Robust optimization of owned and outsourced capacity allocation for logistics companies via NAGA
To optimize the owned and outsourced capacity allocation for logistics companies,considering the demand uncertainty and the vehicle heterogeneity,a robust optimization model for logistic capacity allocation with multiple vehicle types was constructed to maximize annual transport profit.A nested adaptive genetic al-gorithm(NAGA)with an inner and outer double-layer structure was designed based on the hierarchical char-acteristics of decision variables.Using actual data from a logistics company in Shanghai,case studies under certain and uncertain demand scenarios were conducted.Results show that the robust optimization model effec-tively balances economy and robustness,and the NAGA solves it efficiently.Under certain demand,opti-mized transport profit increases by 6.2％,with the NAGA outperforming typical nested heuristic algorithms in solution quality and stability.Under uncertain demand,the proposed method allows decision-makers to adjust the parameters of the robust optimization model according to the market volatility and risk preference,in order to flexibly obtain suitable owned and outsourced capacity allocation schemes.
capacity allocationrobust optimizationtransport demand uncertaintyvehicle type heterogeneitynested adaptive genetic algorithm(NAGA)
万方数据
维普
