Large service organizations such as telecom or utility companies face numerous decision management problems, many of which are related to the management of various resources. The management of spare parts and inventory is one of the key resource management problems an organization faces on a daily basis. Especially their timely availability can have serious impacts on the service quality and the customer satisfaction. Lack of visibility and availability of spare parts in the right place and at the right time can lead to traveling longer distance to supply the parts or in worst case a disruption of the service. We propose to automate the management of spare parts for a legacy technology in a telecom network by leveraging an evolutionary algorithm for optimizing the distribution of spare parts. Our results show that a significant gain can be made in comparison to a assignment typically performed in a manual mechanism.

Third generation telecare involves a customised sensor network, able to detect a person's movements and use of furniture and household items, coupled with a sophisticated fuzzy data analysis process able to answer high level queries such as "is the person eating regularly". Soft computing techniques are needed due to the high degree of uncertainty in the inference process. We describe a trial system, which has been installed in two homes. Results indicate that fuzzy analysis enables us to summarise the data in a manner which is useful to the care providers without them needing to be experts in data analysis.

Many formal specification languages have been developed to engineer complex systems. However natural language (NL) has remained the choice of domain experts to specify the system because formal specification languages are not easy to master. Therefore NL requirements documentation must be reinterpreted by software engineers into a formal specification language. When the system is very complicated, which is mostly the case when one chooses to use formal specification, this conversion is both non-trivial and error-prone, if not implausible. This challenge comes from many factors such as miscommunication between domain experts and engineers. However the major bottleneck of this conversion is from the inborn characteristic of ambiguity of NL and the different level of the formalism between the two domains of NL and the formal specification. This is why there have been very few attempts to automate the conversion from requirements documentation to a formal specification language. This research project is developed as an application of formal specification and linguistic techniques to automate the conversion from a requirements document written in NL to a formal specification language. Contextual Natural Language Processing (CNLP) is used to handle the ambiguity problem in NL and Two Level Grammar (TLG) is used to deal with the different formalism level between NL and formal specification languages to achieve automated conversion from NL requirements documentation into a formal specification (in our case the Vienna Development Method - VDM++). A knowledge base is built from the NL requirements documentation using CNLP by parsing the documentation and storing the syntactic, semantic, and contextual information.