Evolutionary multiobjective optimization of the multi-location transshipment problem

TL;DR

This paper develops a multiobjective evolutionary optimization model for a multi-location transshipment inventory system, balancing costs, service levels, and lead times, and demonstrates the trade-offs through simulation.

Contribution

It introduces a novel multiobjective model for the transshipment problem and applies SPEA2 to approximate the Pareto front, addressing conflicting operational goals.

Findings

Trade-offs between cost, fill rate, and lead time are demonstrated.

SPEA2 effectively approximates the Pareto front for complex inventory problems.

Simulation results highlight the impact of different parameters on objectives.

Abstract

We consider a multi-location inventory system where inventory choices at each location are centrally coordinated. Lateral transshipments are allowed as recourse actions within the same echelon in the inventory system to reduce costs and improve service level. However, this transshipment process usually causes undesirable lead times. In this paper, we propose a multiobjective model of the multi-location transshipment problem which addresses optimizing three conflicting objectives: (1) minimizing the aggregate expected cost, (2) maximizing the expected fill rate, and (3) minimizing the expected transshipment lead times. We apply an evolutionary multiobjective optimization approach using the strength Pareto evolutionary algorithm (SPEA2), to approximate the optimal Pareto front. Simulation with a wide choice of model parameters shows the different trades-off between the conflicting objectives.