Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates

Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

A contribution to supply chain design under uncertainty

Dans le contexte actuel des chaînes logistiques, des processus d'affaires complexes et des partenaires étendus, plusieurs facteurs peuvent augmenter les chances de perturbations dans les chaînes logistiques, telles que les pertes de clients en raison de l'intensification de la concurrence, la pénurie de l'offre en raison de l'incertitude des approvisionnements, la gestion d'un grand nombre de partenaires, les défaillances et les pannes imprévisibles, etc. Prévoir et répondre aux changements qui touchent les chaînes logistiques exigent parfois de composer avec des incertitudes et des informations incomplètes. Chaque entité de la chaîne doit être choisie de façon efficace afin de réduire autant que possible les facteurs de perturbations. Configurer des chaînes logistiques efficientes peut garantir la continuité des activités de la chaîne en dépit de la présence d'événements perturbateurs. L'objectif principal de cette thèse est la conception de chaînes logistiques qui résistent aux perturbations par le biais de modèles de sélection d'acteurs fiables. Les modèles proposés permettent de réduire la vulnérabilité aux perturbations qui peuvent aV, oir un impact sur la continuité des opérations des entités de la chaîne, soient les fournisseurs, les sites de production et les sites de distribution. Le manuscrit de cette thèse s'articule autour de trois principaux chapitres: 1 - Construction d'un modèle multi-objectifs de sélection d'acteurs fiables pour la conception de chaînes logistiques en mesure de résister aux perturbations. 2 - Examen des différents concepts et des types de risques liés aux chaînes logistiques ainsi qu'une présentation d'une approche pour quantifier le risque. 3 - Développement d'un modèle d'optimisation de la fiabilité afin de réduire la vulnérabilité aux perturbations des chaînes logistiques sous l'incertitude de la sollicitation et de l'offre

The state of the art development of AHP (1979-2017): A literature review with a social network analysis

Although many papers describe the evolution of the analytic hierarchy process (AHP), most adopt a subjective approach. This paper examines the pattern of development of the AHP research field using social network analysis and scientometrics, and identifies its intellectual structure. The objectives are: (i) to trace the pattern of development of AHP research; (ii) to identify the patterns of collaboration among authors; (iii) to identify the most important papers underpinning the development of AHP; and (iv) to discover recent areas of interest. We analyse two types of networks: social networks, that is, co-authorship networks, and cognitive mapping or the network of disciplines affected by AHP. Our analyses are based on 8441 papers published between 1979 and 2017, retrieved from the ISI Web of Science database. To provide a longitudinal perspective on the pattern of evolution of AHP, we analyse these two types of networks during the three periods 1979?1990, 1991?2001 and 2002?2017. We provide some basic statistics on AHP journals and researchers, review the main topics and applications of integrated AHPs and provide direction for future research by highlighting some open questions

The state of the art development of AHP (1979-2017): a literature review with a social network analysis

Although many papers describe the evolution of the analytic hierarchy process (AHP), most adopt a subjective approach. This paper examines the pattern of development of the AHP research field using social network analysis and scientometrics, and identifies its intellectual structure. The objectives are: (i) to trace the pattern of development of AHP research; (ii) to identify the patterns of collaboration among authors; (iii) to identify the most important papers underpinning the development of AHP; and (iv) to discover recent areas of interest. We analyse two types of networks: social networks, that is, co-authorship networks, and cognitive mapping or the network of disciplines affected by AHP. Our analyses are based on 8441 papers published between 1979 and 2017, retrieved from the ISI Web of Science database. To provide a longitudinal perspective on the pattern of evolution of AHP, we analyse these two types of networks during the three periods 1979–1990, 1991–2001 and 2002–2017. We provide some basic statistics on AHP journals and researchers, review the main topics and applications of integrated AHPs and provide direction for future research by highlighting some open questions

Modelling project complexity driven risk paths in new product development

Project complexity has been extensively explored in the literature because of its major contribution towards the failure of major projects in terms of cost and time overruns. Researchers have identified important factors that contribute to the project complexity and validated their findings through case studies. Few studies have even focused on developing tools for evaluating the project complexity. However, existing research has not explored an important aspect of linking project complexity to different types of project and supply chain risks. We propose a framework for establishing risk paths across project complexity elements, project and supply chain risks, and resulting consequences. Project complexity elements are the knowns at the commencement stage of a project whereas project and supply chain risks are the uncertainties that might realize within the life cycle of the project. We demonstrate application of our proposed framework through a simple simulation example using Bayesian Belief Network. The method can be an important contribution to the literature and beneficial to the practitioners in terms of introducing a new perspective of investigating causal paths of interacting project complexity elements and risks

Robust optimization method of emergency resource allocation for risk management in inland waterways

This study proposes a robust optimization method for waterborne emergency resource allocation in inland waterways that addresses the uncertainties and mismatches between supply and demand. To accomplish this, we integrate the risk evaluation of maritime with a robust optimization model and employ the Entropy Weighted Method (EWM)-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-Analytic Hierarchy Process (AHP) method to evaluate the risk of various areas. The approach enables exploration of the relationship between maritime risk and emergency resource allocation strategy. The robust optimization method is used to deal with uncertainty and derive the robust counterpart of the proposed model. We establish an emergency resource allocation model that considers both the economy and timeliness of emergency resource allocation. We construct an optimization model and transform it into an easily solvable robust counterpart model. The results demonstrate that the proposed method can adapt to real-world scenarios, and effectively optimize the configuration effect while improving rescue efficiency under reasonable resource allocation. Specifically, the proportion of rescue time saved ranges from 28.52% to 92.60%, and the proportion of total cost saved is 95.82%. Our approach has significant potential to provide a valuable reference for decision-making related to emergency resource allocation in maritime management