TY - JOUR
T1 - Stochastic Benders decomposition for the supply chain investment planning problem under demand uncertainty
AU - Oliveira, Fabricio
AU - Hamacher, Silvio
PY - 2012/9
Y1 - 2012/9
N2 - This paper presents the application of a stochastic Benders decomposition algorithm for the problem of supply chain investment planning under uncertainty applied to the petroleum byproducts supply chain. The uncertainty considered is related with the unknown demand levels for oil products. For this purpose, a model was developed based on two-stage stochastic programming. It is proposed two different solution methodologies, one based on the classical cutting plane approach presented by Van Slyke & Wets (1969), and other, based on a multi cut extension of it, firstly introduced by Birge & Louveaux (1988). The methods were evaluated on a real sized case study. Preliminary numerical results obtained from computational experiments are encouraging.
AB - This paper presents the application of a stochastic Benders decomposition algorithm for the problem of supply chain investment planning under uncertainty applied to the petroleum byproducts supply chain. The uncertainty considered is related with the unknown demand levels for oil products. For this purpose, a model was developed based on two-stage stochastic programming. It is proposed two different solution methodologies, one based on the classical cutting plane approach presented by Van Slyke & Wets (1969), and other, based on a multi cut extension of it, firstly introduced by Birge & Louveaux (1988). The methods were evaluated on a real sized case study. Preliminary numerical results obtained from computational experiments are encouraging.
KW - Stochastic benders decomposition
KW - Stochastic optimization
KW - Supply chain investment planning
UR - http://www.scopus.com/inward/record.url?scp=84871524958&partnerID=8YFLogxK
U2 - 10.1590/S0101-74382012005000027
DO - 10.1590/S0101-74382012005000027
M3 - Article
AN - SCOPUS:84871524958
SN - 0101-7438
VL - 32
SP - 663
EP - 676
JO - Pesquisa Operacional
JF - Pesquisa Operacional
IS - 3
ER -