Responsive Contingency Planning for Supply Chain Disruption Risk Mitigation

Contingent sourcing from a backup resource is an effective risk mitigation strategy under major disruptions. The production volumes and speeds of the backup resource are important protection design considerations, as they affect recovery. The objective of this dissertation is to show that cost-effective protection of existing supply networks from major disruptions result from planning appropriate volume and response speeds of a backup production facility prior to the disruptive event by considering operational aspects such as congestion that may occur at facilities. Contingency strategy are more responsive and disruption recovery periods can be shortened through such prior planning. The dissertation focuses on disruption risk arising from intelligent or pre-meditated attacks on supply facilities. An intelligent attacker has the capability to create worst case loss depending on the protection strategy of a given network. Since the attacker seeks the maximum loss and the designer tries to identify the protection scheme which minimizes this maximum loss, there exists an interdependence between attack and protection decisions. Ignoring this characteristic leads to suboptimal mitigation solutions under such disruptions. We therefore develop a mathematical model which utilizes a game theoretic framework of attack and defense involving nested optimization problems. The model is used to decide optimal selection of backup production volume and the response speeds, the facilities to build such capability within the available budget. The reallocation of demands from a disrupted facility to an undisrupted facility in a contingency strategy leads to congestion of the undisrupted facility, which may result in longer lead times and reduced throughput during disruption periods, thereby limiting the effectiveness of a contingency strategy. In the second part of the dissertation, we therefore analyze congestion effects in responsive contingency planning. The congestion cost function is modeled and integrated into the mathematical model of responsive contingency planning developed in the first part of the dissertation. The main contribution of this dissertation is that a decision tool has been developed to plan protection of an existing supply networks considering backup sourcing through gradual capacity acquisition. The solution methodology involving recursive search tree has been implemented which allows exploring protection solutions under a given budget of protection and multiple combinations of response speeds and production capacities of a backup facility. The results and analysis demonstrate the value of planning for responsive contingency in supply chains subject to risks of major disruptions and provide insights to aid managerial decision making

What it takes to design a supply chain resilient to major disruptions and recurrent interruptions

Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents an optimization model for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose probability of occurrence and magnitude of impact can be mitigated through fortification investments. Numerical results and managerial insights obtained from model implementation are presented. Our analysis focuses on how supply chain design decisions are influenced by facility fortification strategies, a decision maker’s conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints. Finally, examining the performance of the proposed model using a Monte Carlo simulation method provides additional insights and practical implications

Multiple Allocation Hub Interdiction and Protection Problems: Model Formulations and Solution Approaches

In this paper, we present computationally efficient formulations for the multiple allocation hub interdiction and hub protection problems, which are bilevel and trilevel mixed integer linear programs, respectively. In the hub interdiction problem, the aim is to identify a subset of r critical hubs from an existing set of p hubs that when interdicted results in the maximum post-interdiction cost of routing flows. We present two alternate ways of reducing the bilevel hub interdiction model to a single level optimization problem. The first approach uses the dual formulation of the lower level problem. The second approach exploits the structure of the lower level problem to replace it by a set of closest assignment constraints (CACs). We present alternate sets of CACs, study their dominance relationships, and report their computational performances. Further, we propose refinements to CACs that offer computational advantages of an order-of-magnitude compared to the one existing in the literature. Further, our proposed modifications offer structural advantages for Benders decomposition, which lead to substantial computational savings, particularly for large problem instances. Finally, we study and solve large scale instances of the trilevel hub protection problem exactly by utilizing the ideas developed for the hub interdiction problem

Effect of Continuity Rate on Multistage Logistic Network Optimization under Disruption Risk

Modern companies have been facing devastating impacts from unexpected events such as demand uncertainties, natural disasters, and terrorist attacks due to the increasing global supply chain complexity. This paper proposes a multi stage logistic network model under disruption risk. To formulate the problem practically, we consider the effect of continuity rate, which is defined as a percentage of ability of the facility to provide backup allocation to customers in the abnormal situation and affect the investments and operational costs. Then we vary the fixed charge for opening facilities and the operational cost according to the continuity rate. The operational level of the company decreases below the normal condition when disruption occurs. The backup source after the disruption is recovered not only as soon as possible, but also as much as possible. This is a concept of the business continuity plan to reduce the recovery time objective such a continuity rate will affect the investments and operational costs. Through numerical experiments, we have shown the proposed idea is capable of designing a resilient logistic network available for business continuity management/plan

Passenger railway network protection: A model with variable post-disruption demand service

Protecting transportation infrastructures is critical to avoid loss of life and to guard against economic upheaval. This paper addresses the problem of identifying optimal protection plans for passenger rail transportation networks, given a limited budget. We propose a bi-level protection model which extends and refines the model previously introduced by Scaparra et al, (Railway infrastructure security, Springer, New York, 2015). In our extension, we still measure the impact of rail disruptions in terms of the amount of unserved passenger demand. However, our model captures the post-disruption user behaviour in a more accurate way by assuming that passenger demand for rail services after disruptions varies with the extent of the travel delays. To solve this complex bi-level model, we develop a simulated annealing algorithm. The efficiency of the heuristic is tested on a set of randomly generated instances and compared with the one of a more standard exact decomposition algorithm. To illustrate how the modelling approach might be used in practice to inform protection planning decisions, we present a case study based on the London Underground. The case study also highlights the importance of capturing flow demand adjustments in response to increased travel time in a mathematical model

Optimization Approaches To Protect Transportation Infrastructure Against Strategic and Random Disruptions

Past and recent events have proved that critical infrastructure are vulnerable to natural catastrophes, unintentional accidents and terrorist attacks. Protecting these systems is critical to avoid loss of life and to guard against economical upheaval. A systematic approach to plan security investments is paramount to guarantee that limited protection resources are utilized in the most effcient manner. This thesis provides a detailed review of the optimization models that have been introduced in the past to identify vulnerabilities and protection plans for critical infrastructure. The main objective of this thesis is to study new and more realistic models to protect transportation infrastructure such as railway and road systems against man made and natural disruptions. Solution algorithms are devised to effciently solve the complex formulations proposed. Finally, several illustrative case studies are analysed to demonstrate how solving these models can be used to support effcient protection decisions

Passenger railway network protection: A model with variable post-disruption demand service

Protecting transportation infrastructures is critical to avoid loss of life and to guard against economic upheaval. This paper addresses the problem of identifying optimal protection plans for passenger rail transportation networks, given a limited budget. We propose a bi-level protection model which extends and refines the model previously introduced by Scaparra et al, (Railway infrastructure security, Springer, New York, 2015). In our extension, we still measure the impact of rail disruptions in terms of the amount of unserved passenger demand. However, our model captures the post-disruption user behaviour in a more accurate way by assuming that passenger demand for rail services after disruptions varies with the extent of the travel delays. To solve this complex bi-level model, we develop a simulated annealing algorithm. The efficiency of the heuristic is tested on a set of randomly generated instances and compared with the one of a more standard exact decomposition algorithm. To illustrate how the modelling approach might be used in practice to inform protection planning decisions, we present a case study based on the London Underground. The case study also highlights the importance of capturing flow demand adjustments in response to increased travel time in a mathematical model

Optimal Size and Location of Warehouses under Risk of Failure

Nowadays, every company faces challenges that seem to be loaded with a contradiction: how to reduce operations and transportation costs while increasing customer satisfaction levels. Designing a supply chain network is an effective solution to such an issue. Supply chain network design involves making decisions about the number, sizes, and locations of the facilities in a supply chain. The focus of this study is how to choose appropriate warehouse locations and sizes in supply chain network design. The study is divided into two parts. In the second part, the risk of warehouse failure is considered while in the first part, it is not. Three sets of mathematical optimization models for warehouse location and branch assignment were developed. The first set of mathematical optimization models covered the case of warehouse location without risk. Two sets of decision variables were introduced to determine the locations for new warehouses and assign warehouses to branches. The second set of mathematical optimization models covered the warehouse location problem under the risk of warehouse failure. Again, two sets of decision variables were introduced. The first set of decision variables helped in determining the locations for new warehouses, and the second set helped in assigning a primary and a backup warehouse to each branch. The backup warehouse to be used in case of failure of the primary warehouse. The third set of mathematical optimization models covered the case in which some warehouses can be fortified to become totally risk-free. Each branch was either assigned to a primary fortified warehouse only or to a primary warehouse that was not fortified and a secondary fortified warehouse. Fortification model required an additional variables indicating which warehouses to be fortified. Warehouses with multiple capacity levels and multiple part category types were considered, which is a contribution to the topic of warehouse disruption risk. Specialized warehouses were also considered in this dissertation, which is another contribution of this dissertation. Some linearization and relaxation methods were used to help in solving the three models. Further, a solution methodology was presented based on the solution to scenario subproblems that are more easily, i.e., more quickly, solved. This requires an algorithm to determine the scenarios. Each scenario represents the number and sizes of warehouses needed to be built. The scenarios are novel in that they do not specify a subset of warehouses to be opened, but rather they specify the number of warehouses of each size to be opened. The results showed the effectiveness of the proposed solution methodology by application to an example based on a case study of a Canadian company; and a created example based on European cities

The design of effective and robust supply chain networks

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2009-2010Pour faire face aux risques associés aux aléas des opérations normales et aux périls qui menacent les ressources d'un réseau logistique, une méthodologie générique pour le design de réseaux logistiques efficaces et robustes en univers incertain est développée dans cette thèse. Cette méthodologie a pour objectif de proposer une structure de réseau qui assure, de façon durable, la création de valeur pour l'entreprise pour faire face aux aléas et se prémunir contre les risques de ruptures catastrophiques. La méthodologie s'appuie sur le cadre de prise de décision distribué de Schneeweiss et l'approche de modélisation mathématique qui y est associée intègre des éléments de programmation stochastique, d'analyse de risque et de programmation robuste. Trois types d'événements sont définis pour caractériser l'environnement des réseaux logistiques: des événements aléatoires (ex. la demande, les coûts et les taux de changes), des événements hasardeux (ex. les grèves, les discontinuités d'approvisionnement des fournisseurs et les catastrophes naturelles) et des événements profondément incertains (ex. les actes de sabotage, les attentats et les instabilités politiques). La méthodologie considère que l'environnement futur de l'entreprise est anticipé à l'aide de scénarios, générés partiellement par une méthode Monte-Carlo. Cette méthode fait partie de l'approche de solution et permet de générer des replications d'échantillons de petites tailles et de grands échantillons. Elle aide aussi à tenir compte de l'attitude au risque du décideur. L'approche générique de solution du modèle s'appuie sur ces échantillons de scénarios pour générer des designs alternatifs et sur une approche multicritère pour l'évaluation de ces designs. Afin de valider les concepts méthodologiques introduits dans cette thèse, le problème hiérarchique de localisation d'entrepôts et de transport est modélisé comme un programme stochastique avec recours. Premièrement, un modèle incluant une demande aléatoire est utilisé pour valider en partie la modélisation mathématique du problème et étudier, à travers plusieurs anticipations approximatives, la solvabilité du modèle de design. Une approche de solution heuristique est proposée pour ce modèle afin de résoudre des problèmes de taille réelle. Deuxièmement, un modèle incluant les aléas et les périls est utilisé pour valider l'analyse de risque, les stratégies de resilience et l'approche de solution générique. Plusieurs construits mathématiques sont ajoutés au modèle de base afin de refléter différentes stratégies de resilience et proposer un modèle de décision sous risque incluant l'attitude du décideur face aux événements extrêmes. Les nombreuses expérimentations effectuées, avec les données d'un cas réaliste, nous ont permis de tester les concepts proposés dans cette thèse et d'élaborer une méthode de réduction de complexité pour le modèle générique de design sans compromettre la qualité des solutions associées. Les résultats obtenus par ces expérimentations ont pu confirmer la supériorité des designs obtenus en appliquant la méthodologie proposée en termes d'efficacité et de robustesse par rapport à des solutions produites par des approches déterministes ou des modèles simplifiés proposés dans la littérature

Multistage Logistic Network Optimization under Disruption Risk

Getting over disruptions risk has been a challenging issue for many\ud companies under the globalization that will link to potential external source\ud such as demand uncertainties, natural disasters, and terrorist attacks. The\ud disruption is an unexpected event that disturbs normal flows of products and\ud materials within a supply chain. The disruption at one members of supply chain\ud will propagate the offers and finally affect significant impacts on the entire\ud chain. If we look back at the natural disasters in the recent decade, we know\ud the supply chain activities have been put at the edge of high risk that bring\ud catastrophic impact to companies. Not only such disruptions in the supply\ud chain are increasing in frequency, but also the severity of their impact is\ud escalating in terms of costs and losses. Since they will eventually bring a\ud company a partial or complete halt, it is avoidable to consider disruption as a\ud potential threat to supply chain and logistic network. Thereat, we can anticipate\ud the disruption by considering preventive action to ensure the supply chain. If\ud the supply chain takes preventive action against the disruption, such action is\ud viewed as mitigation planning.\ud In this research, we analyzed possible strategies that a company can\ud apply to mitigate and minimize the impacts of supply chain disruptions and\ud design supply chain network in which facilities are unreliable by considering\ud the fact that the facility members may fail. Failure of the facility means that the\ud facility is no longer available to serve its customers. When these facilities\ud ii\ud happen to fail, the concerned organization has to find alternate sources of\ud supply to continue service to the customers, to reroute assignments that were\ud initially intended or to incur large penalties.\ud To cope with the problem, we are interested in a three echelon logistic\ud networks composed of distribution center (DC), relay station (RS) and\ud Customer. Thereat, we consider two kinds of relay station like reliable relay\ud station (RRS) and unreliable relay station (URS). The URS is subject to\ud failures and the reliable relay station (RRS) becomes the hardened ones by\ud having additional capacity or external alternative sourcing strategy. So it is\ud more expensive to establish or operate such facility compared to URS. If the\ud primary facility is disrupted, however, RRS will act as backup facilities to\ud provide supply of product to customers.\ud Under those conditions, we formulate the logistic optimization problem\ud so that the expected total cost associated with disruption probability is\ud minimized under various constraints. It refers to a probabilistic mixed-integer\ud programming problem. Then, this dissertation concerned three main problems.\ud The first problem considers three types of allocation model, i.e., multimulti\ud allocation, multi-single allocation and single-single allocation model.\ud Taking these models, we compared some properties among three allocation\ud models which have different configurations of the network. This is because the\ud configuration is one of the most important and strategic issues in the logistic\ud network design that has long lasted effect. Concern with this issue, we carried\ud out a morphological analysis in order to measure the complexity of the multi\ud stage logistic networks besides the expected cost. Finally, numerical\ud experiment is carried out by applying commercial software to validate the\ud proposed idea.\ud The operational level of the company will decrease below the normal\ud condition when disruption occurs. The backup source after the disruption\ud should be recovered not only as soon as possible, but also as much as possible.\ud This is related to the concept of the business continuity management/plan\ud iii\ud (BCM/P) to reduce the recovery time objective. The second problem considers\ud a robust supply chain network design by considering the effect of continuity\ud rate to cope with the more practical circumstances. That is to say, we assume\ud that URS is not completely halted and RRS will decrease the backup ability\ud depending on the continuity rate of facility. Eventually, the continuity rate is\ud percentage of ability facility to provide backup allocation to customers in\ud abnormal situation and will affect the investment and operational costs. We\ud evaluated the effect of the continuity rate for the foregoing three models.\ud Finally, numerical experiment is carried out to derive some prospects for the\ud future studies.\ud In the real-world situation, we need to concern huge numbers of facility\ud members that make the resulting problem extremely difficult to solve.\ud Accordingly, with increasing problems size, it becomes almost impossible to\ud solve the problem by any currently available software. In the last problem,\ud therefore, we developed an effective hybrid method so that we can solve the\ud problem regardless of the size. The approach is composed of meta-heuristic\ud method like tabu search and graph algorithm. Some bench mark problems are\ud solved to validate the effectiveness