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This paper considers locating congested fast charging stations (FCSs) and deploying chargers in a stochastic environment, while the related studies have predominantly focused on problems in deterministic environments. Reducing the inconvenience caused by congestion at FCSs is an important challenge for FCS service provider. This is the underlying motivation for this study to consider a problem for FCS network design with the congestion restriction in a stochastic environment. We proposed a maximal coverage problem subject to budget constraints and a congestion restriction in order to maximize the demand coverage. With the derivation of the congestion restriction in the considered stochastic environment, the problem is formulated into an integer programming model. A real-life case study is conducted and managerial implications are drawn from its results.

This paper aims to describe the development of a knowledge management system (KMS) for the Nadir and Occultation for Mars Discovery (NOMAD) instrument on board the ESA/Roscosmos 2016 ExoMars Trace Gas Orbiter (TGO) spacecraft. The KMS collects knowledge acquired during the engineering process that involved over 30 project partners. In addition to the documentation and technical data (explicit knowledge), a dedicated effort was made to collect the gained experience (tacit knowledge) that is crucial for the operational phase of the TGO mission and also for future projects. The system is now in service and provides valuable information for the scientists and engineers working with NOMAD.

The NOMAD KMS was built around six areas: official documentation, technical specifications and test results, lessons learned, management data (proposals, deliverables, progress reports and minutes of meetings), picture files and movie files. Today, the KMS contains 110 GB of data spread over 11,000 documents and more than 13,000 media files. A computer-aided design (CAD) library contains a model of the full instrument as well as exported sub-parts in different formats. A context search engine for both documents and media files was implemented.

The conceived KMS design is basic, flexible and very robust. It can be adapted to future projects of a similar size.

The paper provides practical guidelines on how to retain the knowledge from a larger aerospace project. The KMS tool presented here works offline, requires no maintenance and conforms to data protection standards.

This paper shows how knowledge management requirements for space missions can be fulfilled. The paper demonstrates how to transform the large collection of project data into a useful tool and how to address usability aspects.

This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.

The data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.

The contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.