Proximity search heuristics for wind farm optimal layout

A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-proposed proximity search paradigm. Computational results on very large scale instances involving up to 20,000 potential turbine sites prove the practical viability of the overall approach

Lower bounds and heuristic algorithms for the ki-partitioning problem

We consider the problem of partitioning a set of positive integers values into a given number of subsets, each having an associated cardinality limit, so that the maximum sum of values in a subset is minimized, and the number of values in each subset does not exceed the corresponding limit. The problem is related to scheduling and bin packing problems. We give combinatorial lower bounds, reduction criteria, constructive heuristics, a scatter search approach, and a lower bound based on column generation. The outcome of extensive computational experiments is presente

A New General-Purpose Algorithm for Mixed-Integer Bilevel Linear Programs

International audienceBilevel optimization problems are very challenging optimization models arising in many important practical contexts, including pricing mechanisms in the energy sector, airline and telecommunication industry, transportation networks, critical infrastructure defense, and machine learning. In this paper, we consider bilevel programs with continuous and discrete variables at both levels, with linear objectives and constraints (continuous upper level variables, if any, must not appear in the lower level problem). We propose a general-purpose branch-and-cut exact solution method based on several new classes of valid inequalities, which also exploits a very effective bilevel-specific preprocessing procedure. An extensive computational study is presented to evaluate the performance of various solution methods on a common testbed of more than 800 instances from the literature and 60 randomly generated instances. Our new algorithm consistently outperforms (often by a large margin) alternative state-of-the-art methods from the literature, including methods exploiting problem-specific information for special instance classes. In particular, it solves to optimality more than 300 previously unsolved instances from the literature. To foster research on this challenging topic, our solver is made publicly available online

Adaptive robust optimization with objective uncertainty

In this work, we study optimization problems where some cost parameters are not known at decision time and the decision flow is modeled as a two-stage process within a robust optimization setting. We address general problems in which all constraints (including those linking the first and the second stages) are defined by convex functions and involve mixed-integer variables, thus extending the existing literature to a much wider class of problems. We show how these problems can be reformulated using Fenchel duality, allowing to derive an enumerative exact algorithm, for which we prove-convergence in a finite number of operations. An implementation of the resulting algorithm, embedding a column generation scheme, is then computationally evaluated on two different problems, using instances that are derived starting from the existing literature. To the best of our knowledge, this is the first approach providing results on the practical solution of this class of problems

Effect of contrasting crop rotation systems on soil chemical and biochemical properties and plant root growth in organic farming: First results

Organic farming is claimed to improve soil fertility. Nonetheless, among organic practices, net C-inputs may largely vary in amount and composition and produce different soil conditions for microbial activity and plant-root system adaptation and development. In this study, we hypothesised that, in the regime of organic agriculture, soil chemical and biochemical properties can substantially differ under contrasting crop rotation systems and produce conditions of soil fertility to which the plant responds through diverse growth and production. The impact of 13 years of Alfalfa-Crop rotation (P-C) and Annual Crop rotation (A-C) was evaluated on the build up of soil organic carbon (SOC), active (light fraction organic matter, LFOM; water soluble organic carbon, WSOC) and humic fraction (fulvic acids carbon, FAC; humic acids carbon, HAC), soil biochemical properties (microbial biomass carbon, MBC; basal respiration, dBR; alkaline phosphatase AmP; arylsulfatase ArS; orto-diphenoloxidase, o-DPO) and the amount of available macro-nutrients (N, P, and S) at two different soil depths (0-10 cm and 10-30 cm) before and after cultivation of wheat. We also studied the response of root morphology, physiology and yield of the plant-root system of wheat. Results showed that the level of soil fertility and plant-root system behaviour substantially differed under the two crop rotation systems investigated here. We observed high efficiency of the P-C soil in the build up of soil organic carbon, as it was 2.9 times higher than that measured in the A-C soil. With the exception of o-DPO, P-C soil always showed a higher level of AmP and ArS activity and an initial lower amount of available P and S. The P-C soil showed higher rootability and promoted thinner roots and higher root density. In the P-C soil conditions, the photosynthesis and yield of durum wheat were also favoured. Finally, cultivation of wheat caused an overall depletion of the accrued fertility of soil, mainly evident in the P-C soil, which maintained a residual higher level of all the chemical and biochemical properties tested

The risk stratification of adverse neonatal outcomes in women with gestational diabetes (STRONG) study

Aims: To assess the risk of adverse neonatal outcomes in women with gestational diabetes (GDM) by identifying subgroups of women at higher risk to recognize the characteristics most associated with an excess of risk. Methods: Observational, retrospective, multicenter study involving consecutive women with GDM. To identify distinct and homogeneous subgroups of women at a higher risk, the RECursive Partitioning and AMalgamation (RECPAM) method was used. Overall, 2736 pregnancies complicated by GDM were analyzed. The main outcome measure was the occurrence of adverse neonatal outcomes in pregnancies complicated by GDM. Results: Among study participants (median age 36.8 years, pre-gestational BMI 24.8 kg/m2), six miscarriages, one neonatal death, but no maternal death was recorded. The occurrence of the cumulative adverse outcome (OR 2.48, 95% CI 1.59–3.87), large for gestational age (OR 3.99, 95% CI 2.40–6.63), fetal malformation (OR 2.66, 95% CI 1.00–7.18), and respiratory distress (OR 4.33, 95% CI 1.33–14.12) was associated with previous macrosomia. Large for gestational age was also associated with obesity (OR 1.46, 95% CI 1.00–2.15). Small for gestational age was associated with first trimester glucose levels (OR 1.96, 95% CI 1.04–3.69). Neonatal hypoglycemia was associated with overweight (OR 1.52, 95% CI 1.02–2.27) and obesity (OR 1.62, 95% CI 1.04–2.51). The RECPAM analysis identified high-risk subgroups mainly characterized by high pre-pregnancy BMI (OR 1.68, 95% CI 1.21–2.33 for obese; OR 1.38 95% CI 1.03–1.87 for overweight). Conclusions: A deep investigation on the factors associated with adverse neonatal outcomes requires a risk stratification. In particular, great attention must be paid to the prevention and treatment of obesity