A spatial parallel heuristic approach for solving very large‐scale vehicle routing problems

Abstract

The vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large-scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man-made VRP benchmark and three real-world super-large-scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time. K E Y W O R D S Local search, logistics, parallel computing, spatial partition, vehicle routing problems 1 | I NTR OD U CTI ON The vehicle routing problem (VRP) is one of the most prominent problems in the literature of spatial optimization.A solution of a VRP aims to find optimal routes to serve a set of geographically scattered customers such that each customer is served once and the vehicle does not operate beyond its capacity. It has many real-world applications in both the public and private sectors, such as parcel delivery

An Algorithm to Solve Heterogeneous Vehicle Routing Problem With Second Trip

“…By consulting the latest relevant research literature, we found that they only contain one or part of the above constraints, rather than all. Tu [2] proposed a novel spatial parallel optimization approach integrating spatial partitioning, local search, and parallel, effectively solving the very large-scale VRP of thousands of customers within half an hour. However, this method only restricts vehicle capacity.…”

Employing waterborne autonomous vehicles for museum visits: a case study in Amsterdam

“…The TSP route should eventually return to the departure point. When more than one vehicle is considered, VRP is used to find the optimal routes traversing a set of locations for a fleet of vehicles [7,8]. Different from TSP, in VRP, multiple vehicles depart from depots or warehouses and cooperate to deliver or pick-up goods [36].…”

“…Different from TSP, in VRP, multiple vehicles depart from depots or warehouses and cooperate to deliver or pick-up goods [36]. All three types of models have been widely applied in many smart city applications, such as pedestrian navigation in complex urban environment [37], tourist route recommendation [38], taxicab operations [39], urban logistics [7,8], etc.…”

“…Regarding TSP and VRP, they can be solved with the exact and the heuristics algorithm. As TSP and VRP belong to the combination problem, it is hard to use them to solve large scale problems in a reasonable time [7,8]. Heuristic algorithms are effective to find local optimal routes, such as the insertion heuristics, local search, simulated annealing, ant colony optimization, and genetic algorithm.…”

“…Meanwhile, artificial intelligence (AI) is changing contemporary cities by introducing disruptive technologies and smart city operations [5]. AI is incorporated into urban solutions as tools to solve urban issues and bring more efficiency [6][7][8][9]. Autonomous vehicles (AV) are imagined to drastically change the mobility landscape in cities.…”