Angels against demons: Fight against smuggling in an illicit supply chain with uncertain outcomes and unknown structure

• Published in: Computers & industrial engineering 2023-02, Vol.176, p.109007, Article 109007
• Main Authors: Najafi, Mehdi, Zolfagharinia, Hossein, Asadi, Fatemeh
• Format: Article
• Language: English
• Subjects: Illicit supply chain; Maximum flow; Multi-commodity; Network interdiction; Uncertain outcome
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2023.109007

•We model a maximum flow interdiction problem under uncertainty.•We investigate a multi-commodity and multi-source relationship network.•The problem is formulated as bi-level mixed-integer programming.•A solution method is designed based on the Benders decomposition procedure.•A simulation analysis is conducted to evaluate the impacts of capturing uncertainty. Illegal smuggling is one of the main concerns in many countries due to its significant negative economic and societal impacts. To control the problem, law enforcement officials work to prevent illegal smuggling operations. However, these officials usually encounter a multi-commodity network with an unidentified structure where traffickers trade illicit commodities at different levels. Therefore, they should manage their resources to prioritize identifying the network members, monitoring the identified members, and arresting them to remove their corresponding illegal trades from the network. To manage these operations, this study aims to utilize a mathematical approach, including a formulation and solution method, to manage the available resources in the network and minimize the illicit flows between the origins and destinations. The proposed approach considers real-world conditions, including multi-commodity flows and operations uncertainty. In other words, we investigate a multi-commodity maximum flow network interdiction problem with uncertain outcomes and an unknown network structure. The network under investigation denotes the relationships among criminals who trade illegal commodities in the distribution network. Given the complexity of the problem, this study contributes to the literature by developing a bi-level mixed-integer programming formulation to interdict maximum flow in a multi-commodity, multi-source relationship network considering uncertain outcomes of leader’s attacks. In addition, it develops a solution procedure based on the Benders decomposition algorithm to solve the developed formulation. Finally, to show the practicality of the developed formulation and solution method in real-world problems and to demonstrate their performance, we conduct a simulation analysis to determine how considering uncertainty affects the decisions. Our obtained results reveal that although considering the outcome uncertainty increases the solution time, it also increases the number of arrested criminals by about 29% on average and decreases the volume of trafficking by up to 14%.