Home  << 1 >> 
JerezHanckes, C., & Labarca, I. (2023). Timedomain multiple traces boundary integral formulation for acoustic wave scattering in 2D. Eng. Anal. Bound. Elem., 157, 216–228.
Abstract: We present a novel computational scheme to solve acoustic wave transmission problems over twodimensional composite scatterers, i.e. penetrable obstacles possessing junctions or triple points. The continuous problem is cast as a local multiple traces timedomain boundary integral formulation. For discretization in time and space, we resort to convolution quadrature schemes coupled to a nonconforming spatial spectral discretization based on second kind Chebyshev polynomials displaying fast convergence. Computational experiments confirm convergence of multistep and multistage convolution quadrature for a variety of complex domains.

Lagos, F., Boland, N., & Savelsbergh, M. (2022). Dynamic discretization discovery for solving the Continuous Time Inventory Routing Problem with OutandBack Routes. Comput. Oper. Res., 141, 105686.
Abstract: In time dependent models, the objective is to find the optimal times (continuous) at which activities occur and resources are utilized. These models arise whenever a schedule of activities needs to be constructed. A common approach consists of discretizing the planning time and then restricting the decisions to those time points. However, this approach leads to very large formulations that are intractable in practice. In this work, we study the Continuous Time Inventory Routing Problem with OutandBack Routes (CIRPOB). In this problem, a company manages the inventory of its customers, resupplying a single product from a single facility during a finite time horizon. The product is consumed at a constant rate (product per unit of time) by each customer. The customers have local storage capacity. The goal is to find the minimum cost delivery plan with outandback routes only that ensures that none of the customers run out of product during the planning period. We study the Dynamic Discovery Discretization algorithm (DDD) to solve the CIRPOB by using partially constructed timeexpanded networks. This method iteratively discovers time points needed in the network to find optimal continuous time solutions. We test this method by using randomly generated instances in which we find provable optimal solutions.
Keywords: NETWORK DESIGN; CUT ALGORITHM; DECOMPOSITION; POLICIES

Li, B., Moataz, F. Z., Nisse, N., & Suchan, K. (2018). Minimum size treedecompositions. Discret Appl. Math., 245, 109–127.
Abstract: We study in this paper the problem of computing a treedecomposition of a graph with width at most k and minimum number of bags. More precisely, we focus on the following problem: given a fixed k >= 1, what is the complexity of computing a treedecomposition of width at most k with minimum number of bags in the class of graphs with treewidth at most k? We prove that the problem is NPcomplete for any fixed k >= 4 and polynomial for k <= 2; for k = 3, we show that it is polynomial in the class of trees and 2connected outerplanar graphs. (C) 2017 Elsevier B.V. All rights reserved.
Keywords: Treedecomposition; Treewidth; NPhard

Munoz, F. D., Hobbs, B. F., & Watson, J. P. (2016). New bounding and decomposition approaches for MILP investment problems: Multiarea transmission and generation planning under policy constraints. Eur. J. Oper. Res., 248(3), 888–898.
Abstract: We propose a novel twophase bounding and decomposition approach to compute optimal and nearoptimal solutions to largescale mixedinteger investment planning problems that have to consider a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen's inequality to define a lower bound, which we extend to stochastic programs that use expectedvalue constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improves upon the standard Benders' algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen's inequalitybased approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a subsampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. However, the decomposition phase is required to attain optimality gaps. Use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
Keywords: OR in energy; Stochastic programming; Benders decomposition

Pereira, J., & Vila, M. (2016). A new model for supply chain network design with integrated assembly line balancing decisions. Int. J. Prod. Res., 54(9), 2653–2669.
Abstract: Supply chain network design aims at the integration of the different actors of a supply chain within a single framework in order to optimise the total profit of the system. In this paper, we consider the integration of line balancing issues within the tactical decisions of the supply chain, and we offer a novel model and a solution approach for the problem. The new approach decomposes the problem into multiple line balancing problems and a mixed integer linear model, which is easier to solve than the previously available nonlinear mixed integer formulation. The results show that the new method is able to solve previously studied models within a fraction of the reported running times, and also allows us to solve larger instances than those reported in earlier works. Finally, we also provide some analysis on the influence of the cost structure, the demand and the structure of the assembly process on the final configuration of the assemblies and the distribution network.

PerezQuezada, J. F., Perez, C. A., Brito, C. E., Fuentes, J. P., Gaxiola, A., AguileraRiquelme, D., et al. (2021). Biotic and abiotic drivers of carbon, nitrogen and phosphorus stocks in a temperate rainforest. Forest. Ecol. Manag., 494, 119341.
Abstract: Forest ecosystems are recognized for their large capacity to store carbon (C) in their aboveground and belowground biomass and soil pools. While the distribution of C among ecosystem pools has been extensively studied, less is known about nitrogen (N) and phosphorus (P) pools and how these stocks relate to each other. There is also a need to understand how biotic and abiotic ecosystem properties drive the magnitude and distribution of CNP stocks. We studied a temperate rainforest in southern South America to answer the following questions: 1) how are CNP total stocks distributed among the different ecosystem pools?, 2) how do C:N, C:P and N:P ratios vary among ecosystem pools?, and 3) which are the main biotic and abiotic drivers of CNP stocks? We established 33 circular plots to estimate C, N, and P stocks in different pools (i.e. trees, epiphytes, understory, necromass, leaf litter, and soil) and a set of biotic (e.g., tree density and richness) and abiotic variables (e.g., air temperature, humidity and soil depth). We used structural equation modeling to identify the relative importance of environmental drivers on CNP stocks. We found that total ecosystem stocks (mean +/ SE) were 1062 +/ 58 Mg C ha1, 28.8 +/ 1.5 Mg N ha1, and 347 +/ 12.5 kg P ha1. The soil was the largest ecosystem pool, containing 68%, 92%, and 73% of the total C, N, and P stocks, respectively. Compared to representative temperate forests, the soil of this forest contains the largest concentrations and stocks of C and N. The low P stock and wide soil C:P and N:P ratios suggest that P may be limiting forest productivity. The ecosystem CNP stocks were mainly driven by abiotic properties measured in the study area, however for N stocks, variables such as plant diversity and canopy openness were also relevant. Our results provide evidence about the importance not only of understanding the differences in C, N, and P stocks but also of the factors that drive such differences. This is key to inform conservation policies related to preserving oldgrowth forests in southern South America, which indeed are facing a rapid landuse change process.

RamirezPico, C., Ljubic, I., & Moreno, E. (2023). Benders AdaptiveCuts Method for TwoStage Stochastic Programs. Transp. Sci., Early Access.
Abstract: Benders decomposition is one of the most applied methods to solve twostage stochastic problems (TSSP) with a large number of scenarios. The main idea behind the Benders decomposition is to solve a large problem by replacing the values of the second stage subproblems with individual variables and progressively forcing those variables to reach the optimal value of the subproblems, dynamically inserting additional valid constraints, known as Benders cuts. Most traditional implementations add a cut for each scenario (multicut) or a single cut that includes all scenarios. In this paper, we present a novel Benders adaptivecuts method, where the Benders cuts are aggregated according to a partition of the scenarios, which is dynamically refined using the linear programdual information of the subproblems. This scenario aggregation/disaggregation is based on the Generalized Adaptive Partitioning Method (GAPM), which has been successfully applied to TSSPs. We formalize this hybridization of Benders decomposition and the GAPM by providing sufficient conditions under which an optimal solution of the deterministic equivalent can be obtained in a finite number of iterations. Our new method can be interpreted as a compromise between the Benders singlecuts and multicuts methods, drawing on the advantages of both sides, by rendering the initial iterations faster (as for the singlecuts Benders) and ensuring the overall faster convergence (as for the multicuts Benders). Computational experiments on three TSSPs [the Stochastic Electricity Planning, Stochastic Multi Commodity Flow, and conditional valueatrisk (CVaR) Facility Location] validate these statements, showing that the new method outperforms the other implementations of Benders methods, as well as other standard methods for solving TSSPs, in particular when the number of scenarios is very large. Moreover, our study demonstrates that the method is not only effective for the riskneutral decision makers, but also that it can be used in combination with the riskaverse CVaR objective.
