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In this article, a model of a T-shaped fin, consisting of a set of ordinary differential equations (ODEs), is considered. The purpose of this article is to numerically solve ODE systems of a T-shaped fin (there is no reported exact and analytical solution) using an alternative approach. Utilizing a base approximation function, some mathematical principles and metaheuristics, an approximate solution very close to the existing numerical solution was found. The weighted residual function is used as an objective function along with its constraints, such as boundary and initial values. For the sake of comparison, a generational distance metric is used to evaluate the obtained results compared with the existing results in the literature. The approximate solution found by the applied approach demonstrates its efficiency and performance compared with the existing numerical approach. [ABSTRACT FROM AUTHOR]

Traffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration (i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

In this article, scheduling and rescheduling problems with increasing processing time and new job insertion are studied for reprocessing problems in the remanufacturing process. To handle the unpredictability of reprocessing time, an experience-based strategy is used. Rescheduling strategies are applied for considering the effect of increasing reprocessing time and the new subassembly insertion. To optimize the scheduling and rescheduling objective, a discrete harmony search (DHS) algorithm is proposed. To speed up the convergence rate, a local search method is designed. The DHS is applied to two real-life cases for minimizing the maximum completion time and the mean of earliness and tardiness (E/T). These two objectives are also considered together as a bi-objective problem. Computational optimization results and comparisons show that the proposed DHS is able to solve the scheduling and rescheduling problems effectively and productively. Using the proposed approach, satisfactory optimization results can be achieved for scheduling and rescheduling on a real-life shop floor. [ABSTRACT FROM AUTHOR]

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms. [ABSTRACT FROM PUBLISHER]

The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]