AA-CS: Supply Chain Network Optimization by Nonlinear Log-Space Modelling with Adaptive Probability-based Crow Search

Abstract

Closed-loop supply chain networks (CLSCN) are modelled and handled to concern the forward and reverse supplying activities explicitly for the total lifespan of the product. Many researchers have been focused on this topic. However, problems persist owing to economical and consistency issues. Hence, this paper intends to address the practical vulnerabilities in the state-of-the-art models for the optimal design of CLSCN. While the conventional models attempt to transform the design constraints of the supply chain networks (SCN) into sum of cost functions, the proposed model transforms the cost function into a nonlinear subspace. Moreover, the subspace is optimised for a logarithmic scale and so the multiple network constraints such as, inventory cost (IC), fixed cost (FC), manufacturing cost (MC), penalty cost (PC), scrap cost (SC), return cost, surplus cost (SuC) and transmission cost (TC) are mapped within the subspace. Subsequently, crow search algorithm (CSA) is included with adaptiveness which is termed as adaptive awareness probability-based CS (AA-CS), and thus the nonlinear cost function can be solved effectively. The adaptiveness is mainly based on the ability of handing every network constraints which are analysed by the simulation results.