# Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery
## 2 Related Works  
Transportation optimization has been a central topic in scientific research, focusing on improving logistical systems and transportation infrastructure. The literature covers various aspects of this field, including route optimization [1; 2; 3; 4; 5], traffic flow management [6; 7; 8; 9], and transportation cost minimization [10; 11; 12]. The GA has become a widely used approach for solving transportation logistics problems. Many studies highlight the ability of a GA to handle large datasets and provide approximate solutions to complex combinatorial problems, such as vehicle routing, resource allocation, and transportation network optimization. These methods offer a structured way to address challenges that require balancing computational complexity and solution quality. [PERSON] et al. [13] presented a location-inventory-routing model for perishable products; by integrating strategic, tactical, and operational factors, they proposed a GA approach that achieved high-quality solutions in a reasonable time frame by introducing novel chromosome representation and local search heuristics, which were validated through analysis. [PERSON] et al. [14] tackled the transportation-location routing problem (TLRP), where they addressed environmental and social concerns in supply chain management. Their method optimizes distribution networks to minimize costs, fuel consumption, and carbon emissions while meeting customer time windows and balancing driver workloads. Using Non-dominated Sorting GA (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO), it offered efficient solutions, where MOPSO showed superior performance. [PERSON] et al. [15] explored a multinode facility location routing problem in logistics, integrating disposal centers, transfer stations, and collection sites into a hierarchical model. GA is used to optimize costs, including construction and transport. The model considers bidirectional transportation and dynamic routing for unconnected sites, solving both the facility location problem (FLP) and vehicle routing problem (VRP). Through a case study, the model demonstrated cost reductions of 6-8% compared with traditional logistics systems. [PERSON] et al. [16] introduced a two-stage location routing model for cold-chain logistics optimization. A hybrid GA was developed to minimize the total costs and accommodate varying vehicle capacities. Validation through random tests confirmed the model's efficacy. The results show the feasibility and effectiveness of the two-stage approach in reducing distribution costs for cold-chain logistics enterprises compared with single-stage routing problems. [PERSON] et al. [17] introduced an integrated model for sustainable supply chains in industries with perishable products, addressing location, routing, and inventory problems. They proposed a two-phase approach incorporating sustainability dimensions and employed hybrid metaheuristics to solve the NP-hard problem. Test instances showed the superiority of parallel hybridization. Additionally, decentralization in the manufacturing sector and local commodity production enhanced the sustainability performance. The limitations hindering the market share of battery electric vehicles (BEVs) by introducing mobile charging vehicles (MCVs) for on-demand charging are highlighted by [PERSON] et al. [18]. The approach integrates location and routing decisions to optimize the placement and movement of plug-in charging facilities and MCVs simultaneously. The problem is formulated as the multiple types of plug-in charging facilities' location routing problem with time windows for MCVs (MTPCF-LRPwTWMCVs), tackled through a mixed integer linear program. Computational results and sensitivity analyses demonstrate the efficiency improvements with increased battery capacities or charging rates. This approach offers practical solutions to real-world challenges in BEV charging infrastructure.