A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts

This paper addresses a new robust multi-objective multi-period model for supply chain planning under uncertainty considering quantity discounts. The proposed model maximizes the current profit of the distributor by making a balance between the total costs of the supply chain and the distributor comp...

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Bibliographic Details
Published in:Journal of industrial and production engineering Vol. 35; no. 4; pp. 214 - 228
Main Authors: Rahimi, Erfan, Paydar, Mohammad Mahdi, Mahdavi, Iraj, Jouzdani, Javid, Arabsheybani, Amir
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis Ltd 19.05.2018
Subjects:
Commodities
Discounts
Economic models
Food processing industry
Integer programming
Mathematical programming
Multiple objective analysis
Optimization models
Robustness
Supply chains
Uncertainty
Variation
ISSN:2168-1015, 2168-1023
Online Access:Get full text
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Summary:This paper addresses a new robust multi-objective multi-period model for supply chain planning under uncertainty considering quantity discounts. The proposed model maximizes the current profit of the distributor by making a balance between the total costs of the supply chain and the distributor company's revenues of selling products and also maximizes the company's expected profit by introducing brands and taking the risk of loss on it. Considering uncertainty in the purchasing cost, selling fees, and demand fluctuations, the new robust multi-objective mixed-integer programming model is solved as a single-objective mixed-integer programming model by utilizing the LP-metrics method. By settling regulatory penalty parameters and considering different economic scenarios, the robustness and effectiveness of the developed model are verified with the data from BEH PAKHSH Company, a commodities distributor in Iran. The outcomes show that the proposed model is a promising approach to run an efficient supply chain.
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ISSN:2168-1015
2168-1023
DOI:10.1080/21681015.2018.1441195