Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc.) through special facilities or transshipment points called hubs. In this paper, we consider a central mine and a number of hubs (e.g., factories) connected to a number of nodes (e.g., shops or customers) in a network. First, the hub network is designed, then, a raw materials transportation from a central mine to the hubs (i.e., factories) is scheduled. In this case, we consider only one transportation system regarded as single machine scheduling. Furthermore, we use this hub network to solve the scheduling model. In this paper, we consider the capacitated single allocation hub covering location problem (CSAHCLP) and then present the mixed-integer programming (MIP) model. Due to the computational complexity of the resulted models, we also propose two improved meta-heuristic ALGORITHMS, namely a genetic algorithm and a SHUFFLED FROG LEAPING algorithm in order to find a near-optimal solution of the given problem. The performance of the solutions found by the foregoing proposed ALGORITHMS is compared with exact solutions of the mathematical programming model.

Various methods have been proposed for constructing and optimizing fuzzy inference systems. This paper first proposes a new method to cre-ate zero-order Sugeno fuzzy inference systems using the SHUFFLED FROG LEAPING Algorithm ((SFL)A). As the second contribution, the paper introduces four improvements over (SFL)A. The resulting version of (SFL)A, called I(SFL)A (Improved (SFL)A), is also applied to create zero-order Sugeno fuzzy inference systems. We conducted experiments to assess I(SFL)A and compare it with the original (SFL)A and three well-known evolutionary ALGORITHMS over five standard classification data sets from the UCI machine learning repository. The experimental results show that I(SFL)A creates fuzzy systems more efficiently than the standard (SFL)A and some other evolutionary ALGORITHMS, i. e., GA, ACO and PSO. Moreover, with respect to the accuracy and the convergence speed criteria, I(SFL)A and PSO outperform other evolutionary ALGORITHMS, while their performance is comparable to each other.

Several industrial products such as moulds, dies, engine block, automotive parts, etc., require machining of a large number of holes. Similarly, applications like boiler plates, food-business processing separator's, printed circuit boards, drum and trammel screens, etc., consist of a matrix of a large number of holes. Many machining operations, such as drilling, enlargement, tapping, or reaming, are needed to achieve the final sizes of individual holes, resulting in a variety of possible sequences to complete the hole-making operations. The major issue involved in hole-making operations is the tool travel time. It is often vital to determine the optimal sequence of operations so that the overall processing cost of hole-making operations can be minimized. In this work, thus an attempt is made to minimize the total tool travel of hole-making operations by using a relatively new optimization algorithm known as modified SHUFFLED FROG LEAPING for the determination of the optimal sequence of operations. Modification is made in the present SHUFFLED FROG-LEAPING algorithm by using three parameters with their positive values in order to widen the search capability of the existing algorithm. This paper considers three case studies of a rectangular matrix of holes to explain the proposed procedure. The outcomes of optimization with a modified SHUFFLED FROG-LEAPING algorithm are compared to those obtained with the genetic algorithm and the ant colony algorithm. Additionally, the higher dimensional problem of 20 x 20 rectangular matrix of holes is considered in this work.