A biased random-key genetic algorithm for the two-stage capacitated facility location problem

Artigo CientíficoThis paper presents a new metaheuristic approach for the two-stage capacitated facility location problem (TSCFLP), which the objective is to minimize the operation costs of the underlying two-stage transportation system, satisfying demand and capacity constraints. In this problem, a single product must be transported from a set of plants to meet customers demands passing out by intermediate depots. Since this problem is known to be NP-hard, approximated methods become an efficient alternative to solve real-industry problems. As far as we know, the TSCFLP is being solved in most cases by hybrid approaches supported by an exact method, and sometimes a commercial solver is used for this purpose. Bearing this in mind, a BRKGA metaheuristic and a new local search for TSCFLP are proposed. It is the first time that BRKGA had been applied to this problem and the computational results show the competitiveness of the approach developed in terms of quality of the solutions and required computational time when compared with those obtained by state-of-the-art heuristics. The approach proposed can be easily coupled in intelligent systems to help organizations enhance competitiveness by optimally placing facilities in order to minimize operational costs.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Diseño de una cadena de suministro de biocombustible integrando decisiones estratégicas y tácticas

CD-T 662.88 V58; 75 pEl objetivo de esta investigación es el diseño de una cadena de suministro de biocombustible, que integre decisiones de instalaciones e inventario, en busca de la maximización del valor presente neto (VPN) del sistema. Un modelo de Programación Linea Entera Mixta (PLEM) determina la capacidad y ubicación de centros de acopio y biorefinerías, además de los flujos a lo largo de la cadena.Universidad Libre Seccional Pereir

Multi-level Facility Location Problems

We conduct a comprehensive review on multi-level facility location problems which extend several classical facility location problems and can be regarded as a subclass within the well-established field of hierarchical facility location. We first present the main characteristics of these problems and discuss some similarities and differences with related areas. Based on the types of decisions involved in the optimization process, we identify three different categories of multi-level facility location problems. We present overviews of formulations, algorithms and applications, and we trace the historical development of the field

Reliability-constrained design optimisation of extra-large offshore wind turbine support structures

The offshore wind industry has evolved significantly over the last decade, contributing considerably to Europe’s energy mix. For further penetration of this technology, it is essential to reduce its costs to make it competitive with conventional power generation technologies. To this end, optimising the design of components while simultaneously fulfilling design criteria is a crucial requirement for producing more cost-effective strategies. Traditional design optimisation techniques rely on the optimisation of design variables against constraints such as stresses or deformation in the form of limit states and to minimise an objective function such as the total mass of a component. Although this approach leads to more optimal designs, the presence of uncertainties, for instance, in material properties, manufacturing tolerances and environmental loads, requires more systematic consideration of these uncertainties. A combination of optimisation methods with concepts of structural reliability can be a suitable approach if challenges such as the approximation of the load effect concerning global input loads and computational requirements are addressed accordingly. In this study, a reliability-constrained optimisation framework for offshore wind turbine (OWT) support structures is developed, applied, and documented for the first time. First, a parametric finite element analysis (FEA) model of OWT support structures is developed, considering stochastic material properties and environmental loads. The parametric FEA model is then combined with response surface and Monte Carlo (MC) to create an assessment model in the Six Sigma module in ANSYS, which is then further integrated with an optimisation algorithm to develop a fully coupled reliability-constrained optimisation framework. The framework is applied to the NREL 5MW OWT and OC3 sub-structure. Results indicate that the proposed optimisation framework can effectively reduce the mass of OWT support structures meeting target reliability levels focusing on realistic limit states. At the end of the optimisation loop, an LCOE comparison is done to see the effect of mass reduction on the wind turbine cost. The study expanded with a scaling-up approach and investigated the technical feasibility of increasing the system’s power and size in deeper water depth for bottom-fixed support structures. Additionally, parametric equations have been developed to estimate the wind turbine rating and weight considering water depth in the conceptual design stage. Furthermore, the sensitivity analysis was performed on the latest reference support structure of the IEA 15MW turbine to see the effect of water depth between 30m to 60m. The results showed the influences of water depth on the current structural response of the monopile. It revealed that utilising the proposed support structure is not feasible for water-depth above 50m as the analysis did not fulfil design criteria.The offshore wind industry has evolved significantly over the last decade, contributing considerably to Europe’s energy mix. For further penetration of this technology, it is essential to reduce its costs to make it competitive with conventional power generation technologies. To this end, optimising the design of components while simultaneously fulfilling design criteria is a crucial requirement for producing more cost-effective strategies. Traditional design optimisation techniques rely on the optimisation of design variables against constraints such as stresses or deformation in the form of limit states and to minimise an objective function such as the total mass of a component. Although this approach leads to more optimal designs, the presence of uncertainties, for instance, in material properties, manufacturing tolerances and environmental loads, requires more systematic consideration of these uncertainties. A combination of optimisation methods with concepts of structural reliability can be a suitable approach if challenges such as the approximation of the load effect concerning global input loads and computational requirements are addressed accordingly. In this study, a reliability-constrained optimisation framework for offshore wind turbine (OWT) support structures is developed, applied, and documented for the first time. First, a parametric finite element analysis (FEA) model of OWT support structures is developed, considering stochastic material properties and environmental loads. The parametric FEA model is then combined with response surface and Monte Carlo (MC) to create an assessment model in the Six Sigma module in ANSYS, which is then further integrated with an optimisation algorithm to develop a fully coupled reliability-constrained optimisation framework. The framework is applied to the NREL 5MW OWT and OC3 sub-structure. Results indicate that the proposed optimisation framework can effectively reduce the mass of OWT support structures meeting target reliability levels focusing on realistic limit states. At the end of the optimisation loop, an LCOE comparison is done to see the effect of mass reduction on the wind turbine cost. The study expanded with a scaling-up approach and investigated the technical feasibility of increasing the system’s power and size in deeper water depth for bottom-fixed support structures. Additionally, parametric equations have been developed to estimate the wind turbine rating and weight considering water depth in the conceptual design stage. Furthermore, the sensitivity analysis was performed on the latest reference support structure of the IEA 15MW turbine to see the effect of water depth between 30m to 60m. The results showed the influences of water depth on the current structural response of the monopile. It revealed that utilising the proposed support structure is not feasible for water-depth above 50m as the analysis did not fulfil design criteria