A Mathematical Model to Optimize Cost, Time in The Three echelon Supply Chain in Post COVID 19 pandemic

The purpose of this paper is to optimize Cost & time in the three echelon supply chain (SC) network. This paper developed a linear programing (LP) model to consider economic data.

The overall objective of the present study is to use high-quality raw materials, at the same time in post COVID 19 pandemic and the highest profitability is achieved. The model in the problem is solved using two metaheuristic algorithms, namely, Cuckoo and Genetic. Optimization of supply chain performance indicators in minimization of cost and time and maximization of sustainability indexes of the system.

The differences found between the genetic algorithms (GAs) and the LP approaches can be explained by handling the constraints and their various logics. To deal with ambiguity in the reverse logistics network, a fuzzy approach has been applied. To solve the problem in large dimensions, meta-heuristic algorithms of Cuckoo and Genetic were employed by applying MATLAB software. In order to compare two optimization algorithms, a series of sample problems have been generated then the results of two algorithms were compared and superiority of each of them was discussed.