A simulation–optimization approach for a multi-period, multi-objective supply chain with demand uncertainty and an option contract. (July 2018)

• Record Type: Journal Article
• Authors: Heidary, M Hajian; Aghaie, A; Jalalimanesh, A
• Other Names: Yao Baozhen guest-editor.
• Is Part Of: Simulation. Volume 94:Number 7(2018)
• Page Start: 649
• Page End: 662
• Publication Date: 2018-07
• Subjects: Supply chain management -- simulation–optimization -- risk analysis -- newsvendor problem; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549718761588
• Languages: English
• ISSNs: 0037-5497

Abstract

One of the main challenges in global procurement problems is the uncertainty in the demand and supply sides of supply chains. Besides, decision making in the stochastic supply chains is a complex problem. A powerful technique for decision analysis in complex stochastic problems is simulation. In this paper we propose a simulation-based optimization approach to solve a bi-objective (profit and service level) supply chain with uncertain customer demands and disruption events in the suppliers. The basic assumptions used in this paper are adopted from the multi-period newsvendor problem. In addition, based on the risk attitude of the buyers (retailers), to cope with the uncertainties, they can sign an option contract, reserving additional capacity in the secondary suppliers. Hence, a simulation approach is used to model the behavior (risk attitude) of the buyers. Indeed, because of the demand uncertainty, at the beginning of each contract period, buyers should decide on the amount of ordering from the primary suppliers. The risk attitude of the retailer (as a spectrum) is defined based on the amount of ordering from the primary supplier. Also, we use the Non-dominated Sorting Genetic Algorithm to optimize the bi-objective model. Finally, a numerical example has been solved with the proposed algorithm and the results are reported. The results showed that if the profit is more important than service level, the risk sensitive retailer prefers to show more risk averse behavior.