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Purpose Sustainability in supply chain is gaining attention in recent years due to environmental concern, enforced legislation, green issues, social responsibility, etc. Sustainable supply chain (SSC) has revolved around the various dimensions including economy, environment and societal factors since its inception. The purpose of this paper is to identify, prioritize and evaluate the indicators of SSC so that organizations can cultivate strategies to implement them on priority.Design/methodology/approach This paper proposes a methodology based on fuzzy analytic hierarchy process to prioritize the indicators of SSC. A numerical analysis of Indian automotive industry is presented to demonstrate the use of the proposed method. This proposed method considered fuzzy framework that can handle impreciseness and uncertainty. Sensitivity analysis is also performed to test the robustness of the proposed model.Findings Potential indicators are identified from relevant literature and validated by industry experts. This research finalizes the SSC indicators under three dimensions so that prioritization of identified indicators can be developed and the insights relationship of factors would be explored. The results of the study found that environmental and social dimensions of sustainability contribute more toward the sustainability.Research limitations/implications This study is limited to identify evaluation factors and other factors have not been identified and categorized. Evaluation is done by experts in this area so it is natural that views of decision makers may be subjective and vary with regard to industry type, priorities, resources, etc.Practical implications This study will help industry to identify, evaluate and prioritize factors for successful implementation of sustainability in their supply chain. Automotive companies could device these factors by applying the outcome of the study in their operations with higher priority to integrate sustainability in their supply chain.Originality/value These factors are identified to implement sustain ability into supply chain practices for automotive industry. [ABSTRACT FROM AUTHOR]

Summary: Time series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir. [Copyright &y& Elsevier]

Reservoir management is a highly complex problem, which includes the uncertainty in the inflows as well as in the objectives. Stochastic dynamic programming (SDP) has been widely used to develop the optimal operating policies of multipurpose reservoirs. In the recent years, fuzzy optimization has been used to incorporate the uncertainty caused due to the imprecise nature of objectives. In the present study, a comparison is made between a conventional SDP model and fuzzy dynamic programming models for optimal operation of a multipurpose reservoir. The two models are developed for Hirakud Reservoir on River Mahanadi in the State of Orissa in India. The performance of the reservoir is evaluated under the operating policies derived from these models through simulation. The results indicate that the fuzzy dynamic programming model performs better in achieving the flood control objective and thus in the overall performan1ce of reservoir. [ABSTRACT FROM AUTHOR]