213
Views
17
CrossRef citations to date
0
Altmetric
Articles

Variable neighborhood search incorporating a new bounding procedure for joint replenishment and delivery problem

ORCID Icon, &
Pages 201-219 | Received 23 Mar 2016, Accepted 18 Jan 2017, Published online: 16 Jan 2018
 

Abstract

In recent years, joint replenishment and delivery problem (JRD) has been examined extensively in the context of replenishment and inventory control. However, as an extension of joint replenishment problem, the JRD is more complex and difficult to optimize with an exact algorithm. In this study, lower and upper bounds of JRD are obtained approximately but quickly using a novel bounding procedure. Then, a variable neighborhood search (VNS) with the bounding method is developed to solve the JRD. Computational examples show that the bounding procedure can effectively and efficiently determine satisfactory bounds, which are helpful for the proposed VNS. Results of randomly generated examples further indicate that the hybrid VNS performs better than the best known heuristic and metaheuristic for JRD in terms of accuracy.

Acknowledgments

The authors are very grateful for the constructive comments of editors and referees. This research is partially supported by National Natural Science Foundation of China (grants numbers: 71371080; 71531009) and Humanities and Social Sciences Foundation of Chinese Ministry of Education (No. 15YJA630095).

Notes

Please note this paper has been re-typeset by Taylor & Francis from the manuscript originally provided to the previous publisher.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 277.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.