Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty

Muhammad Imran, Muhammad Salman Habib, Amjad Hussain, Naveed Ahmed and Abdulrahman M. Al-Ahmari
Additional contact information
Muhammad Imran: NUST Business School (NBS), National University of Sciences and Technology, Islamabad 44000, Pakistan
Muhammad Salman Habib: Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Amjad Hussain: Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Naveed Ahmed: Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Abdulrahman M. Al-Ahmari: Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11415, Saudi Arabia

Mathematics, 2020, vol. 8, issue 3, 1-29

Abstract: This paper presents a multi-objective, multi-period inventory routing problem in the supply chain of perishable products under uncertain costs. In addition to traditional objectives of cost and greenhouse gas (GHG) emission minimization, a novel objective of priority index maximization has been introduced in the model. The priority index quantifies the qualitative social aspects, such as coordination, trust, behavior, and long-term relationships among the stakeholders. In a multi-echelon supply chain, the performance of distributor/retailer is affected by the performance of supplier/distributor. The priority index measures the relative performance index of each player within the supply chain. The maximization of priority index ensures the achievement of social sustainability in the supply chain. Moreover, to model cost uncertainty, a time series integrated regression fuzzy method is developed. This research comprises of three phases. In the first phase, a mixed-integer multi-objective mathematical model while considering the cost uncertainty has been formulated. In order to determine the parameters for priority index objective function, a two-phase fuzzy inference process is used and the rest of the objectives (cost and GHG) have been modeled mathematically. The second phase involves the development of solution methodology. In this phase, to solve the mathematical model, a modified interactive multi-objective fuzzy programming has been employed that incorporates experts’ preferences for objective satisfaction based on their experiences. Finally, in the third phase, a case study of the supply chain of surgical instruments is presented as an example. The results of the case provide optimal flow of products from suppliers to hospitals and the optimal sequence of the visits of different vehicle types that minimize total cost, GHG emissions, and maximizes the priority index.

Keywords: inventory routing problem; time series predicted uncertain costs; time series integrated regression fuzzy; priority index; fuzzy-inference system (FIS); modified interactive multi-objective fuzzy programming (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/3/382/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/3/382/ (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:8:y:2020:i:3:p:382-:d:330293

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 ().