STRATEGIC DECISION MAKING IN SUPPLY CHAINS UNDER RISK OF DISRUPTIONS

Ph.DDOCTOR OF PHILOSOPH

The multilevel critical node problem : theoretical intractability and a curriculum learning approach

Évaluer la vulnérabilité des réseaux est un enjeu de plus en plus critique. Dans ce mémoire, nous nous penchons sur une approche étudiant la défense d’infrastructures stratégiques contre des attaques malveillantes au travers de problèmes d'optimisations multiniveaux. Plus particulièrement, nous analysons un jeu séquentiel en trois étapes appelé le « Multilevel Critical Node problem » (MCN). Ce jeu voit deux joueurs s'opposer sur un graphe: un attaquant et un défenseur. Le défenseur commence par empêcher préventivement que certains nœuds soient attaqués durant une phase de vaccination. Ensuite, l’attaquant infecte un sous ensemble des nœuds non vaccinés. Finalement, le défenseur réagit avec une stratégie de protection. Dans ce mémoire, nous fournissons les premiers résultats de complexité pour MCN ainsi que ceux de ses sous-jeux. De plus, en considérant les différents cas de graphes unitaires, pondérés ou orientés, nous clarifions la manière dont la complexité de ces problèmes varie. Nos résultats contribuent à élargir les familles de problèmes connus pour être complets pour les classes NP, $\Sigma_2^p$ et $\Sigma_3^p$. Motivés par l’insolubilité intrinsèque de MCN, nous concevons ensuite une heuristique efficace pour le jeu. Nous nous appuyons sur les approches récentes cherchant à apprendre des heuristiques pour des problèmes d’optimisation combinatoire en utilisant l’apprentissage par renforcement et les réseaux de neurones graphiques. Contrairement aux précédents travaux, nous nous intéressons aux situations dans lesquelles de multiples joueurs prennent des décisions de manière séquentielle. En les inscrivant au sein du formalisme d’apprentissage multiagent, nous concevons un algorithme apprenant à résoudre des problèmes d’optimisation combinatoire multiniveaux budgétés opposant deux joueurs dans un jeu à somme nulle sur un graphe. Notre méthode est basée sur un simple curriculum : si un agent sait estimer la valeur d’une instance du problème ayant un budget au plus B, alors résoudre une instance avec budget B+1 peut être fait en temps polynomial quelque soit la direction d’optimisation en regardant la valeur de tous les prochains états possibles. Ainsi, dans une approche ascendante, nous entraînons notre agent sur des jeux de données d’instances résolues heuristiquement avec des budgets de plus en plus grands. Nous rapportons des résultats quasi optimaux sur des graphes de tailles au plus 100 et un temps de résolution divisé par 185 en moyenne comparé au meilleur solutionneur exact pour le MCN.Evaluating the vulnerability of networks is a problem which has gain momentum in recent decades. In this work, we focus on a Multilevel Programming approach to study the defense of critical infrastructures against malicious attacks. We analyze a three-stage sequential game played in a graph called the Multilevel Critical Node problem (MCN). This game sees two players competing with each other: a defender and an attacker. The defender starts by preventively interdicting nodes from being attacked during what is called a vaccination phase. Then, the attacker infects a subset of non-vaccinated nodes and, finally, the defender reacts with a protection strategy. We provide the first computational complexity results associated with MCN and its subgames. Moreover, by considering unitary, weighted, undirected and directed graphs, we clarify how the theoretical tractability or intractability of those problems vary. Our findings contribute with new NP-complete, $\Sigma_2^p$-complete and $\Sigma_3^p$-complete problems. Motivated by the intrinsic intractability of the MCN, we then design efficient heuristics for the game by building upon the recent approaches seeking to learn heuristics for combinatorial optimization problems through graph neural networks and reinforcement learning. But contrary to previous work, we tackle situations with multiple players taking decisions sequentially. By framing them in a multi-agent reinforcement learning setting, we devise a value-based method to learn to solve multilevel budgeted combinatorial problems involving two players in a zero-sum game over a graph. Our framework is based on a simple curriculum: if an agent knows how to estimate the value of instances with budgets up to B, then solving instances with budget B+1 can be done in polynomial time regardless of the direction of the optimization by checking the value of every possible afterstate. Thus, in a bottom-up approach, we generate datasets of heuristically solved instances with increasingly larger budgets to train our agent. We report results close to optimality on graphs up to 100 nodes and a 185 x speedup on average compared to the quickest exact solver known for the MCN

Terrorism affected regions : the impact of different supply chain risk management strategies on financial performance

Purpose: Current geo-political events, such as terrorism and climatologic adversities, have highlighted the potential risks to supply chains (SCs), and their disastrous financial impacts on supply chains. Within supply chains, risk management plays a major role in successfully managing business processes in a proactive manner and ensuring the business continuity and financial performance (FP). The purpose of this study is to explore the supply chain risks and strategies in a terrorism-affected region (TAR), and to examine supply chain risk management (SCRM) strategies and their impacts on FP, including the war on terror (WoT) and its impacts on the local logistics industry. In addition, this study investigates the knowledge gaps in the published research on terrorism-related risk in supply chains, and develops a framework of strategies and effective decision-making to enable practitioners to address terrorism-related risks for SCRM.Methodology: The study initially adopts a novel combination of triangulated methods comprising a systematic literature review, text mining, and network analysis. Additionally, risk identification, risk analysis and strategies scrutiny are conducted by using semi-structured interviews and Qualitative Content Analysis in a TAR. A model of strategies was developed from a review of existing studies and interviews. The model is empirically tested with survey data of 80 firms using fuzzy-set Qualitative Comparative Analysis (fsQCA).Findings: This study reveals a number of key themes in the field of SCRM linked with terrorism. It identifies relevant mitigation strategies and practices for effective strategic decision-making. This subsequently leads to development of a strategic framework, consisting of strategies and effective-decision making practices to address terrorism-related risks that affect SCRM. It also identifies key the knowledge gaps in the literature and explores the main contributions by disciplines (e.g., business schools, engineering, and maritime institutions) and countries.Further, it identifies the SC risks in a TAR, which consist of value streams: disruption risks, operational risks and financial risks. Among these, the emerging risks emcompass terrorist groups’ demand for protection money, smog, paedophilia and the use of containers to block protesters. To mitigate these risks, firms frequently implemented the following strategies: information sharing, SC coordination, risk sharing, SC finance, SC security and facilitation payment. Five strategies out of the six (except facilitation payment) are able to lead to FP, confirmed quantitatively as well. There are various equifinal configurations of SCRM strategies leading to FP. In addition, information sharing acts as a moderator in the relationship between SC security and FP. SC coordination has a mediating role in the relationship between information sharing and SC security capabilities and FP.Research limitations/Contribution: The sample size a limitation of the study, meaning that the findings should be generalized with caution. The most valuable implications is the identification of configurations of strategies that can help managers and policymakers in implementing those findings.Originality/value: No empirical study was found in the SCRM literature that specifically investigates the relationships between the identified strategies and FP with fsQCA, in particular in a TAR context; this study thus fills an important gap in the SCRM literature and contributes empirically