A simulation- and genetic algorithm-based optimisation of closed-loop multi-echelon inventory system

TLDR: A genetic algorithm (GA)-based optimisation approach for a multi-echelon closed-loop inventory system of items, which are repairable in nature, is developed and established that keeping a higher base stock of spare tyres at the divisions than at the depots is operationally better.

Abstract: The objective of this research is to develop a genetic algorithm (GA)-based optimisation approach for a multi-echelon closed-loop inventory system of items, which are repairable in nature. In the context of the passenger transportation industry, engineering aggregates like engines, alternators, axles and tyres are representative examples of such systems. The present work is motivated by a real-life example of state-owned transport corporation having more than 9,000 buses. Operationally, the corporation is divided across several depots (the lower most echelon) and divisions (the next higher echelon). The contribution from this research is manifold. In terms of specific insights, it is established that keeping a higher base stock of spare tyres at the divisions than at the depots is operationally better. This is a consequence of the risk pooling effect. The present research enables us to understand the optimal policy parameters of a complex multi-echelon inventory system. The whole optimisation approach and the simulation model can be generalised and can fit well in several other related problems and their contexts. Typically, the approach is applicable to the problems of repairable-parts inventory related to industries with heavy utilisation of equipments like the chemical and the petrochemical industries.