Sustainable-resilient-responsive supply chain with demand prediction: An interval type-2 robust programming approach

Abstract

This study configures a novus multi-objective three-stage supply chain network that aims at optimizing sustainability, resilient and responsive measures, simultaneously. At first, a novel prediction model based on Markov chain, Dempster-Safer evidence theory and Shapely value is developed under trapezoidal interval type-2 fuzzy ambience to predict the demand of future scenarios. The prediction model prevents undesired loss occurred due to illogical demand consideration. To encounter interval type-2 fuzziness as well as risk in decision making, an unprecedented trapezoidal interval type-2 fuzzy robust possibilistic programming approach is introduced. The DEcision MAking Trial and Evaluation Laboratory method is suggested in the framework of trapezoidal interval type-2 fuzzy to prioritize the objective function by scrutinizing the interrelationship among different objective function components. As a result, the subjectiveness in the decision making framework is reduced. The generated configuration is then analysed using a blended approach that merges multi-choice reference goal programming with utility functions and particle swarm optimization. The integrated approach produces high-quality solution in lesser computational time. The insightful explorations are delivered based on a case study of hydrogen fuel supply chain in India. The outcomes show how resilient tactics are quite effective and can significantly enhance social, environmental, and economic aspects. It is determined that the best results for supply chain are obtained when the suggested sustainable, resilience and responsiveness tactics are used jointly. Moreover, the sensitivity analysis results suggest that the objective functions are substantially affected by the responsive level; therefore, managers ought to envisage the trade-off between responsiveness and other objective functions.