Can We Avoid Tacit Trade-Offs between Flexibility and Efficiency in Systematic Conservation Planning? The Mediterranean Sea as a Case Study

Species distribution models (SDMs) provide robust inferences about species-specific site suitability and are increasingly used in systematic conservation planning (SCP). SDMs are subjected to intrinsic uncertainties, and conservation studies have generally overlooked these. The integration of SDM uncertainties in conservation solutions requires the development of a suitable optimization algorithm. Exact optimization algorithms grant efficiency to conservation solutions, but most of their implementations generate a single binary and indivisible solution. Therefore, without variation in their parameterization, they provide low flexibility in the implementation of conservation solutions by stakeholders. Contrarily, heuristic algorithms provide such flexibility, by generating large amounts of sub-optimal solutions. As a consequence, efficiency and flexibility are implicitly linked in conservation applications: mathematically efficient solutions provide less flexibility, and the flexible solutions provided by heuristics are sub-optimal. To avoid this trade-off between flexibility and efficiency in SCP, we propose a reserve-selection framework, based on exact optimization combined with a post-selection of SDM outputs. This reserve-selection framework provides flexibility and addresses the efficiency and representativeness of conservation solutions. To exemplify the approach, we analyzed an experimental design, crossing pre- and post-selection of SDM outputs versus heuristics and exact mathematical optimizations. We used the Mediterranean Sea as a biogeographical template for our analyses, integrating the outputs of eight SDM techniques for 438 fish species.

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

This paper deals with the problem of the optimal selection and location of batteries in DC distribution grids by proposing a new mixed-integer convex model. The exact mixed-integer nonlinear model is transformed into a mixed-integer quadratic convex model (MIQC) by approximating the product among voltages in the power balance equations as a hyperplane. The most important characteristic of our proposal is that the MIQC formulations ensure the global optimum reaching via branch & bound methods and quadratic programming since each combination of the binary variables generates a node with a convex optimization subproblem. The formulation of the objective function is associated with the minimization of the energy losses for a daily operation scenario considering high renewable energy penetration. Numerical simulations show the effectiveness of the proposed MIQC model to reach the global optimum of the optimization model when compared with the exact optimization model in a 21-node test feeder. All the validations are carried out in the GAMS optimization software.

Release planning for multi-tenant software as a service (SaaS) applications

In multi-tenant Software as a Service (SaaS) applications, the providers are required to regularly deliver new releases of the software in order to satisfy the evolving requirements of tenants. The first step in a release development lifecycle is the release planning process. This thesis formulates the problem of the "next release" planning for multi-tenant Software as a Service (SaaS) applications. Two variables that influence release planning in SaaS applications are introduced: the degree of commonality of features and the contractual constraints. The commonality of a feature denotes the number of tenants that have requested that feature. The contractual constraints denote the effects of service levels to which tenants have subscribed on the release planning process. Furthermore, this thesis proposes three novel approaches in order to tackle the problem of the "next release" planning for multi-tenant SaaS applications. The first one is a prioritization approach that employs a Fuzzy Inference System (FIS) engine in order to speed up the release planning process and overcome the uncertainty associated with the human judgment. In this approach, the human expertise, which is represented by fuzzy rules, is considered automatically in the release planning process. The second and third approaches consider release planning as an optimization problem. The second approach uses an exact optimization method (Binary Linear Programming (BLP)) in order to generate an optimal release plan, while the third approach uses heuristic optimization method (Genetic Algorithm (GA)). All of the three approaches aim to generate a plan for the next release that maximizes the degree of overall tenants’ satisfaction, maximizes the degree of commonality, and minimizes the potential risk while taking into account contractual, effort, and dependencies constraints. Moreover, the thesis presents an experimental study of the proposed approaches in order to determine which approach is best suited to different sets of scenarios. In this experiment, the performance of the proposed approaches is evaluated using four criteria: the overall tenants’ satisfaction, the commonality, the adherence to the risk, and the running time. Additionally, the thesis presents an experiment that compares the proposed approaches with a compared model that is selected from the literature.

Release planning for multi-tenant software as a service (SaaS) applications

In multi-tenant Software as a Service (SaaS) applications, the providers are required to regularly deliver new releases of the software in order to satisfy the evolving requirements of tenants. The first step in a release development lifecycle is the release planning process. This thesis formulates the problem of the "next release" planning for multi-tenant Software as a Service (SaaS) applications. Two variables that influence release planning in SaaS applications are introduced: the degree of commonality of features and the contractual constraints. The commonality of a feature denotes the number of tenants that have requested that feature. The contractual constraints denote the effects of service levels to which tenants have subscribed on the release planning process. Furthermore, this thesis proposes three novel approaches in order to tackle the problem of the "next release" planning for multi-tenant SaaS applications. The first one is a prioritization approach that employs a Fuzzy Inference System (FIS) engine in order to speed up the release planning process and overcome the uncertainty associated with the human judgment. In this approach, the human expertise, which is represented by fuzzy rules, is considered automatically in the release planning process. The second and third approaches consider release planning as an optimization problem. The second approach uses an exact optimization method (Binary Linear Programming (BLP)) in order to generate an optimal release plan, while the third approach uses heuristic optimization method (Genetic Algorithm (GA)). All of the three approaches aim to generate a plan for the next release that maximizes the degree of overall tenants’ satisfaction, maximizes the degree of commonality, and minimizes the potential risk while taking into account contractual, effort, and dependencies constraints. Moreover, the thesis presents an experimental study of the proposed approaches in order to determine which approach is best suited to different sets of scenarios. In this experiment, the performance of the proposed approaches is evaluated using four criteria: the overall tenants’ satisfaction, the commonality, the adherence to the risk, and the running time. Additionally, the thesis presents an experiment that compares the proposed approaches with a compared model that is selected from the literature.

Mexican Axolotl Optimization: A Novel Bioinspired Heuristic

When facing certain problems in science, engineering or technology, it is not enough to find a solution, but it is essential to seek and find the best possible solution through optimization. In many cases the exact optimization procedures are not applicable due to the great computational complexity of the problems. As an alternative to exact optimization, there are approximate optimization algorithms, whose purpose is to reduce computational complexity by pruning some areas of the problem search space. To achieve this, researchers have been inspired by nature, because animals and plants tend to optimize many of their life processes. The purpose of this research is to design a novel bioinspired algorithm for numeric optimization: the Mexican Axolotl Optimization algorithm. The effectiveness of our proposal was compared against nine optimization algorithms (artificial bee colony, cuckoo search, dragonfly algorithm, differential evolution, firefly algorithm, fitness dependent optimizer, whale optimization algorithm, monarch butterfly optimization, and slime mould algorithm) when applied over four sets of benchmark functions (unimodal, multimodal, composite and competition functions). The statistical analysis shows the ability of Mexican Axolotl Optimization algorithm of obtained very good optimization results in all experiments, except for composite functions, where the Mexican Axolotl Optimization algorithm exhibits an average performance.

A Branch-and-Cut-and-Price Algorithm for the Electric Vehicle Routing Problem with Multiple Technologies

We provide an exact optimization algorithm for the electric vehicle routing problem with multiple recharge technologies. Our branch-and-cut-and-price algorithm relies upon a path-based formulation, where each column in the master problem represents a sequence of customer visits between two recharge stations instead of a whole route. This allows for massive decomposition, and parallel implementation of the pricing phase, exploiting the large number of independent pricing sub-problems. The algorithm could solve instances with up to thirty customers, nine recharge stations, five vehicles and three technologies to proven optimality. Near-optimal heuristic solutions were obtained with a general-purpose MIP solver from the columns generated at the root node.