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Multi-stakeholder based construction projects are subject to potential risk factors due to dynamic business environment and stakeholders' lack of knowledge. When solving project management tasks, it is necessary to quantify the main risk indicators of the projects. Managing these requires suitable risk mitigation strategies to evaluate and analyse their severity. The existence of information asymmetry also causes difficulties with achieving Pareto efficiency. Hence, to ensure balanced satisfaction of all participants, risk evaluation of these projects can be considered as an important part of the multi-criteria decision-making (MCDM) process. In real-life problems, evaluation of project risks is often uncertain and even incomplete, and the prevailing methodologies fail to handle such situations. To address the problem, this paper extends the analytical network process (ANP) methodology in the D numbers domain to handle three types of ambiguous information's, viz. complete, uncertain, and incomplete, and assesses the weight of risk criteria. The D numbers based approach overcomes the deficiencies of the exclusiveness hypothesis and completeness constraint of Dempster-Shafer (D-S) theory. Here, preference ratings of the decision matrix for each decision-maker are determined using a D numbers extended consistent fuzzy preference relation (D-CFPR). An extended multi-attributive border approximation area comparison (MABAC) method in D numbers is then developed to rank and select the best alternative risk response strategy. Finally, an illustrative example from construction sector is presented to check the feasibility of the proposed approach. For checking the reliability of alternative ranking, a comparative analysis is performed with different MCDM approaches in D numbers domain. Based on different criteria weights, a sensitivity analysis of obtained ranking of the hybrid D-ANP-MABAC model is performed to verify the robustness of the proposed method.

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In recent era of globalization, the world is perceiving an alarming rise in its energy consumption resulting in shortage of fossil fuels in near future. Developing countries like India, with fast growing population and economy, is planning to explore among its existing renewable energy sources to meet the acute shortage of overall domestic energy supply. For balancing diverse ecological, social, technical and economic features, selection among alternative renewable energy must be addressed in a multi-criteria context considering both subjective and objective criteria weights. In the proposed COPRAS-Z methodology, Z-number model fuzzy numbers with reliability degree to represents imprecise judgment of decision makers’ in evaluating the weights of criteria and selection of renewable energy alternatives. The fuzzy numbers are defuzzified and renewable energy alternatives are prioritized as per COmplex PropoRtional ASsessment (COPRAS) decision making method in terms of significance and utility degree. A sensitivity analysis is done to observe the variation in ranking of the criteria, by altering the coefficient of both subjective and objective weight. Also, the proposed methodology is compared with existing multi-criteria decision making (MCDM) methods for checking validity of the obtained ranking result.

The Birbhum HDSS was established in 2008 and covers 351 villages in four administrative blocks in rural areas of Birbhum district of West Bengal, India. The project currently follows 54 585 individuals living in 12557 households. The population being followed up is economically underprivileged and socially marginalized. The HDSS, a prospective longitudinal cohort study, has been designed to study changes in population demographic, health and healthcare utilization. In addition to collecting data on vital statistics and antenatal and postnatal tracking, verbal autopsies are being performed. Moreover, periodic surveys capturing socio-demographic and economic conditions have been conducted twice. Data on nutritional status (children as well as adults), non-communicable diseases, smoking etc. have also been collected in special surveys. Currently, intervention studies on anaemia, undernutrition and common preschool childhood morbidities through behavioural changes are under way. For access to the data, a researcher needs to send a request to the Data Manager [suri.shds@gmail.com]. Data are shared in common formats like comma-separated files (csv) or Microsoft Excel (xlsx) or Microsoft Access Database (mdb).The HDSS will soon upgrade its data management system to a more integrated platform, coordinated and guided by INDEPTH data sharing policy.