A unified and computationally efficient framework for procurement portfolio optimization with option contracts

Efficient procurement decisions are crucial for maintaining stable material flows in competitive supply chains. This paper introduces a unified and computationally efficient framework for the procurement portfolio problem with option contracts (PPP-OC) in a two-echelon supply chain, consisting of multiple suppliers and one buyer. The buyer, facing uncertain demand, must reserve capacity from suppliers who offer a two-part fee structure: option price and strike price. We equivalently transform the PPP-OC into a transportation problem (TP) that can be solved efficiently. We propose three dominance rules among option contracts to filter out invalid options, simplifying the problem. Additionally, we present a binary-search-based (BSB) algorithm, offering significantly lower computational complexity than existing methods. We also address five PPP-OC variants, namely, procurement settings with fixed costs and a reserving quantity window, stockout allowances, spot market integration, multi-channel procurement with supply risks, and block reservations. These variants are solved efficiently using our TP-based framework, demonstrating the flexibility and robustness of our framework across various procurement settings. An extensive numerical study evaluates the efficiency of our proposed solution methods in various procurement scenarios, verifies the dominance rules and identifies active options, and examines the sensitivity of the optimal solution to varying stockout cost, demand variability, and levels of supply risk.

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Publication Details

Journal
European Journal of Operational Research
Published
2025-09-22
DOI
https://doi.org/10.1016/j.ejor.2025.09.003
Primary Topic
Supply Chain and Inventory Management
Type
article
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article

A unified and computationally efficient framework for procurement portfolio optimization with option contracts

Bin Hou, Bo Chen, Jiafu Tang, Xinyu Wang
European Journal of Operational Research
Supply Chain and Inventory Management
article

A unified and computationally efficient framework for procurement portfolio optimization with option contracts

Bin Hou, Bo Chen, Jiafu Tang, Xinyu Wang
article en

Abstract

Efficient procurement decisions are crucial for maintaining stable material flows in competitive supply chains. This paper introduces a unified and computationally efficient framework for the procurement portfolio problem with option contracts (PPP-OC) in a two-echelon supply chain, consisting of multiple suppliers and one buyer. The buyer, facing uncertain demand, must reserve capacity from suppliers who offer a two-part fee structure: option price and strike price. We equivalently transform the PPP-OC into a transportation problem (TP) that can be solved efficiently. We propose three dominance rules among option contracts to filter out invalid options, simplifying the problem. Additionally, we present a binary-search-based (BSB) algorithm, offering significantly lower computational complexity than existing methods. We also address five PPP-OC variants, namely, procurement settings with fixed costs and a reserving quantity window, stockout allowances, spot market integration, multi-channel procurement with supply risks, and block reservations. These variants are solved efficiently using our TP-based framework, demonstrating the flexibility and robustness of our framework across various procurement settings. An extensive numerical study evaluates the efficiency of our proposed solution methods in various procurement scenarios, verifies the dominance rules and identifies active options, and examines the sensitivity of the optimal solution to varying stockout cost, demand variability, and levels of supply risk.

European Journal of Operational ResearchVol. 330(3)
Openalex Percentile: Top 58%
Supply Chain and Inventory Management
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A unified and computationally efficient framework for procurement portfolio optimization with option contracts — Bin Hou, Bo Chen, et al. · European Journal of Operational Research (2025) | TGRS Research Map | TGRS