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BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 23, 442 - 451, 30.09.2023
https://doi.org/10.55549/epstem.1371794

Öz

Kaynakça

  • Adrio, G., García-Villoria, A., Juanpera, M., & Pastor, R. (2023). MILP model for the mid-term production planning in a chemical company with non-constant consumption of raw materials. an industrial application. Computers & Chemical Engineering, 177, 108361.
  • Alegoz, M., & Yapicioglu, H. (2019). Supplier selection and order allocation decisions under quantity discount and fast service options. Sustainable Production and Consumption, 18, 179–189.
  • Ali, Md. R., Nipu, S. Md. A., & Khan, S. A. (2023). A decision support system for classifying supplier selection criteria using machine learning and random forest approach. Decision Analytics Journal, 7, 100238.

Probabilistic Piecewise-Objective Optimization Model for Integrated Supplier Selection and Production Planning Problems Involving Discounts and Probabilistic Parameters: Single Period Case

Yıl 2023, Cilt: 23, 442 - 451, 30.09.2023
https://doi.org/10.55549/epstem.1371794

Öz

In manufacturing and retail industries, supplier selection problems deal with allocating the optimal raw material amount that should be purchased to each supplier such that the procurement cost is minimal. Meanwhile, production planning problems deal with maximizing the product amount to be produced. Decision-makers need to take optimal decisions for those problem to gain the maximal revenue. In this paper, a novel mathematical model in the class of probabilistic piecewise programming is proposed as a decision-making support that can be used to find the optimal decision in solving both integrated supplier selection and production planning problems involving discounts and probabilistic parameters. The objective is to gain the optimal performance of the supply chain, i.e., maximizing the profit from the production activity. The model covers multi-raw material, multi-supplier, multi-product, and multi-buyer situations. Numerical experiments were conducted to evaluate the proposed model and to illustrate how the optimal decision is taken. Results showed that the proposed decision-making support successfully solved the problem and provided the optimal decision for the given problem. Therefore, the proposed model can be implemented by decision-makers/managers in industries.

Kaynakça

  • Adrio, G., García-Villoria, A., Juanpera, M., & Pastor, R. (2023). MILP model for the mid-term production planning in a chemical company with non-constant consumption of raw materials. an industrial application. Computers & Chemical Engineering, 177, 108361.
  • Alegoz, M., & Yapicioglu, H. (2019). Supplier selection and order allocation decisions under quantity discount and fast service options. Sustainable Production and Consumption, 18, 179–189.
  • Ali, Md. R., Nipu, S. Md. A., & Khan, S. A. (2023). A decision support system for classifying supplier selection criteria using machine learning and random forest approach. Decision Analytics Journal, 7, 100238.
Toplam 3 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çevresel ve Sürdürülebilir Süreçler
Bölüm Makaleler
Yazarlar

Sutrisno Sutrisno

Widowati Wıdowatı

Robertus Heri Soelistyo Utomo

Erken Görünüm Tarihi 5 Ekim 2023
Yayımlanma Tarihi 30 Eylül 2023
Yayımlandığı Sayı Yıl 2023Cilt: 23

Kaynak Göster

APA Sutrisno, S., Wıdowatı, W., & Utomo, R. H. S. (2023). Probabilistic Piecewise-Objective Optimization Model for Integrated Supplier Selection and Production Planning Problems Involving Discounts and Probabilistic Parameters: Single Period Case. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 23, 442-451. https://doi.org/10.55549/epstem.1371794