Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

Tahereh Mohammadi, Seyed Mojtaba Sajadi (), Seyed Esmaeil Najafi and Mohammadreza Taghizadeh-Yazdi
Additional contact information
Tahereh Mohammadi: Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
Seyed Mojtaba Sajadi: Operations and Information Management Department, Aston Business School, Aston University, Birmingham B4 7ET, UK
Seyed Esmaeil Najafi: Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
Mohammadreza Taghizadeh-Yazdi: Faculty of Management, University of Tehran, Tehran 1417466191, Iran

Mathematics, 2024, vol. 12, issue 5, 1-30

Abstract: With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

Keywords: intelligent supply chain; Internet of Things; Radio Frequency Identification; Wireless Sensor Network; mathematical modeling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/5/679/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/5/679/ (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:12:y:2024:i:5:p:679-:d:1346058

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 ().