Intelligent Planning of Logistic Networks to Counteract Uncertainty Propagation

Abstract

AbstractA major obstacle to stable and cost-efficient management of goods distribution systems is the bullwhip effect – reinforced demand uncertainty propagating among system nodes. In this work, by solving a formally established optimization problem, it is shown how one can mitigate the bullwhip effect, at the same minimizing transportation costs, in modern logistic networks with complex topologies. The flow of resources in the analyzed network is governed by the popular order-up-to inventory policy, which thrives to maintain sufficient stock at the nodes to answer a priori unknown, uncertain demand. The optimization objective is to decide how intensive a given transport channel should be used so that unnecessary goods relocation and the bullwhip effect are avoided while being able to fulfill demand requests. The computationally challenging optimization task is solved using a population-based evolutionary technique – Biogeography-Based Optimization. The results are verified in extensive simulations of a real-world transportation network.