https://scholars.lib.ntu.edu.tw/handle/123456789/435396
標題: | An OR practitioner's solution approach to the multidimensional knapsack problem | 作者: | Kern, Z Lu, Y Vasko, FJ CHIEN-JU CHIANG |
關鍵字: | Mixed-integer programming; Payment term; Trade credit; Logistics; Quantity flexible contract; Factoring | 公開日期: | 2020 | 出版社: | GROWING SCIENCE | 卷: | 11 | 期: | 1 | 起(迄)頁: | 73 | 來源出版物: | INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS | 摘要: | © 2019 by the authors; licensee Growing Science, Canada. 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/435396 | ISSN: | 1923-2926 | DOI: | 10.5267/j.ijiec.2019.6.004 |
顯示於: | 食品安全與健康研究所 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。