Abstract
Existing literature in humanitarian aid supply chain management has not examined the impacts of disruption on timely and cost-efficient delivery of perishable products during disasters. This paper aims to contribute to fill this gap from a supply chain network design perspective. The supply chain network under investigation entails suppliers, mobile and fixed warehouses, distribution centers, and customers. A realistic problem is considered in which facilities and routes between them are subject to disruptions and might become inaccessible in the aftermath of disasters. We present a bi-objective robust optimization model that is resilient to disaster scenarios. The proposed model integrates strategic and tactical decisions and aims to minimize the time and cost of delivering products to customers after the occurrence of a disaster, while it considers the possibility of multiple disruptions in facilities and routes among them. A solution approach based on Lagrangian relaxation and ε-constraint is developed to efficiently solve the bi-objective model. To validate the formulation and derive practical insights, we apply the proposed methodology to a real case study of blood supply chains. The resulting analyses focus on: (i) comparing the impacts of disruptions at different facilities and routes, (ii) examining the influence of planning for disruptions on the expected delivery time, (iii) investigating the effects of an increase in budget on the expected delivery time, and (iv) evaluating the performance of the proposed robust model and the solution approach.
Original language | English (US) |
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Pages (from-to) | 125-138 |
Number of pages | 14 |
Journal | International Journal of Production Economics |
Volume | 212 |
DOIs | |
State | Published - Jun 2019 |
Keywords
- Disruption
- Lagrangian relaxation
- Robust optimization
- Supply chain network design
- Uncertainty
ASJC Scopus subject areas
- General Business, Management and Accounting
- Economics and Econometrics
- Management Science and Operations Research
- Industrial and Manufacturing Engineering