Healthy Diet Lists Considering Carbon Footprint and Calories
- DOI
- 10.2991/978-2-494069-37-4_34How to use a DOI?
- Keywords
- Diet list; Calories; Carbon footprint; Mathematical programming; Combinatorial optimization
- Abstract
To maintain good health, people need to take suitable calories every day. The calories needed for persons depend on their personal features, such as gender, age, weight and levels of physical activity. In this paper, we develop a mathematical model to generate optimized diet lists according to individual conditions. To keep a sustainable environment, the carbon footprint related to diet is also considered in the model. Integer programming is employed to help users find solutions and compose suitable diet lists. As the number of candidate items and constraints become larger, the optimization problem discussed here becomes more complex. Given such understanding, a variety of experiments are performed to investigate the influences of some critical factors on the results. Experiments from this study show that the proposed approach can generate satisfying diet lists effectively.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Chih-Teng Chen AU - Chia-Ying Chang AU - Rong-Chang Chen AU - Yi-Ching Hsiao AU - Pin-Jung Lai PY - 2022 DA - 2022/12/19 TI - Healthy Diet Lists Considering Carbon Footprint and Calories BT - Proceedings of the 2022 International Conference on Diversified Education and Social Development (DESD 2022) PB - Atlantis Press SP - 264 EP - 275 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-37-4_34 DO - 10.2991/978-2-494069-37-4_34 ID - Chen2022 ER -