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Green Lean Six Sigma has emerged in the Industry 4.0 era as a business strategy contributing to the circular economy by adopting the 3R concept, i.e., reduce, reuse, and recycle. Despite its broadly acknowledged capabilities in the manufacturing industry, practitioners continue to be cautious about its implementation, owing to insufficient knowledge and culture. Hence, there is a need to systematize the existing knowledge regarding this green initiative and also to recognize the key factors enabling its implementation. In the Malaysian manufacturing context, the enabling factors have yet to be identified and evaluated. This current study is the first of its kind to identify and examine these factors and to create a structural model to conceptualize and operationalize this business strategy. The implemented methodological approach includes two steps. Firstly, it performs a systematic review of leading studies on the topic, which are rather scarce in the current context. The second step entails a principal component factor analysis using varimax rotation to finalize the findings. The theoretical and empirical results revealed a structural model with five interconnected key factors, including twenty-seven enablers, that can be used to narrow the existing knowledge gap in the understudied context.

The scheduling of open-pit mine production is a large-scale, mixed-integer linear programming problem that is computationally expensive. The purpose of this study is to create a computationally efficient algorithm for solving open-pit production scheduling problems with uncertain geological parameters. To demonstrate the effectiveness of the proposed research, a case study of an Indian iron ore mine is presented. Multiple realizations of the resource models were developed and integrated within the stochastic production scheduling framework to capture uncertainty and incorporate it into the mine plan. In this case study, two hybrid methods were developed to evaluate their performance. Model 1 is a combined branch and cut with the longest path, whereas Model 2 is a sequential parametric maximum flow and branch and cut. The results show that both methods produce similar materials, ore, metal, and risk profiles; however, Model 2 generates slightly more (4 percent) discounted cash flow from this study mine than Model 1. The results also show that Model 2’s computational time is 46.64 percent less than that of Model 1.