A two-stage method for the capacitated multi-facility location-allocation problem

This is the author accepted manuscript. The final version is available from Inderscience via the DOI in this recordThis paper examines the capacitated planar multi-facility location-allocation problem, where the number of facilities to be located is specified and each of which has a capacity constraint. A two-stage method is put forward to deal with the problem where in the first stage a technique that discretises continuous space into discrete cells is used to generate a relatively good initial facility configurations. In stage 2, a variable neighbourhood search (VNS) is implemented to improve the quality of solution obtained by the previous stage. The performance of the proposed method is evaluated using benchmark datasets from the literature. The numerical experiments show that the proposed method yields competitive results when compared to the best known results from the literature. In addition, some future research avenues are also suggested

FACILITY LOCATION MODEL WITH INVENTORY TRANSPORTATION AND MANAGEMENT COSTS

This work is focused on the integration of the standard EOQ (Economic Order Quantity) model within the facility location decision model. This is proposed to extend on the facility location task which is usually performed based on just the overall demand of the customer locations to be served. If the inventory costs are considered within the demand supply process, these may affect the overall transportation costs as these are not linearly dependent of the demand. As such, the extended model considers, besides the distances, performance and capacity of the vehicles, the order quantities and the period in which they should be fulfilled. This model was tested with a reference instance of 200 suppliers and one distribution centre. The distances were estimated by considering the geographical locations of all elements in the network and the spherical model of the Earth’s surface to obtain the metric in kilometres. As analysed, by considering the inventory costs within the facility location model, it can lead to refine the location to obtain long-term savings in transportation

Robust vulnerability analysis of nuclear facilities subject to external hazards

Natural hazards have the potential to trigger complex chains of events in technological installations leading to disastrous effects for the surrounding population and environment. The threat of climate change of worsening extreme weather events exacerbates the need for new models and novel methodologies able to capture the complexity of the natural-technological interaction in intuitive frameworks suitable for an interdisciplinary field such as that of risk analysis. This study proposes a novel approach for the quantification of risk exposure of nuclear facilities subject to extreme natural events. A Bayesian Network model, initially developed for the quantification of the risk of exposure from spent nuclear material stored in facilities subject to flooding hazards, is adapted and enhanced to include in the analysis the quantification of the uncertainty affecting the output due to the imprecision of data available and the aleatory nature of the variables involved. The model is applied to the analysis of the nuclear power station of Sizewell B in East Anglia (UK), through the use of a novel computational tool. The network proposed models the direct effect of extreme weather conditions on the facility along several time scenarios considering climate change predictions as well as the indirect effects of external hazards on the internal subsystems and the occurrence of human error. The main novelty of the study consists of the fully computational integration of Bayesian Networks with advanced Structural Reliability Methods, which allows to adequately represent both aleatory and epistemic aspects of the uncertainty affecting the input through the use of probabilistic models, intervals, imprecise random variables as well as probability bounds. The uncertainty affecting the output is quantified in order to attest the significance of the results and provide a complete and effective tool for risk-informed decision making

A critical review of the approaches to optimization problems under uncertainty

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2001.Thesis (Master's) -- Bilkent University, 2001.Includes bibliographical references leaves 58-72.In this study, the issue of uncertainty in optimization problems is studied. First of all, the meaning and sources of uncertainty are explained and then possible ways of its representation are analyzed. About the modelling process, different approaches as sensitivity analysis, parametric programming, robust optimization, stochastic programming, fuzzy programming, multiobjective programming and imprecise optimization are presented with advantages and disadvantages from different perspectives. Some extensions of the concepts of imprecise optimization are also presented.Gürtuna, FilizM.S

Deformation and Breakup of Finite-sized Bubbles in Intense Turbulence

From rain droplets in clouds to entrained gas bubbles in oceans, the majority of fluid mechanics problems in nature and industry are turbulent and consist of multiple phases. In such flows, bubbles and droplets experience complex deformation. Though this deformation occurs at small-scale interfaces, it plays important roles in many large-scale processes e.g. the overall heat and mass transfer in two-phase energy systems. To understand the fundamental physics behind the interaction between turbulence and deformable bubbles, simultaneous 3D measurements of both phases are essential. However, obtaining such measurements is a very challenging task. To address this problem, a unique vertical water tunnel (V-ONSET) capable of generating energetic turbulence is designed. V-ONSET is equipped with six high-speed cameras uniformly distributed around its test section to obtain high-resolution images of both bubbles and the turbulent carrier phase simultaneously. To reconstruct the 3D shapes of bubbles, a new algorithm addressing the limited-angle reconstruction problem by using the physical constraint of minimum surface energy is developed. Moreover, to quantify turbulence, tracer particles in the surrounding flow are tracked with an in-house OpenLPT code. Leveraging such unique simultaneous measurements of bubbles and their surrounding turbulent flow, we investigate the mechanisms in turbulence responsible for the deformation and breakup of bubbles. We identify and evaluate two key mechanisms namely, the coarse-grained turbulent strains and the slip velocity between the two phases. Interestingly, two Weber numbers based on these two mechanisms show that in strong turbulence, the rather ignored mechanism of the slip velocity has a comparable magnitude to the other mechanism of turbulent strains. The distributions of these two Weber numbers are modeled based on turbulent flow characteristics. This also helps to estimate bubble breakup probability in turbulence. Furthermore, we investigate the orientation dynamics of bubbles with respect to the aforementioned deformation mechanisms. It elucidates that bubbles exhibit the strongest alignment with the slip velocity direction indicating the dominant role played by the compression induced by the slip velocity. Finally, a Lagrangian model including both deformation mechanisms is proposed to predict bubble deformation and orientation in turbulence

Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches

Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment