Artikel Jurnal :: Kembali

Artikel Jurnal :: Kembali

A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts / Erfan Rahimi, Mohammad Mahdi paydar, Iraj Mahdavi, Javid Jouzdani, Amir Arabsheybani

Erfan Rahimi; Mohammad Mahdi Paydar; Iraj Mahdavi; Javid Jouzdani; Amir Arabsheybani (Taylor and Francis, 2018)
 Abstrak
ABSTRACT

This paper addresses a new robust multi objective multi period model for supply chain planning under uncertainty considering quantity discounts. The proposed model maximizes the current proht of the distributor by making a balance between the total costs of the supply chain and the distributor company s revenues of selling products and also maximizes the company s expected profit by introducing brands and taking the risk of loss on it. Considering uncertainty in the purchasing cost, selling fees, and demand fluctuations, the new robust multi objective mixed integer programming model is solved as a single objective mixed integer programming model by utilizing the LP metrics method. By settling regulatory penalty parameters and considering different economic scenarios, the robustness and effectiveness of the developed model are verified with the data from BEH PAKHSH Company, a commodities distributor in Iran. The outcomes show that the proposed model is a promising approach to run an efficient supply chain.
 Metadata
No. Panggil : 658 JIPE 35:4 (2018)
Pengarang :
Pengarang/kontributor lain :
Subjek :
Penerbitan : Philadelphia: Taylor and Francis, 2018
Sumber PengataloganLibUI eng rda
ISSN21681015
Majalah/JurnalJournal of Industrial and Production Engineering
VolumeVol. 35, No. 4, June 2018: Hal. 214-228
Tipe Konten text
Tipe Media unmediated
Tipe Carrier volume
Akses Elektronik
Institusi Pemilik Universitas Indonesia
Lokasi Perpustakaan UI, Lantai 4, R. Koleksi Jurnal
  • Ketersediaan
  • Ulasan
  • Sampul
No. Panggil No. Barkod Ketersediaan
658 JIPE 35:4 (2018) 03-18-375218234 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 20479575