Two evolutionary approaches with objective-specific variation operators for vehicle routing problem with time windows and quality of service objectives

Srivastava, Gaurav; Singh, Alok

doi:10.1016/j.asoc.2022.109964

Cited by 9 publications

(1 citation statement)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, similar to literature 12 , it only establishes the single objective function of the lowest distribution cost, and does not consider the two objectives of vehicle utilization and distribution duration, nor does it consider the constraints of different types of vehicles. Srivastava et al studied the multi-objective, single-center, single-type vehicle routing problem, and focused on the two objectives of distribution cost and distribution time 18 . Similarly, this work did not consider the vehicle utilization.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of heterogeneous vehicle logistics scheduling with multi-objectives and multi-centers

He,

Zhang,

Chen

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Industrial enterprises have high requirements on timeliness and cost when delivering industrial products to their customers. For this reason, this paper studies the vehicle routing problem (VRP) of different vehicle models in multiple distribution centers. First of all, we consider the multi-dimensional constraints in the actual distribution process such as vehicle load and time window, and build a multi-objective optimization model for product distribution with the goal of minimizing the distribution time and cost and maximizing the loading rate of vehicles. Furthermore, an Improved Life-cycle Swarm Optimization (ILSO) algorithm is proposed based on the life cycle theory. Finally, we use the order data that Yunnan Power Grid Company needs to deliver to the customer (municipal power supply bureau) on a certain day to conduct a dispatching experiment. The simulation and application results show that the transportation cost of transportation obtained by the ILSO algorithm is reduced by 0.8% to 1.6% compared with the other five algorithms. Therefore, ILSO algorithm has advantages in helping enterprises reduce costs and improve efficiency.

show abstract

Section: Literature Reviewmentioning

confidence: 99%