A joint chance-constrained programming approach for the single-item capacitated lot-sizing problem with stochastic demand
AffiliationUniv Arizona, Dept Syst & Ind Engn
Joint probabilistic constraint
Sample approximation approach
Mixed-integer linear programming
MetadataShow full item record
CitationGicquel, C. & Cheng, J. Ann Oper Res (2018) 264: 123. https://doi.org/10.1007/s10479-017-2662-5
JournalANNALS OF OPERATIONS RESEARCH
Rights© Springer Science+Business Media, LLC 2017
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractWe study the single-item single-resource capacitated lot-sizing problem with stochastic demand. We propose to formulate this stochastic optimization problem as a joint chance-constrained program in which the probability that an inventory shortage occurs during the planning horizon is limited to a maximum acceptable risk level. We investigate the development of a new approximate solution method which can be seen as an extension of the previously published sample approximation approach. The proposed method relies on a Monte Carlo sampling of the random variables representing the demand in all planning periods except the first one. Provided there is no dependence between the demand in the first period and the demand in the later periods, this partial sampling results in the formulation of a chance-constrained program featuring a series of joint chance constraints. Each of these constraints involves a single random variable and defines a feasible set for which a conservative convex approximation can be quite easily built. Contrary to the sample approximation approach, the partial sample approximation leads to the formulation of a deterministic mixed-integer linear problem having the same number of binary variables as the original stochastic problem. Our computational results show that the proposed method is more efficient at finding feasible solutions of the original stochastic problem than the sample approximation method and that these solutions are less costly than the ones provided by the Bonferroni conservative approximation. Moreover, the computation time is significantly shorter than the one needed for the sample approximation method.
Note12 month embargo; published online: 04 October 2017
VersionFinal accepted manuscript
SponsorsFrench National Research Agency (Project LotRelax) [ANR-11-JS0002-01]