# Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery
## 2 Related Works
A novel variant of the location routing problem (LRP) called the LRP with simultaneous pickup and delivery (LRPSPD) was proposed by [PERSON] [19]. It proposes a hyper-heuristic approach based on iterated local search (ILS-HH) to automatically optimize the LRPSPD. The framework incorporates four selection mechanisms and five activation strategies, evaluated through three computational evaluations. The results indicate that the proposed framework outperformed classical methods in terms of solution quality and computing time. FRRMAB-TS (fitness ratio rank based on multi-armed bandit with tabu search) emerged as the most effective selection method. Additionally, the proposed approach yielded competitive or superior results compared with state-of-the-art approaches. The simultaneous facility location and vehicle routing problems in healthcare logistics in the Netherlands was described by [PERSON] et al. [20]. It addressed medication delivery from local pharmacies via lockers or home delivery. The goal was to determine which lockers to open and generate routes while minimizing costs. The problem was formally defined and solved using a branch-and-bound algorithm and a fast hybrid heuristic. Computational results demonstrated the heuristic's superiority over a commercial solver and its robustness. Insights on key problem parameters were provided, informed by both randomly generated instances and real-world data from Alliance Healthcare Netherlands. The study [21] extended a multi-objective mathematical model for industrial hazardous waste management, integrating locating, vehicle routing, and inventory control decisions. It addressed uncertainty through a shift to a stochastic environment, multi-period planning, and inventory considerations. The problem was formulated as a multi-objective stochastic mixed-integer nonlinear programming (MINLP) model that was convertible to a MILP. A novel simheuristic approach that combined Non-Dominated Sorting GA-II (NSGA-II) and Monte Carlo simulation was developed to tackle the stochastic combinatorial optimization problem efficiently. Computational findings demonstrate the approach's effectiveness in finding high-quality solutions within reasonable timeframes. The need for environmentally conscious solutions for the VRP was addressed by [PERSON] et al. [22]. They introduced models for green routing problems, considering multiple depots, time window constraints, vehicle heterogeneity, and product variations. To tackle the NP-hardness of the problem, a combined GA and simulated annealing (SA) algorithm was proposed. Comparative analysis against exact solving methods in GAMS software validated the efficiency of the proposed algorithm. Table 1 provides a summary of relevant studies that addressed LRP and related logistical challenges. The table outlines the primary focus of each work, the methods employed, key results, and identified limitations. Despite extensive research on the LRP and its variants, a gap remains in strategically positioning regional branch warehouses within dynamic logistics networks. While previous studies focused on integrating location, routing, and inventory decisions, few explicitly addressed a two-stage optimization approach for branch warehouse selection and delivery route optimization. Specifically, the challenge of dynamically selecting optimal branch warehouse locations from a set of candidate cities in response to fluctuating cargo flows has not been thoroughly explored. This study aimed to bridge this gap.