Abstract
Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples. Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology. Most of the modern integrative clustering approaches rely on independent analysis of each dataset and consensus clustering, probabilistic or statistical modeling, while flexible distance-based integrative clustering techniques are sparsely covered. We propose two distance-based integrative clustering frameworks based on bi-level and bi-objective extensions of the p-median problem. A hybrid branch-and-cut method is developed to find global optimal solutions to the bi-level p-median model. As to the bi-objective problem, an epsilon-constraint algorithm is proposed to generate an approximation to the Pareto optimal set. Every solution found by any of the frameworks corresponds to an integrative clustering. We present an application of our approaches to integrative analysis of NCI-60 human tumor cell lines characterized by gene expression and drug activity profiles. We demonstrate that the proposed mathematical optimization-based approaches outperform some state-of-the-art and traditional distance-based integrative and non-integrative clustering techniques.

Abstract
Pricing and capacity decisions in car rental companies are characterized by high flexibility and interdependence. When planning a selling season, tackling these two types of decisions in an integrated way has a significant impact. This paper tackles the integration of capacity and pricing problems for car rental companies. These problems include decisions on fleet size and mix, acquisitions and removals, fleet deployment and repositioning, as well as pricing strategies for the different rental requests. A novel mathematical model is proposed, which considers the specific dynamics of rentals on the relationship between inventory and pricing as well as realistic requirements from the flexible car rental business, such as upgrades. Moreover, a solution procedure that is able to solve real-sized instances within a reasonable time frame is developed. The solution procedure is a matheuristic based on the decomposition of the model, guided by a biased random-key genetic algorithm (BRKGA) boosted by heuristically generated initial solutions. The positive impact on profit, of integrating capacity and pricing decisions versus a hierarchical/sequential approach, is validated.

Abstract
In mature countries manufacturing is one of the most significant sources of economic development and growth. In those countries, manufacturing transformation systems will be grounded on seamless collaborative environments and will have a high degree of flexibility in production, in terms of product needs (specifications, quality, design), volume, timing, resource efficiency and cost, being able to adapt to customer needs and make use of the entire network chain for value creation. Future transformations systems will be massively collaborative and will be enabled by a network-centric approach, making use of multidimensional data analytics, driven by advanced ICT and the latest available proven manufacturing technologies.

Abstract
The growing economic importance of the biomass-for-bioenergy in Europe motivates research on biomass supply chain design and planning. The temporally and geographically fragmented availability of woody biomass makes it particularly relevant to find cost-effective solutions for biomass production, storage and transportation up to the consumption facility. This paper addresses tactical decisions related with optimal allocation of wood chips from forest residues at forest sites to terminals and power plants. The emphasis is on a "hot-system" with synchronized chipping and chips transportation at the roadside. Thus, decisions related with the assignment of chippers to forest sites are also considered. We extend existing studies by considering the impact of the wood chips energy content variation in the logistics planning. This is a key issue in biomass-for-bioenergy supply chains. The higher the moisture content of wood chips, the lower its net caloric value and therefore, a larger amount of chips is needed to meet the contracted demand. We propose a Mixed Integer Programming (MIP) model to solve this problem to optimality. Results of applying the model in a biomass supply chain case in Finland are presented. Results suggest that a 20% improvement in the supplier profit can be obtained with the proposed approach when compared with a baseline situation that relies on empirical estimates for a fixed and known moisture content in the end of an obliged storage age.

Abstract
The load balance aspect of the Container Loading Problem (CLP) has been handled in an simplified way in the literature. Either load balance has been treated as a soft constraint or the geometrical centre of the container has been assumed to be the ideal location for the centre of gravity of the cargo, or both, which does not meet regulatory directives and transportation legislation. In this paper, we treat load balance as a 'hard constraint and adopt vehicle specific diagrams that define the feasibility domain for the location of the centre of gravity of the cargo, according to the vehicle specific technical characteristics, thus fulfilling and complying with real-world regulations and legislation. We propose a multi-population biased random-key genetic algorithm (BRKGA), with a new fitness function that takes static stability and load balance into account. Extensive computational experiments were performed with different variants of the proposed approach. Also solutions taken from the literature were evaluated in terms of load balance. The computational results show that it is possible to obtain stable and load balanced solutions without compromising the performance in terms of container volume utilization, and demonstrate also the advantage in incorporating load balance in the packing generation algorithm.

Abstract
Solving nesting problems or irregular strip packing problems is to position polygons on a fixed width and unlimited length strip, obeying polygon integrity containment constraints and non-overlapping constraints, in order to minimize the used length of the strip. To ensure non-overlapping, we use separation lines, i.e., straight lines that separate polygons. We present a nonlinear programming model that considers free rotations of the polygons and of the separation lines. This model uses a considerable smaller number of variables than the few other approaches proposed in the literature. We use the nonlinear programming solver IPOPT (an algorithm of interior points type), which is part of COIN-OR. Computational tests were run using established benchmark instances and the results were compared with the ones obtained with other methodologies in the literature that use free rotations. © 2018 Brazilian Operations Research Society.