A Two-Phase Distributed Ruin-and-Recreate Genetic Algorithm for Solving the Vehicle Routing Problem With Time Windows

Abstract

Developing an algorithm that can solve the vehicle routing problem with time windows (VRPTW) and create near-optimal solutions with the least difference in magnitude is a challenging task. This task is evident from the fact that when an algorithm runs multiple times based on a given instance, the generated solutions deviate from each other and may not near-optimal. For this reason, an algorithm that can solve these problems is effective and highly sought after. This paper proposes a novel systematic framework using a two-phase distributed ruin-and-recreate genetic algorithm (RRGA). The two-phase consists of the RRGA phase and ruin-and-recreate (RR) phase, which is designed to run in the distributed computing environment that leveraging these networked resources. This combination of algorithms harnesses the strength of the search diversification and intensification, thereby producing very high-quality solutions. Experiments with Solomon's benchmark show the RRGA can produce results superior to the recently published hybrid algorithms, best-known solutions, and nine leading hybrid algorithms.

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

“…Notably, LNS-MRSO attains a remarkable score of 994 in terms of total distance traveled, ranking fourth and significantly surpassing the BKS. As the bolded data shows, only three algorithms, namely MFGA [21], RRGA [54], and Tabu-ABC [55], outperform LNS-MRSO in this metric. A common characteristic among these superior algorithms is their capacity to reduce the total travel distance by increasing the number of vehicles utilized.…”

A Systematic Literature Review of Vehicle Routing Problems with Time Windows

“…In recent years, VRPTW has become an active area of research, and several studies have focused on developing new algorithms and techniques to improve the efficiency and effectiveness of vehicle routing. However, despite significant progress, there are still several challenges that need to be addressed, including the incorporation of multiple constraints [2][3][4][5][13][14][15][16][32][33][34][35][36][37][38][39][40][41][42][43][44][45], multiple objects [2,4,5,16,[45][46][47][48], the use of realistic datasets [4,28,49,50], and the adaptation of solutions to real-world scenarios [8,9,20,25,28,38,[51][52][53]. Another important challenge in the context of VRPTW is the evaluation and comparison of existing algorithms.…”

An Adaptive Variable Neighborhood Search Ant Colony Algorithm for Vehicle Routing Problem With Soft Time Windows

“…Therefore, researchers often use a metaheuristic algorithm or an improved heuristic algorithm to solve the optimal combinatorial optimization problem [12]. The metaheuristic algorithms commonly used by scholars in this field include tabu search (TS) algorithms [13], [14], simulated annealing algorithms (SAA) [15], genetic algorithms (GA) [16], ant colony algorithms (ACA) [17], and particle swarm optimization (PSO) algorithms [18]. Due to the positive feedback performance and simple operation of ACA, many scholars use this algorithm to solve the vehicle routing problem [19]- [22].…”

“…obtained by two successive iterations is less than 0.001. If the trigger condition is satisfied, the pheromone volatilization coefficient is adjusted according to equation(16).…”