783. Distributioanlly Robust Supplier Selection and Order Allocation under Disruption Risk over a Wasserstein Ambiguity
Invited abstract in session TD-39: Sustainable & Resilient Digital Twins, stream Sustainable & Resilient Systems and Infrastructures.
Tuesday, 14:30-16:00Room: Newlyn LG.01
Authors (first author is the speaker)
| 1. | Xin Li
|
| Warwick Business School, The University of Warwick | |
| 2. | Xuan Vinh Doan
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| Warwick Business School, The University of Warwick | |
| 3. | Juergen Branke
|
| Warwick Business School, University of Warwick |
Abstract
Supply chain resilience has become indispensable for safeguarding operations against disruption risk, presenting decision-makers with the challenge of balancing cost efficiency and effective risk mitigation. We study the problem of resilient supplier selection and order allocation under uncertain disruption impact. Probability distributions of the actual disruption impact are unknown, and only a possibly small set of historical realizations may be available. To handle the distributional uncertainty, we first construct an ambiguity set that encompasses all possible distributions of disruption impact within a 1-Wasserstein distance from the empirical distribution. We then define a two-stage distributionally robust supplier selection and order allocation problem to determine optimal sourcing and contingency decisions among available suppliers under capacity constraints. The objective is to minimize the total cost consisting of the fixed cost related to contracting suppliers plus the worst-case mean-CVaR associated with disruption over all distributions defined in the ambiguity set. We derive an equivalent mixed-integer linear programming (MILP) reformulation that can be implemented and solved using off-the-shelf optimization software. We conduct numerical experiments with real-world procurement data and the experimental results demonstrate that our proposed model yields robust solutions and achieves superior out-of-sample performance over the stochastic approach.
Keywords
- Production and Inventory Systems
- Robust Optimization
- Disaster and Crisis Management
Status: accepted
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