1. Proceedings
  2. I3M
  3. 2023
  4. FOODOPS
  5. 10.46354/i3m.2023.foodops.008

An asexual genetic algorithm for the smallholders’ demand selection problem 

  • a Manuella Germanos ,
  • b Oussama Ben-Amma
  • c Gregory Zacharewicz
  • a,b,c  Laboratory for the Science of Risks, IMT Mines Alès, France 
  • a,b,c  EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Alès, Alès, France 
Cite as
Germanos, M., Ben-Ammar, O., Zacharewicz, G. (2023). An asexual genetic algorithm for the smallholders’ demand selection problem. Proceedings of the 9th International Food Operations and Processing Simulation Workshop (FoodOPS 2023).,008. DOI: https://doi.org/10.46354/i3m.2023.foodops.008
 Download PDF

Abstract

As food safety shakes due to climate change and the looming possibility of a calamity similar to that of the COVID-19 pandemic, along with the increase in the world’s population, there is an immediate need to increase food production by optimizing the agri-food supply chain. This work aims to help small-scale farmers manage this increase in orders and help them gear their resources towards more profitable practices by employing a multi-objective optimization model based on genetic algorithms that considers environmental aspects. To do so, we implement an asexual genetic algorithm that takes as input the demands received by the farmer and outputs the best combinations of demands to meet. The model takes into consideration the amount of land available for cultivation, as well as the resources (water, cost of cultivation, etc.) and revenue of the demands to determine the best combinations of demands to meet. This work is developed in the SMALLDERS framework and builds over the scarce literature that tackled the demand selection problem in the agricultural field.

References

  1. Aday, S. and Aday, M. S. (2020). Impact of covid-19 on the food supply chain. Food Quality and Safety, 4(4):167– 180. 
  2. Akbari, M., Asadi, P., Besharati Givi, M., and Khodaban dehlouie, G. (2014). Artificial neural network and opti mization. Advances in friction-stir welding and processing, pages 543–599. 
  3. Aouam, T., Geryl, K., Kumar, K., and Brahimi, N. (2018). Production planning with order acceptance and demand uncertainty. Computers & Operations Research, 91:145– 159. 
  4. Cantó, J., Curiel, S., and Martínez-Gómez, E. (2009). A simple algorithm for optimization and model fitting: Aga (asexual genetic algorithm). Astronomy & Astro physics, 501(3):1259–1268. 
  5. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197. 
  6. Dumetz, L., Gaudreault, J., Thomas, A., Lehoux, N., Marier, P., and El-Haouzi, H. (2017). Evaluating order accep tance policies for divergent production systems with co-production. International Journal of Production Re search, 55(13):3631–3643. 
  7. Ebben, M. J., Hans, E. W., and Olde Weghuis, F. (2005). Workload based order acceptance in job shop environ ments. OR spectrum, 27:107–122. 
  8. Fogel, D. (1994). Asymptotic convergence properties of ge netic algorithms and evolutionary programming: Anal ysis and experiments. Cybernetics and Systems, 25:389– 407.
  9. Geunes, J. and Geunes, J. (2012). Dynamic lot sizing with demand selection and the pricing analog. Demand Flex ibility in Supply Chain Planning, pages 41–50.
  10. Geunes, J., Merzifonluoğlu, Y., Romeijn, H. E., and Taaffe, K. (2005). Demand selection and assignment problems in supply chain planning. In Emerging Theory, Methods, and Applications, pages 124–141. INFORMS. 
  11. Leng, J., Ruan, G., Song, Y., Liu, Q., Fu, Y., Ding, K., and Chen, X. (2021). A loosely-coupled deep reinforce ment learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0. Journal of cleaner production, 280:124405. 
  12. Li, Q., Zhang, D., Wang, S., and Kucukkoc, I. (2019). A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production. The International Journal of Advanced Manufacturing Technol ogy, 105:3711–3729.
  13. Mohammadivojdan, R. and Geunes, J. (2018). Supply and demand selection problems in supply chain planning. Open Problems in Optimization and Data Analysis, pages 61–82.
  14. Moscato, P. and Cotta, C. (2010). A modern introduction to memetic algorithms. Handbook of metaheuristics, pages 141–183. 
  15. Salesi, S., Cosma, G., and Mavrovouniotis, M. (2021). Taga: Tabu asexual genetic algorithm embedded in a filter/filter feature selection approach for high dimensional data. Information Sciences, 565:105–127.
  16. Shu, J., Li, Z., and Huang, L. (2013). Demand selection deci sions for a multi-echelon inventory distribution system. Journal of the Operational Research Society, 64:1307–1313. 
  17. Silva, Y. L. T., Subramanian, A., and Pessoa, A. A. (2018). Exact and heuristic algorithms for order acceptance and scheduling with sequence-dependent setup times. Computers & operations research, 90:142–160.