Managing inventory levels and time to market in assembly supply chains by swarm intelligence algorithms

Abstract

An assembly supply chain (SC) is composed of stages that provide the components, assemble both sub-assemblies and final products, and deliver products to the customer. The activities carried out in each stage could be performed by one or more options, thus the decision-maker must select the set of options that minimises the cost of goods sold (CoGS) and the lead time (LT), simultaneously. In this paper, an ant colony-based algorithm is proposed to generate a set of SC configurations using the concept of Pareto optimality. The pheromones are updated using an equation that is a function of the CoGS and LT. The algorithm is tested using a notebook SC problem, widely used in literature. The results show that the ratio between the size of the Pareto Front computed by the proposed algorithm and the size of the one computed by exhaustive enumeration is 90%. Other metrics regarding error ratio and generational distance are provided as well as the CPU time to measure the performance of the proposed algorithm.