| Author: | Wang, Lixing |
| Title: | An approach for the perishable product logistics based on real-time monitoring with radio frequency identification (RFID) |
| Degree: | Ph.D. |
| Year: | 2013 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Business logistics. Perishable goods. Radio frequency identification systems. |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | xx, 224 p. : ill. ; 30 cm. |
| Language: | English |
| Abstract: | Food and plasma are essential in our lives. Spoilage or contamination of these products can lead to serious consequences. Most of them belong to perishable products. Perishable products are products that only have a short shelf-life. Transportation involving Third-Party Logistics companies (3PL) is a weak point in product management. The damage caused by deterioration in the quality of perishable products during transportation is often responsible for great monetary losses. Consequently, it is necessary to improve the management of such perishable products. However, the efficient monitoring of such products and effective data management are big challenges for researchers. Radio frequency identification (RFID) technology provides a potential efficient way to improve the management of perishable products. RFID is an emerging technology that has been increasingly used in logistics and supply chain management (SCM) in recent years, since it can offer a long read range, large data capacity, and multiple readings at the same time. This research aims to study the transportation management of perishable products based on the real-time monitoring of the entire supply chain of perishable products from manufacture to retail using RFID technology. The study proposes a system called the Monitoring-based Decision Support System (MDSS), which integrates three modules for the major functions, namely a Real-time Monitoring Module (RMM) with enabling RFID for quality evaluation, a Forecasting and Warning Module (FWM) for arrival time prediction and emergency warning, and a Decision Support Module (DSM) for vehicle and emergence management. The value degeneration process is firstly introduced in the MDSS. Several mathematical models are used to describe the different categories of perishing that are applied to the system. Based on mathematical models, data and evaluation results from RMM, environmental factors and product information are transmitted to FWM for forecasting and warning judgments. If anything abnormal occurs the corresponding information is then transmitted to DSM for emergency management. DSM also helps develop the vehicle schedule before transportation. RMM is designed with RFID technology and sensor networks. The module introduces a hybrid algorithm that combines k-Nearest Neighbour algorithm (k-NN) with Artificial Neural Networks (ANN) to evaluate the product quality. FWM applies Fuzzy Case-based Reasoning (CBR) in its forecasting function, and fast Rule-based Reasoning (RBR) in its warning function. In DSM, an Improved Quantum-inspired Evolutionary Algorithm (IQEA) and Genetic Algorithm (GA) are applied to create an optimal schedule for vehicle management before the transportation of perishable products. These two algorithms aim to solve vehicle schedule problems at different scales. In addition to static optimisation, DSM can also help cope with any emergency. Using heuristic approaches, DSM adjusts the vehicle schedule and provides suggestions on how to cope with any emergency. Finally, a particular case is studied to test the performance of the system. The results from the case study show that MDSS has a positive effect on perishable product management, especially during transportation. For further research, the system can be extended to managing perishable products over the entire supply chain, including storage, retail and recall, in addition to delivery. |
| Rights: | All rights reserved |
| Access: | open access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b26392203.pdf | For All Users | 2.18 MB | Adobe PDF | View/Open |
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