Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management

Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational in order make profits an extremely competitive environment. Therefore, the efficiency of management supply chain quick response customers’ demands are treated as additional source profit. One warehouse operations for intelligent design, called cross-docking (CD) operations, is used inventory levels improve responsiveness meet requirements. Accordingly, optimization a vehicle dispatch schedule imperative produce plan with minimum transport cost while meeting demand allocation. Methods: This paper developed two-phase algorithm, sAIS, solve problem (VRP) CD facilities systems operations. The sAIS algorithm based on clustering-first routing-later approach. sweep method cluster trucks initial solution second phase: optimizing by Artificial Immune System. Results: In examine performance proposed approach, we compared model Genetic Algorithm (GA) VRP pickup delivery benchmark problems, showing average improvements 7.26%. Conclusions: this study, novel solving problems simulating human body immune reactions. experimental results showed that robustly GA criterion quality measured two-sample t-test.