Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Abstract

Vehicle routing is a well-known optimization research topic with significant practical importance. Among different approaches to solving vehicle routing, heuristics can produce a satisfactory solution at a reasonable computational cost. Consequently, much effort has been made in the past decades to develop vehicle routing heuristics. In this article, we systematically survey the existing vehicle routing heuristics, particularly on works carried out in recent years. A classification of vehicle routing heuristics is presented, followed by a review of their methodologies, recent developments, and applications. Moreover, we present a general framework of state-of-the-art methods and provide insights into their success. Finally, three emerging research topics with notable works and future directions are discussed.

A Neural Multi-Objective Capacitated Vehicle Routing Optimization Algorithm Based on Preference Adjustment

“…Osman combined tabu search and simulated annealing (SA) to solve the vehicle routing problem [32]. There are also many other studies that combined different heuristic methods to adapt to more complicated problems [19].…”

“…Xin et al [37] further proposed a multi-decoder architecture, which learns multiple decoding policies at the same time, which increases the chance of finding better solutions. Despite the potential of the end-to-end approach, the machine learning-assisted heuristic has better performance [19]. Wu et al [38] learned a method to improve the original solution.…”

“…In many related studies, the VRP is modeled as a sequential decision problem, which may lead to the problem of a long computation time when solving large-scale problems. Therefore, solving the VRP with neural combinatorial optimization methods both has potential and is challenging [19].…”