This work presents the modeling and simulation of a hydraulic network with four nodes and two branches that form a two-level water distribution system. It also proposes a distribution of hydraulic valves that allows emulating a leak using a valve and different network configurations, e.g., simple ducts, closed networks and branched networks. The network is modeled in the steady state considering turbulent flow. Numerical experiments are performed, and the results show that the proposed network is useful for the design of leakage diagnosis and control algorithms in different configurations and leakage scenarios.

This work proposes a model predictive control (MPC) strategy for pressure management and leakage reduction in a water distribution system (WDS). Unlike most of the reported models that mainly consider EPANET-based models, the proposed method considers its dynamic representation given by ordinary differential equations. The proposed MPC uses a pressure-reducing valve (PRV) as a control element to regulate the pressure in the WDS to track the demand. The control scheme proposes a strategy to manage the high nonlinearity of the PRV and takes into account the demand profile throughout the day as well as the leaks that occur in the pipeline. The estimates of magnitude and location of the leak are provided by an Extended Kalman Filter from previous work and with the aid of a rule-based set point manager reduces the fluid loss in the event of a leak. Different scenarios are studied to illustrate the effectiveness of the proposed control system, achieving an approximate reduction of up to 5% of water losses, demonstrating robustness in the case of uncertainty in the leak location estimate.

This paper presents a pressure management technique for the reduction of leaks considering as a case study a branched water distribution system. The proposed technique is based on the detection and location of the leak using a genetic algorithm (GA) and pressure control using a Linear Quadratic Regulator (LQR). The validation of the proposed method uses measured pressure and flow data from a laboratory-scale water distribution system and its dynamic model.

Vehicle Routing Problems are central to logistics and operational research, arising in diverse contexts such as transportation planning, manufacturing systems, and military operations. While Deep Reinforcement Learning has been successfully applied to both deterministic and stochastic variants of Vehicle Routing Problems, existing approaches often neglect critical time-sensitive conditions. This work addresses the Stochastic Capacitated Vehicle Routing Problem with Service Times and Deadlines, a challenging formulation that is suited to model time routing conditions. The proposal, POMO-DC, integrates a novel dynamic context mechanism. At each decision step, this mechanism incorporates the vehicle’s cumulative travel time and delays—features absent in prior models—enabling the policy to adapt to changing conditions and avoid time violations. The model is evaluated on stochastic instances with 20, 30, and 50 customers and benchmarked against Google OR-Tools using multiple metaheuristics. Results show that POMO-DC reduces average delays by up to 88% (from 169.63 to 20.35 min for instances of 30 customers) and 75% (from 4352.43 to 1098.97 min for instances of 50 customers), while maintaining competitive travel times. These outcomes highlight the potential of Deep Reinforcement Learning-based frameworks to learn patterns from stochastic data and effectively manage time uncertainty in Vehicle Routing Problems.

This work proposes solving the Traveling Salesman Problem by applying combined heuristic global and local search methods. The proposed method is divided into three phases: first, it evaluates an initial route and chooses the minimum value of rows in a distance matrix. The next phase seeks to improve the route’s cost globally and with a 2-opt local search method, remove the crossings, and further minimize the cost of departure. Finally, the last phase evaluates and conserves each cost using tabu search, proposing a parameter $\beta$ that describes the algorithm convergence factor. This paper assessed 29 TSPLIB instances and compared them with other algorithms: the ant colony optimization algorithm (ACO), artificial neural network (ANN), particle swarm optimization (PSO), and genetic algorithm (GA). With the proposed algorithm, results close to the optimal ones are obtained, and the proposed algorithm is assessed on 29 TSPLIB instances. Based on 30 independent runs per instance, the method achieves a mean absolute percentage error (MAPE) of 1.4484% relative to the known optima, demonstrating its accuracy. Furthermore, statistical comparisons using the coefficient of variation (CV) for runtime and the Wilcoxon signed-rank test confirm that the proposed hybrid algorithm is significantly faster than traditional ant colony optimization (T-ACO) and a new ant colony optimization algorithm (N-ACO) while maintaining competitive solution quality.

In this paper, a nonlinear observer design is presented for simultaneous parameter estimation and state variables estimation. The case of study is microalgae cultures for biodiesel generation, where reaction rates, biomass concentration, intracellular quota and nitrogen concentration are critical variables that provide information about the state of the process. However, these variables might be difficult to measure due to the lack of specific instruments, high sensor costs or infeasibility of installation in the process. Therefore, two observer-based virtual sensors are presented in this paper as an analytical alternative to perform estimation of the main important variables or parameters of the process: a nonlinear adaptive observer and a nonlinear high-gain observer. The observers are based on the Droop´s mathematical model that describes the ability of microalgae to store nutrients and the decoupling between substrate uptake and biomass growth. Numerical simulations are made in order to evaluate the performance of the proposed observers.