RESUMEN
The one-dimensional cutting-stock problem (1D-CSP) consists of obtaining a set of items of different lengths from stocks of one or different lengths, where the minimization of waste is one of the main objectives to be achieved. This problem arises in several industries like wood, glass, and paper, among others similar. Different approaches have been designed to deal with this problem ranging from exact algorithms to hybrid methods of heuristics or metaheuristics. The African Buffalo Optimization (ABO) algorithm is used in this work to address the 1D-CSP. This algorithm has been recently introduced to solve combinatorial problems such as travel salesman and bin packing problems. A procedure was designed to improve the search by taking advantage of the location of the buffaloes just before it is needed to restart the herd, with the aim of not to losing the advance reached in the search. Different instances from the literature were used to test the algorithm. The results show that the developed method is competitive in waste minimization against other heuristics, metaheuristics, and hybrid approaches.
RESUMEN
The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.
RESUMEN
This article finds feasible solutions to the travelling salesman problem, obtaining the route with the shortest distance to visit n cities just once, returning to the starting city. The problem addressed is clustering the cities, then using the NEH heuristic, which provides an initial solution that is refined using a modification of the metaheuristic Multi-Restart Iterated Local Search MRSILS; finally, clusters are joined to end the route with the minimum distance to the travelling salesman problem. The contribution of this research is the use of the metaheuristic MRSILS, that in our knowledge had not been used to solve the travelling salesman problem using clusters. The main objective of this article is to demonstrate that the proposed algorithm is more efficient than Genetic Algorithms when clusters are used. To demonstrate the above, both algorithms are compared with some cases taken from the literature, also a comparison with the best-known results is done. In addition, statistical studies are made in the same conditions to demonstrate this fact. Our method obtains better results in all the 10 cases compared.
