A Pareto approach for the multi-factory supply chain scheduling and distribution problem

Ali Gharaei () and Fariborz Jolai ()
Additional contact information
Ali Gharaei: University of Tehran
Fariborz Jolai: University of Tehran

Operational Research, 2021, vol. 21, issue 4, No 6, 2333-2364

Abstract: Abstract Integrated decisions in the supply chain are one of the most attractive topics for researchers. But to get closer to the real-world problems, other real assumptions should be considered. One of these assumptions is the multi-agent view in which several sets of customers or agents with their own objective compete with each other to acquire the supply chain resources. Here, an integrated supply chain scheduling problem along with the batch delivery consideration in a series multi-factory environment is investigated and the routing decisions among customers are considered. A mathematical model is presented for this problem. Due to the complexity, a novel ant colony optimization algorithm is developed to obtain Pareto solutions. Also, a simulated annealing based local search is used to improve the quality of solutions. The performance of the algorithm is compared with three well-known multi-objective algorithms. Results show the proper performance of the proposed algorithm compared to the other algorithms.

Keywords: Multi-factory; Ant colony optimization; Batch delivery; Multi-agent (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s12351-019-00536-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:operea:v:21:y:2021:i:4:d:10.1007_s12351-019-00536-7

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-019-00536-7

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().