Mathematical Model and Stochastic Multi-Criteria Acceptability Analysis for Facility Location Problem

This paper studies a real-life public sector facility location problem. The problem fundamentally originated from the idea of downsizing the number of service centres. However, opening of new facilities is also considered in case the current facilities fail to fulfil general management demands. Two operation research methodologies are used to solve the problem and the obtained results are compared. First, a mathematical programming model is introduced to determine where the new facilities will be located, and which districts get service from which facilities, as if there were currently no existing facilities. Second, the Stochastic Multi-criteria Acceptability Analysis-TRI (SMAA-TRI) method is used to select the best suitable places for service centres among the existing facilities. It is noted that the application of mathematical programming model and SMAA-TRI integration approach on facility location problem is the first study in literature. Compression of outcomes shows that mixed integer linear programming (MILP) model tries to open facilities in districts which are favoured by SMAA-TRI solution.</span

Une méthode de tri multicritère multi-périodes pour la sélection de projet en contexte d'incertitude

RÉSUMÉ: Dans les dernières années, le gouvernement du Québec a souligné l'importance de la prise de décision dans un contexte de développement durable et de lutte contre les changements climatiques. L'évaluation des projets dans ce contexte devrait prendre en considération l'équilibre entre les critères économiques, sociaux et environnementaux à court, moyen et long terme. De plus, ces évaluations peuvent être imprécises et tâchées d'incertitude. Les problèmes de décision dans ce contexte sont complexes et caractérisés par les trois aspects suivants, à savoir l'aspect multicritère, l'aspect temporel et l'incertitude. Or, la plupart des méthodes multicritères sont statiques et seules quelques rares méthodes traitent l'aspect temporel des évaluations. En effet, des recherches récentes ont développé des méthodes multicritères multi-périodes de rangement mais au meilleur de notre connaissance, aucune méthode de tri multicritère multi-périodes ne fut développée à date. L'objectif de ce mémoire est de proposer une méthode de tri multicritère multi-périodes dans un contexte d'incertitude pour l'évaluation de la durabilité des projets. La méthode proposée est constituée de deux phases d'agrégation multicritère et d'agrégation multi-périodes. La première phase consiste à conduire les simulations Monte Carlo et à appliquer la méthode SMAA-Tri pour affecter à chaque période le projet à une des catégories prédéfinies. Ensuite, la phase d'agrégation multi-périodes propose d'agréger les résultats obtenus dans chaque période pour arriver à une affectation à la fois multicritère et multi-périodes. La méthode proposée a été appliquée dans le contexte d'aménagement forestier durable. Un projet d'aménagement spécifique qui consiste à implanter un plan de protection spécifique pour l'habitat du caribou a été trié selon un ensemble de critères évalués sur l'horizon de régénération de la forêt de 150 ans. L'incertitude a été simulée par 10000 simulations Monte Carlo à chacune des 30 périodes. Les résultats de cette application démontrent que la méthode proposée permet de généraliser la méthode SMAA Tri au contexte multi-périodes et aboutit à des résultats intéressants. -- Mot(s) clé(s) en français : Sélection de projet, Méthodes de tri multicritère, évaluations multi-périodes, Monte Carlo, incertitude, développement durable. -- ABSTRACT: In the last years, the government of Quebec emphasized sustainable and robust decision making in the context of climate change. Projects evaluation in this context must take into consideration the balance between economic, social and environmental criteria, over the short, medium and long term. Furthermore, decision criteria may be imprecise or uncertain. Decision-making problems in this context are complex and characterized by multi-criteria, temporal and uncertainty aspects. Yet, the majority of the multi-criteria methods are static and only few methods deal with temporal evaluations. In fact, recent studies proposed multi-criteria multi-period ranking methods but to the best of our knowledge, there is no multi-criteria multi-period sorting method proposed yet. The general objective of this research is to propose a multi-criteria multi-period sorting method in the context of uncertainty to be used for sustainability evaluations of projects. The proposed method is composed of two phases, the multi-criteria aggregation phase, and the multi-period aggregation phase. The aggregation phase consists of conducting the Monte-Carlo Simulations and applying the SMAA-TRI method at each period in order to sort the project in one of the predefined categories. Then, the multi-period aggregation proposes to aggregate the results obtained at each period in order to get a global sorting result. The proposed method is applied in the context of sustainable forest management. A particular project of forest management, that aims to implement a specific protection plan for the caribou habitat, is sorted according to a set of criteria evaluated over the regeneration forest horizon of 150 years. Uncertainty has been simulated with 10 000 Monte-Carlo simulations over 30 periods. The results of this application show that the proposed method generalizes the SMAA-TRI method to the multi-period context and provides interesting results. -- Mot(s) clé(s) en anglais : Project selection, multi-criteria sorting methods, multi-period evaluations, Monte Carlo, uncertainty, sustainable development

Advanced Quantitative Risk Assessment of Offshore Gas Pipeline Systems

This research has reviewed the current status of offshore and marine safety. The major problems identified in the research are associated with risk modelling under circumstances where the lack of data or high level of uncertainty exists. This PhD research adopts an object-oriented approach, a natural and straightforward mechanism of organising information of the real world systems, to represent the Offshore Gas Supply Systems (OGSSs) at both the component and system levels. Then based on the object-oriented approach, frameworks of aggregative risk assessment and fault tree analysis are developed. Aggregative risk assessment is to evaluate the risk levels of components, subsystems, and the overall OGSS. Fault trees are then used to represent the cause-effect relationships for a specific risk in the system. Use of these two assessment frameworks can help decision makers to obtain comprehensive view of risks in the OGSS. In order to quantitatively evaluate the framework of aggregative risk, this thesis uses a fuzzy aggregative risk assessment method to determine the risk levels associated with components, subsystems, and the overall OGSS. The fuzzy aggregative risk assessment method is tailored to quantify the risk levels of components, subsystems, and the OGSS. The proposed method is able to identify the most critical subsystem in the OGSS. As soon as, the most critical subsystem is identified, Fuzzy Fault Tree Analysis (FFTA) is employed to quantitatively evaluate the cause-effect relationships for specific undesired event. These results can help risk analysts to select Risk Control Options (RCOs) for mitigating risks in an OGSS. It is not financially possible to employ all the selected RCOs. Therefore, it is necessary to rank and select the best RCO. A decision making method using the Fuzzy TOPSIS (FTOPSIS) is proposed to demonstrate the selection of the best RCOs to control the existing risks in the system. The developed models and frameworks can be integrated to formulate a platform which enables to facilitate risk assessment and safety management of OGSSs without jeopardising the efficiency of OGSSs operations in various situations where traditional risk assessment and safety management techniques cannot be effectively applied

A novel engineering framework for risk assessment of Mobile Offshore Drilling Units

Natural oil and gas has become one of mankind’s most fundamental resources. Hence, the performance of mobile offshore drilling units (MODUs) under various conditions has received considerable attention. MODUs are designed, constructed, operated, and managed for harsh geographical environments, thus they are unavoidably exposed to a wide range of uncertain threats and hazards. Ensuring the operational safety of an MODU’s system is often a complex problem. The system faces hazards from many different sources which dynamically threaten its integrity and can cause catastrophic consequences at time of failure. The main purpose of this thesis is to propose a methodology to improve the current procedures used in the risk assessment of MODUs. The aim is to prevent a critical event from occurring during drilling rather than on measures that mitigate the consequences once the undesirable event has occurred. A conceptual framework has been developed in this thesis to identify a range of hazards associated with normal operational activities and rank them in order to reduce the risks of the MODU. The proposed methodology provides a rational and systematic approach to an MODU’s risk assessment; a comprehensive model is suggested to take into consideration different influences of each hazard group (HG) of an offshore system. The Fuzzy- analytic hierarchy process (AHP) is used to determine the weights of each HG. Fault tree analysis (FTA) is used to identify basic causes and their logical relationships leading to the undesired events and to calculate the probability of occurrence of each undesirable event in an MODU system. The BBN technique is used to express the causal relationships between variables in order to predict and update the occurrence probability of each undesirable event when any new evidence becomes available. Finally, an integrated Fuzzy multiple criteria decision making (MCDM) model based on the Fuzzy-AHP and a Fuzzy techniques for order preference by similarity to an ideal solution (TOPSIS) is developed to offer decision support that can help the Decision maker to set priorities for controlling the risk and improving the MODU’s safety level. All the developed models have been tested and demonstrated with case studies. An MODU’s drilling failure due to its operational scenario has been investigated and focus has been on the mud circulation system including the blowout preventer (BOP)