期刊名称：International Journal of Industrial Engineering Computations
印刷版ISSN：1923-2926
电子版ISSN：1923-2934
出版年度：2020
卷号：11
期号：1
页码：73-82
DOI：10.5267/j.ijiec.2019.6.004
语种：English
出版社：Growing Science Publishing Company
摘要：The 0-1 Multidimensional Knapsack Problem (MKP) is an NP-Hard problem that has many important applications in business and industry. However, business and industrial applications typically involve large problem instances that can be time consuming to solve for a guaranteed optimal solution. There are many approximate solution approaches, heuristics and metaheuristics, for the MKP published in the literature, but these typically require the fine-tuning of several parameters. Fine-tuning parameters is not only time-consuming (especially for operations research (OR) practitioners), but also implies that solution quality can be compromised if the problem instances being solved change in nature. In this paper, we demonstrate an efficient and effective implementation of a robust population-based metaheuristic that does not require parameter fine-tuning and can easily be used by OR practitioners to solve industrial size problems. Specifically, to solve the MKP, we provide an efficient adaptation of the two-phase Teaching-Learning Based Optimization (TLBO) approach that was originally designed to solve continuous nonlinear engineering design optimization problems. Empirical results using the 270 MKP test problems available in Beasley’s OR-Library demonstrate that our implementation of TLBO for the MKP is competitive with published solution approaches without the need for time-consuming parameter fine-tuning.
