Data Collection Module utilizes Radio Frequency Identification (RFID) technology in capturing production operations information. Different RFID reading performance tests are first performed to evaluate the reading performance of all RFID equipment and to verify the most suitable location for the installation of the hardware. A reliable RFID technology implementation plan is formulated to capture real-time production and warehouse information simultaneously. Data Storage and Exchange Module systematically stores captured production and warehouse information in the centralized database and transforms them into meaningful information. Database Management System (DBMS), Query Optimization, and Structured Query Language (SQL) statement are adopted to provide data retrieval and storage to users. The Formulation Module of Optimal Pickup and Delivery Route provides an optimal resource allocation plan for utilizing appropriate resources to pick/transfer/store production materials from the warehouse to the production lines. Artificial Intelligent techniques, such as Case-Based Reasoning (CBR) and Genetic Algorithm (GA) are adopted to select appropriate warehouse resources and formulate the shortest pickup and delivery routes, respectively. To validate the feasibility of the proposed system, two case studies are conducted. Through the pilot run of the system in the case studies, the improved visibility of production and warehouse operations is observed. The efficiency of production and warehouse operations is also significantly enhanced. The results reveal that the proposed system effectively achieves the objectives of this research. The major contribution of this research is the design and development of an effective system, which allows real-time tracking and tracing of production and warehouse resources and corresponding operations, to reduce the effect of stochastic production demand problems and enhance productivity on the shop floor and in the warehouse. The deliverables of this research provide the development of R-PODSS. They also pave the way to future research opportunities for incorporating warehouse resource management in production operation management and implementing emerging RFID technology and artificial intelligent techniques in logistics industry.||en_US|
|dcterms.extent||xix, 164,  leaves : ill. ; 30 cm.||en_US|
|dcterms.isPartOf||PolyU Electronic Theses||en_US|
|dcterms.LCSH||Radio frequency identification systems||en_US|
|dcterms.LCSH||Hong Kong Polytechnic University -- Dissertations||en_US|
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