Designing And Managing The Supply Chain
Designing And Managing The Supply Chain
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Computers and Industrial Engineering
Analysis of supply chains using system dynamics, neural nets, and eigenvalues
WSC '04 Proceedings of the 36th conference on Winter simulation
Evaluating the performance of supply chain simulations with tradeoffs between mulitple objectives
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation-based optimization for material dispatching in a retailer network
WSC '04 Proceedings of the 36th conference on Winter simulation
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
The implementation of neural network for semiconductor PECVD process
Expert Systems with Applications: An International Journal
Backpropagation neural nets with one and two hidden layers
IEEE Transactions on Neural Networks
Supply chain product visibility: Methods, systems and impacts
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In recent supply chain management, as the online use of inventory data becomes available with the development of Radio Frequency Identification (RFID) technology, it is now possible to monitor the performance measures in a timely fashion. Customer service level is a key performance measure that can be computed as the percentage of times that customer orders electronically received are fulfilled by on-hand inventory. Online monitoring of the service level enables the management paradigm to progress toward the closed loop based control which keeps revising the operation policy to reach a target service level. This paper proposes a closed loop supply chain control based on a direct neural network controller. Unlike the simulation based optimizations which usually need a demand forecasting and an early warning model, our proposed approach has the strength that it can maintain the target only by using the actual ones measured online. For the direct neural network controller, an amplification function which increases the learning speed by augmenting the learning error is proposed. Simulation based experiments were performed to test the performance of the controller against two kinds of unstable customer demand curves.