Parallel Machine Scheduling Under Uncertainty: Models and Exact Algorithms

We study parallel machine scheduling for makespan minimization with uncertain job processing times. To incorporate uncertainty and generate solutions that are, in some way, insensitive to unfolding information, three different modeling paradigms are adopted: a robust model, chance-constrained distributionally model. focus on devising generic solution methods can efficiently handle these models. develop two general procedures: cutting-plane method leverages the submodularity models customized dichotomic search procedure decision version of bin packing variant under solved each iteration. A branch-and-price algorithm is designed solve problems. The efficiency our shown through extensive computational tests. compare from report lessons learned regarding choice between frameworks planning uncertainty. History: Accepted by Andrea Lodi, Area Editor Design & Analysis Algorithms—Discrete. Funding: This work was supported National Natural Science Foundation China [Grants 72101264 71801218] Technology Innovation Team Higher Educational Institutions Hunan Province [Grant 2020RC4046]. Supplemental Material: online supplement available at https://doi.org/10.1287/ijoc.2022.1229 .