Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm

Abstract

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.

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

“…Future improvements to the method for predicting wood properties will involve combining feature selection methods and taking time series factors into account. Furthermore, the IBWO method proposed by this study has some potentially practical value in tackling other challenges in the wood industry, such as solving the onedimensional cut-stock problem for wood and minimising wood waste through the IBWO algorithm [51]. A detailed examination of this topic may be the focus of our future efforts.…”