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.

The Flexible Job Shop Scheduling Problem (FJSSP) continues to be studied extensively to test new metaheuristics and because of its closeness to current production systems. A variant of the FJSSP uses fuzzy processing times instead of fixed times. This paper proposes a new algorithm for FJSSP with fuzzy processing times called the global neighborhood with hill-climbing algorithm (GN-HC). This algorithm performs solution exploration using simple operators concurrently for global search neighborhood handling. For local search, random restart hill-climbing is applied at each solution to find the best machine for each operation. For the selection of operations in hill climbing, a record of the operations defining the fuzzy makespan is employed to use them as a critical path. Finally, an estimation of the crisp makespan with the longest processing times in hill climbing is made to improve the speed of the GN-HC. The GN-HC is compared with other recently proposed methods recognized for their excellent performance, using 6 FJSSP instances with fuzzy times. The obtained results show satisfactory competitiveness for GN-HC compared to state-of-the-art algorithms. The GN-HC implementation was performed in Matlab and can be found on GitHub (check Data Availability Statement at the end of the paper).

El problema de corte unidimensional consiste en cortar un objeto o stock, cuya longitud puede ser finita o infinita, para producir objetos más pequeños o ítems con longitud finita. Este problema aparece en una gran cantidad de industrias alrededor del mundo. En este trabajo se propone adaptar el algoritmo de optimización del búfalo Africano con el objetivo de reducir los stocks necesarios para satisfacer la demanda de ítems. El algoritmo se probó en un conjunto de instancias que son referencia para este problema. Los resultados indican que el algoritmo consigue soluciones competitivas de acuerdo a la minimización del stock.

El problema de corte abordado en este documento consiste en minimizar el desperdicio total producido al cortar un conjunto de piezas pequeñas en una secuencia determinada a partir de piezas de material más grandes. El algoritmo del búfalo Africano ha sido empleado exitosamente para resolver problemas de tipo combinatorio. Una de las dificultades de este algoritmo es generar soluciones discretas para esta clase de problemas discretos. En este trabajo se emplea una variante discreta del algoritmo del búfalo Africano en la que se compara una técnica de cruza así como la técnica del valor del orden clasificado para obtener soluciones discretas. Se usa un conjunto de diez instancias de diferente complejidad para realizar la comparación de estas técnicas. Los resultados muestran que la técnica de cruza supera a las otras en cuanto a la calidad de las soluciones. Luego estos resultados se comparan contra otros algoritmos para evaluar su desempeño.