Ant Colony Optimization With an Improved Pheromone Model for Solving MTSP With Capacity and Time Window Constraint

Min Wang,Tongmao Ma,Xue Zhai,Sibo Qiao,Guiling Li

doi:10.1109/access.2020.3000501

Abstract

The optimization of logistics distribution can be defined as the multiple traveling salesman problem (MTSP). The purpose of existing heuristic algorithms, such as Genetic Algorithm (GA), Ant Colony Algorithm (ACO), etc., is to find the optimal path in a short time. However, two important factors of logistics distribution optimization, including work time window and the carrying capacity of the vehicle in distribution system, have been ignored. In this paper, we consider the influences of time limitation of modern commercial logistics and carrying capacity of the vehicle on the logistics optimization, and then propose a MTSP with constraints of time window and capacity of each salesman. We design a novel hybrid algorithm by combining the minimum spanning 1-tree with ACO to find the optimal solution. In addition, we improve the pheromone update rules to increase the search efficiency of ACO algorithm. The experiments show that the novel hybrid algorithm achieves a shorter path than the other algorithms.

Highlights

Logistics distribution industry becomes more and more important due to the rapid development of the e-business
Service composition [1]–[3] is a way which can combine all the logistics services processes efficiently. Another challenge is the balance between paths and items and it is more difficult.The logistics services process in China is to deliver all items from the head office to each brand
RELATED WORK APPLYING ACO TO TSP AND MTSP we review the literatures that ant colony optimization is used to settle traveling salesman problem as well as multiple traveling salesman problem and figure out their pros and cons and clarify the remaining gaps and challenges for further investigations

Summary

INTRODUCTION

Logistics distribution industry becomes more and more important due to the rapid development of the e-business. The authors propose a novel hybrid discrete PSO algorithm which can improve the search performance in convergent speed and precision This method can be applied to solve the problem of path optimization in TSP. In [24], Wang et al propose an ant colony algorithm with neighborhood search called NS-ACO to solve the problem of dynamic TSP which is composed by random traffic factors. They take the advantage of the short-term memory to enhance the kinds of solutions. INTRODUCTION OF ACO Ant colony optimization is a heuristic algorithm which is introduced into many combinatorial optimization problems due to it is one of the highest performance computing methods for MTSP [33].

PATH SELECTION

IMPROVED ALGORITHM

EVALUATION

CONCLUSION