A Multi-Period Vehicle Routing Problem for Emergency Perishable Materials under Uncertain Demand Based on an Improved Whale Optimization Algorithm

Xiaodong Li, Yang Xu (), Kin Keung Lai, Hao Ji, Yaning Xu and Jia Li
Additional contact information
Xiaodong Li: College of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Yang Xu: College of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Kin Keung Lai: Department of Industrial and Manufacturing Systems Engineering, Hong Kong University, Hong Kong 999077, China
Hao Ji: College of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Yaning Xu: College of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Jia Li: Institute of Service Assurance Centre, Air Force Medical University, Xi’an 710000, China

Mathematics, 2022, vol. 10, issue 17, 1-17

Abstract: The distribution of emergency perishable materials after a disaster, such as an earthquake, is an essential part of emergency resource dispatching. However, the traditional single-period distribution model can hardly solve this problem because of incomplete demand information for emergency perishable materials in affected sites. Therefore, for such problems we firstly construct a multi-period vehicle path distribution optimization model with the dual objectives of minimizing the cost penalty of distribution delay and the total corruption during delivery, and minimizing the total amount of demand that is not met, by applying the interval boundary and most likely value weighting method to make uncertain demand clear. Then, we formulate the differential evolutionary whale optimization algorithm (DE-WOA) combing the differential evolutionary algorithm with the whale algorithm to solve the constructed model, which is an up-and-coming algorithm for solving this type of problem. Finally, to validate the feasibility and practicality of the proposed model and the novel algorithm, a comparison between the proposed model and the standard whale optimization algorithm is performed on a numerical instance. The result indicates the proposed model converges faster and the overall optimization effect is improved by 23%, which further verifies that the improved whale optimization algorithm has better performance.

Keywords: emergency material distribution; multi-period; uncertain demand; perishable materials; whale optimization algorithm; differential evolution algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/17/3124/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/17/3124/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:17:p:3124-:d:902895

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().