Pricing supply chain option contracts: a bilevel programming approach

Abir Trabelsi, Hiroaki Matsukawa

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Purpose: This paper considers an option contract in a two-stage supplier-retailer supply chain (SC) when market demand is stochastic. The problem is a Stackelberg game with the supplier as a leader. This research assumes demand information sharing. The purpose of this study is to determine the optimal pricing strategy of the supplier along with the optimal order strategy of the retailer in three option contract cases. Design/methodology/approach: The paper model the option contract pricing problem as a bilevel problem. The problem is then solved using bilevel programing methods. After computing, the generated outcomes are compared to a benchmark (wholesale price contract) to evaluate the contract. Findings: The results reveal that only one of the contract cases can arbitrarily allocate the SC profit. In both other cases, the Stackelberg supplier manages to earn the total SC profit. Further analysis of the first contract, show that from the supplier’s perspective, the first stage forecast inaccuracy is beneficial, whereas the demand uncertainty in the second stage is detrimental. This contracting strategy guarantees both players better outcomes compared to the wholesale price contract. Originality/value: To the best of the authors’ knowledge, this research is the first that links the option contract literature to the bilevel programing literature. It also the first to solve the pricing problem of the commitment option contract with demand update where the retailer exercises the option before knowing the exact demand.

Original languageEnglish
Pages (from-to)1567-1589
Number of pages23
JournalJournal of Modelling in Management
Issue number4
Publication statusPublished - 2020 Nov 13


  • Decision-making
  • Game theory
  • Modeling
  • Optimization

ASJC Scopus subject areas

  • General Decision Sciences
  • Strategy and Management
  • Management Science and Operations Research


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