Evolutionary optimization of the multiobjective transshipment problem with limited storage capacity

In situations where some sellers have surplus stock while others, belonging to the same firm, are stocked out, it may be desirable to share the unsold units to fulfill more unmet demands and avoid holding costs. Such practice is named Transshipment. It ensures cost reduction and service level improvement. In this paper, we present a multiobjective study of a multi-location transshipment inventory which optimizes three objectives: (1) the aggregate cost, (2) the fill rate, and (3) the shared inventory quantity (SIQ), in the presence of different storage capacity constraints. Simulation is needed to evaluate the expected value of the problem stochastic objective functions. Two reference evolutionary multiobjective algorithms (SPEA2 and NSGA-II) are used to solve instances of the problem. Based on the obtained Pareto fronts, it is shown that both low aggregate cost and high fill rate levels could be ensured, while the shared inventory quantity is considerably increased.