Research Article
Open access
Published on 28 March 2024
Download pdf
Wang,R.;Pan,T.;Li,F.;You,S. (2024). Research on the daily replenishment model based on the complete knapsack problem. Advances in Operation Research and Production Management,2,10-16.
Export citation

Research on the daily replenishment model based on the complete knapsack problem

Runpeng Wang *,1, Tao Pan 2, Fukuo Li 3, Shuo You 4
  • 1 Liaoning Technical University
  • 2 Liaoning Technical University
  • 3 Liaoning Technical University
  • 4 Liaoning Technical University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/3029-0880/2/2024010

Abstract

This paper first reflects the sales stability of dishes by defining the calculation method of average profit and selects appropriate dishes based on the relationship between profits. Then, according to the greedy algorithm, the daily replenishment quantity can be optimized into six complete knapsack problems. Finally, the genetic algorithm can solve the selection frequency and weight of each dish. The calculation results show that the maximum profit of the supermarket is 1102.2189 yuan.

Keywords

Daily replenishment quantity, Greedy algorithm, Complete knapsack problem, Genetic algorithm

[1]. Zheng, A. M. (2007). Research on the general parts safety stock model based on supply lead time [Doctoral dissertation, Huazhong University of Science and Technology]. Hubei, China.

[2]. National College Students' Mathematical Modeling Competition. (2023, September 7). National College Students' Mathematical Modeling Competition. Retrieved from http://mcm.edu.cn

[3]. Zhang, Y. (2017). Research on the variable value 0-1 knapsack problem model and its optimization algorithm [Doctoral dissertation, Beijing Jiaotong University]. Beijing, China.

[4]. Li, G. L., & Zhu, X. L. (2007). The 0/1 knapsack problem. Microcomputer Applications, 23(4), 12-14.

[5]. Chen, Z., Zhong, Y. W., & Lin, J. (2021). A hybrid greedy genetic algorithm for solving the 0-1 knapsack problem. Journal of Computer Applications, 41(01), 87-94.

[6]. (2021). Research on the optimization method of complex knapsack problem model based on genetic algorithm [Doctoral dissertation, Anqing Normal University]. Anqing, China.

[7]. Yan, T. S. (2007). Solving the 0/1 knapsack problem with a hybrid genetic algorithm based on a greedy algorithm. Modern Computer (Professional Edition), (08), 14-17.

Cite this article

Wang,R.;Pan,T.;Li,F.;You,S. (2024). Research on the daily replenishment model based on the complete knapsack problem. Advances in Operation Research and Production Management,2,10-16.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Journal:Advances in Operation Research and Production Management

Volume number: Vol.2
ISSN:3029-0880(Print) / 3029-0899(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).