EDI and intelligent agents integration to manage food chains

Electronic Data Interchange (EDI) is a type of inter-organizational information system, which permits the automatic and structured communication of data between organizations. Although EDI is used for internal communication, its main application is in facilitating closer collaboration between organizational entities, e.g. suppliers, credit institutions, and transportation carriers. This study illustrates how agent technology can be used to solve real food supply chain inefficiencies and optimise the logistics network. For instance, we explain how agribusiness companies can use agent technology in association with EDI to collect data from retailers, group them into meaningful categories, and then perform different functions. As a result, the distribution chain can be managed more efficiently. Intelligent agents also make available timely data to inventory management resulting in reducing stocks and tied capital. Intelligent agents are adoptive to changes so they are valuable in a dynamic environment where new products or partners have entered into the supply chain. This flexibility gives agent technology a relative advantage which, for pioneer companies, can be a competitive advantage. The study concludes with recommendations and directions for further research

Modeling user navigation

This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that the network learned to predict the next step for a given trajectory. The analysis of hidden layer shows that the network was able to differentiate between two groups of users identified on the basis of their performance for a spatial task. Time series analysis of hidden node activation values and input vectors suggested that certain hidden units become specialised for place and heading, respectively. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction applications are discussed

“Sharing” the Mykonian summer: The case of AirBnB

The aim of this paper is to illustrate an overview of the current AirBnB market in the Greek island of Mykonos and its place within the entire hospitality real estate market of the island. Global economy has changed its form and functions several times during the years. Sharing economy or collaborative consumption is a new way of economic activity, which basically refers to goods and services sharing. The change from owning to sharing, the wide use of online social networks and electronic markets and the expansion of mobile devices and electronic services have all contributed to the augmentation of sharing economy. The impact of sharing economy on real estate began on 2008, when AirBnB was founded in San Francisco. Nowadays, sharing economy in real estate is very popular not only in USA but internationally too and AirBnB has become one of the most famous short-term lease platforms. Mykonos which is one of the most touristic and popular Greek islands enjoys its own economy. Despite the economic crisis, Mykonos has not been affected in terms of real estate values, hotel room values and holiday expenses. In such an economy, AirBnB has found its place all over the island. This paper aims at mapping the AirBnB phenomenon in Mykonos through the use of GIS, proving that sharing economy is not just targeting markets of lower income or budget and highlighting any geographical patterns in the location of AirBnB properties. Moreover, an analysis on the factors that affect the rental value/day provides an additional insight. It is clearly proven that AirBnB covers any additional need for tourist accommodation successfully even in the high demanding market of Mykonos with over 300 sharing residential facilities, which are described as a shared space, a private room or even an entire house. The paper provides interesting results on the location of each sharing property, the site and property attributes, amenities and services and rental rules, which are all investigated in order to present the Mykonian AirBnB

Intelligent Optimisation Agents in Supply Networks

This paper describes a model of intelligent supply network that improves efficiency within the supply chain. We argue that intelligence creates efficiency and results in chain optimisation. In particular, intelligent agents technology is used to optimise performance of a beverage logistics network. Optimisation agents can help solve specific problems of supply network: reduce inventories and lessen bullwhip effect, improve communication, and enable chain coordination without adverse risk sharing. We model the beer supply network to demonstrate that products can acquire intelligence to direct themselves throughout the distribution network. Further, they gain a capability to be purchased and sold while in transit. Overviews of the supporting technologies that make intelligent supply network a reality are fully discussed. In particular, optimisation agents have the characteristics of autonomous action, being proactive, reactive, and able to communicate. We demonstrate that agents enhance the flexibility, information visibility, and efficiency of the supply chain management. Suggestions and recommendations for further research are provided

Characterization of bacteria with potential to suppress Fusarium solani in plate culture

Black root rot caused by Fusarium solani is considered a minor disease of chickpea. But chickpea grown after rice has been observed during field visits to have this problem more extensively and needs confirmation. A program of acquiring microorganisms (from natural sources, such as compost) with ability to suppress Fusarium solani in plate culture was started at ICRISAT in 1997/98. A large number of bacteria assembled in 1998, was characterized in this study

Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Building

This research is concerned with the novel application and investigation of `Soft Actor Critic' (SAC) based Deep Reinforcement Learning (DRL) to control the cooling setpoint (and hence cooling loads) of a large commercial building to harness energy flexibility. The research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building stock. SAC is a model-free DRL technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy systems. Such control techniques are seen as one possible solution to supporting the operation of a smart, sustainable and future electrical grid. This research tests the suitability of the SAC DRL technique through training and deployment of the agent on an EnergyPlus based environment of the office building. The SAC DRL was found to learn an optimal control policy that was able to minimise energy costs by 9.7% compared to the default rule-based control (RBC) scheme and was able to improve or maintain thermal comfort limits over a test period of one week. The algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available variables. The robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and climates. It was found that the SAC DRL requires minimal training sample points and outperforms the RBC after three months of operation and also without disruption to thermal comfort during this period. The agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended.Comment: submitted to Energy and A