A sustainable supply chain model with two inspection errors and carbon emissions under uncertain demand

This study analyzes a two-level sustainable supply chain model using the meta-heuristic evolutionary algorithm. Defective items are exposed due to malfunctions in the equipment during the manufacturing process. The buyer undergoes a quality inspection process to identify defective products. When inspecting the product quality, the quality checker falsely certifies the good quality item as defective or defective item as non-defective. Moreover, in such an environment, there may be uncertainty in product demand. Therefore, this paper analyzes the ambiguous supply chain model under controllable lead time with two types of inspection errors. Furthermore, in this paper, the demand for the product is addressed as a pentagonal fuzzy number and the defuzzification process follows a graded mean integration method. In addition, energy consumption for various factors and carbon emission are incorporated. The objective of this research is to examine the best optimal solutions via a genetic algorithm with the minimum expected total cost of the supply chain under uncertain demand. In order to validate the proposed model, two numerical examples and their discussion are included. At last, sensitivity analysis, managerial insights, conclusion and future directions are given.

Abstract licence: CC BY-NC

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