Abstract:Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simul… Show more
“…Artificial neural network is used to capture the dynamics from the system dynamic models and analyzes simulation results in order to predict changes before it takes place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations [14].…”
This study integrates artificial immune system and artificial neural network into a real estate evaluation model.Artificial immune system has the abilities of self-organizing, memory, recognition, adaptive, and ability of learning. It can be applied to nonlinear system identification and provided various feasible system models with robust and adaptive characteristics.Artificial neural network doesn't need any complicated mathematics application and its self-learning, self-adaptive capacity, parallel processing capability and strong fault tolerance can obtain more accurate non-linear outputs from various impact factors. The result of this study indicates that the integration of artificial immune system and artificial neural network can achieve promptly accurate and satisfactory results in the real estate evaluation.
“…Artificial neural network is used to capture the dynamics from the system dynamic models and analyzes simulation results in order to predict changes before it takes place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations [14].…”
This study integrates artificial immune system and artificial neural network into a real estate evaluation model.Artificial immune system has the abilities of self-organizing, memory, recognition, adaptive, and ability of learning. It can be applied to nonlinear system identification and provided various feasible system models with robust and adaptive characteristics.Artificial neural network doesn't need any complicated mathematics application and its self-learning, self-adaptive capacity, parallel processing capability and strong fault tolerance can obtain more accurate non-linear outputs from various impact factors. The result of this study indicates that the integration of artificial immune system and artificial neural network can achieve promptly accurate and satisfactory results in the real estate evaluation.
“…Global competition, shorter product life cycles, dynamic changes of demand patterns and product varieties and environmental standards cause remarkable changes in the market scenario thereby forcing the manufacturing enterprises to deliver their best in order to strive [7]. Decrease in lead times and expenses, enrichment of customer service levels and advanced product quality are the characteristics that determine the competitiveness of a company in the contemporary market place [4].…”
Efficient and effective management of inventory throughout the supply chain significantly improves the ultimate service provided to the customer. Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain. The dynamic nature of the excess stock level and shortage level over all the periods is a serious issue when implementation is considered. In addition, consideration of multiple factories manufacturing the same product leads to very complex inventory management process The complexity of the problem increases when lead times of stocks are involved. In this paper, these issues of inventory management have been focused and a novel approach based on Genetic Algorithm has been proposed in which the most probable excess stock level and shortage level required for inventory optimization in the supply chain is distinctively determined so as to achieve minimum total supply chain cost.
“…Due to the global competition, the vastly changing market, shorter product life cycles, vibrant changes of demand patterns and product varieties and environmental standards has produced demands for manufacturing enterprises to skillfully survive [1] all through the last decade. The very important decision which companies meet on a usual basis is the classification of products and/or raw materials.…”
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