Logistics Optimization Using Hybrid Genetic Algorithm (HGA): A Solution to the Vehicle Routing Problem With Time Windows (VRPTW)

Abstract

The Vehicle Routing Problem with Time Windows (VRPTW) is paramount in elevating operational efficiency, driving cost reductions, and enhancing customer satisfaction. It is a renowned challenge with diverse real-world applications, where the core objective is determining the most efficient routes for a fleet of vehicles. This research introduces a cutting-edge Hybrid Genetic Algorithm-Solomon Insertion Heuristic (HGA-SIH) solution, reinforced by the powerful Solomon Insertion constructive heuristic to solve the VRPTW as an NP-hard problem. The performance of the proposed HGA-SIH is validated against Solomon's VRPTW benchmark instances. The results showcase the outstanding performance of HGA, achieving Best-Known Solutions (BKS) for 11 instances and enhancing BKS solutions in one instance. Experimental findings validate that HGA-SIH consistently delivers results on par with or surpasses those obtained by several cutting-edge algorithms when evaluated based on various solution quality metrics. HGA-SIH consistently excels in efficiently managing the number of vehicles while minimizing travel distances, resulting in slight deviations from BKS that remain within practical limits. The research highlights the adaptability and efficacy of HGA-SIH in addressing a wide range of VRPTW scenarios, thereby making substantial contributions to logistics and supply chain optimization.

Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

“…Meta-heuristic approaches such as GA, PSO, and ACO algorithms have been extensively utilized in addressing shortest path problems across diverse research domains. For instance, Ayesha et al (2024) present an innovative Hybrid Genetic Algorithm-Solomon Insertion Heuristic (HGA-SIH) solution, enhanced by the robust Solomon insertion constructive heuristic for solving the NP-hard VRPTW problem [13]. Khoo et al (2021) introduce a genetic algorithm specifically tailored for tackling the multi-objective vehicle routing problem with time windows (MOVRPTW).…”

“…Meta-heuristic algorithms can be categorized into two main groups: single-solutionbased and population-based [13]. The single-solution-based heuristic algorithms encompass classical methods such as SA, TS, and VNS, as well as prominent algorithms like large neighborhood search (LNS) and adaptive large neighborhood search (ALNS).…”

“…Year Algorithms Single-Based Heuristics LNS/ALNS EC SI Other [13] 2024 HGA-SIH ✓ ✓ [14] 2021 HRRGA ✓ [15] 2018 PSO-ABC ✓ [18,19] 2017 GA-VNS ✓ ✓ [20] 2016 FEPSO ✓ ✓ [16] 2024 N-CLPSO ✓ ✓ [17] 2024 ACS-KS ✓ ✓ [21] 2023 MFGA ✓ ✓ [22] 2017 HSA ✓ [23] 2012…”

“…Despite significant improvements in solution quality, current methods still encounter challenges such as lengthy computation time and susceptibility to local optima, particularly when addressing large-scale and highly complex VRPTW. Many scholars have utilized heuristic and meta-heuristic algorithms for solving large-scale VPRTW problems, with GA, PSO, and ACO being widely employed [13][14][15][16][17]. To enhance solution quality, most improved algorithms continue to rely on simple evolutionary and local search methods, resulting in slow convergence and limited optimization effects.…”

“…To enhance solution quality, most improved algorithms continue to rely on simple evolutionary and local search methods, resulting in slow convergence and limited optimization effects. Researchers have started exploring hybrid algorithms that integrate heuristic and meta-heuristic methods, showing some progress [4,[13][14][15][16][17][18][19][20][21][22][23]. However, the integration of swarm intelligence optimization algorithms with population diversity and neighborhood search algorithms is a promising research topic for improving accuracy and convergence speed when dealing with largescale and complex VRPTW problems.…”