Abstract
In collaborative logistics network (CLN) models, the uncertainty of supply often occurs during the resource matching process due to the fact that each individual cooperative enterprise tries to maximize its own operating profit. This paper presents a bi-level programming model which can reduce or eliminate the adverse effects of supply uncertainty in CLN resource matching process. By adding the robust constraints, this model reduces both the frequency and the cost of resource planning changes, which lead to increasing stability of CLN operation. Based on the particular form of the model, a new algorithm combined by Monte Carlo method (MCM) and particle swarm optimization (PSO) is proposed. Results from example analysis demonstrate the effectiveness of the proposed model and algorithm.
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