Projecting the flow variables for hub location problems

We consider two formulations for the uncapacitated hub location problem with single assignment (UHL), which use multicommodity flow variables. We project out the flow variables and determine some extreme rays of the projection cones. Then we investigate whether the corresponding inequalities define facets of the UHL polyhedron. We also present two families of facet defining inequalities that dominate some projection inequalities. Finally, we derive a family of valid inequalities that generalizes the facet defining inequalities and that can be separated in polynomial time. © 2004 Wilev Periodicals. Inc

A new formulation and branch-and-cut method for single-allocation hub location problems

A new compact formulation for uncapacitated single-allocation hub location problems with fewer variables than the previous Integer Linear Programming formulations in the literature is introduced. Our formulation works even with costs not based on distances and not satisfying triangle inequality. Moreover, costs can be given in aggregated or disaggregated way. Different families of valid inequalities that strengthen the formulation are developed and a branch-and-cut algorithm based on a relaxed version of the formulation is designed, whose restrictions are inserted in a cut generation procedure together with two sets of valid inequalities. The performance of the proposed methodology is tested on well-known hub location data sets and compared to the most recent and efficient exact algorithms for single-allocation hub location problems. Extensive computational results prove the efficiency of our methodology, that solves large-scale instances in very competitive times

On hub location problems in geographically flexible networks

The authors were partially supported by research groups SEJ-584 and FQM-331 (Junta de Andalucia) and projects MTM2016-74983-C02-01 (Spanish Ministry of Education and Science/FEDER), FEDER-US-1256951, P18-FR-1422, P18-FR-2369 (Junta de Andalucia), CEI-3FQM331 (Andalucia Tech), and NetmeetData (Fundacion BBVA - Big Data 2019). We also would like to acknowledge Elena Fernandez (Universidad de Cadiz) for her useful and detailed comments on previous versions of this manuscript.In this paper, we propose an extension of the uncapacitated hub location problem where the potential positions of the hubs are not fixed in advance. Instead, they are allowed to belong to a region around an initial discrete set of nodes. We give a general framework in which the collection, transportation, and distribution costs are based on norm-based distances and the hub-activation setup costs depend not only on the location of the hub that are opened but also on the size of the region where they are placed. Two alternative mathematical programming formulations are proposed. The first one is a compact formulation while the second one involves a family of constraints of exponential size that we separate efficiently giving rise to a branch-and-cut algorithm. The results of an extensive computational experience are reported showing the advantages of each of the approaches.Junta de Andalucia SEJ-584 FQM-331 FEDER-US-1256951 P18-FR-1422 P18-FR-2369Spanish Government European Commission MTM2016-74983-C02-01Andalucia Tech CEI-3FQM331NetmeetData (Fundacion BBVA - Big Data 2019

Development of Methods for Uncertainty Quantification in CFD Applied to Wind Turbine Wake Prediction

The CFD 2030 vision aims to improve computer simulations of fluid dynamics in fields like aerospace and energy. They focus on managing uncertainties in these simulations. This study presents two methods:1. Intrusive Polynomial Chaos (IPC) Stochastic Solver: This method employs Polynomial Chaos expansion to tackle uncertainties linked to fluid flow simulations. It characterizes parametric uncertainties, studying their nonlinear effects. The solver is tested on various scenarios, showing its promise for reliable Uncertainty Quantification (UQ) analysis in CFD without being overly intrusive or costly.2. Surrogate Based Uncertainty Quantification (SBUQ) using Deep Learning: A novel approach involves constructing a surrogate model using a neural network, capable of predicting wind flow within a wind farm based on single wind turbine data. This model is used to assess uncertainty in wind farm predictions, accounting for parameter and model form uncertainties.These techniques were tested on different scenarios and demonstrated their capability to analyze complex CFD simulations under various uncertainties. They contribute to the potential of enhancing accuracy and efficiency in UQ analysis. The IPC-based stochastic solver integrates efficiently with existing code, while the SBUQ approach utilizes data from individual wind turbine simulations to predict flow patterns in wind farms.Both methods enhance the accuracy of fluid simulations under different uncertainties. This research contributes to more dependable simulations for aerospace, energy, and environmental engineering applications

Release Time Scheduling and Hub Location for Next-Day Delivery

Cataloged from PDF version of article.Inspired by a real-life problem faced by one of the largest ground-based cargo companies of Turkey, the current study introduces a new facet to the hub location literature. The release time scheduling and hub location problem aims to select a specified number of hubs from a fixed set of demand centers, to allocate each demand center to a hub, and to decide on the release times of trucks from each demand center in such a way that the total amount of cargo guaranteed to be delivered to every potential destination by the next day is not below a threshold and the total routing cost is minimized. The paper introduces integer programming models to solve this problem in the special cases when the cargo uniformly arrives to each demand center during the day and the more realistic pattern of when the cargo arrivals exhibit a piecewise linear form. Several classes of valid inequalities are proposed to strengthen the formulations. Extensions with multiple service levels and discrete sets for release times are also discussed. Computational results show the computational viability of the models under realistic scenarios as well as the validity of the proposed problems in answering several interesting questions from the cargo sector’s perspective