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• W2257931904 abstract "We consider a single-server scheduling problem given a fixed sequence of appointment arrivals with random no-shows and service durations. The probability distribution of the uncertain parameters is assumed to be ambiguous, and only the support and first moments are known. We formulate a class of distributionally robust (DR) optimization models that incorporate the worst-case expectation/conditional value-at-risk penalty cost of appointment waiting, server idleness, and overtime into the objective or constraints. Our models flexibly adapt to different prior beliefs of no-show uncertainty. We obtain exact mixed-integer nonlinear programming reformulations and derive valid inequalities to strengthen the reformulations that are solved by decomposition algorithms. In particular, we derive convex hulls for special cases of no-show beliefs, yielding polynomial-sized linear programming models for the least and the most conservative supports of no-shows. We test various instances to demonstrate the computational efficacy of our approaches and to compare the results of various DR models given perfect or ambiguous distributional information. The e-companion is available at https://doi.org/10.1287/opre.2017.1656 ." @default.
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• W2257931904 date "2017-12-01" @default.
• W2257931904 modified "2023-10-11" @default.
• W2257931904 title "Integer Programming Approaches for Appointment Scheduling with Random No-Shows and Service Durations" @default.
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• W2257931904 doi "https://doi.org/10.1287/opre.2017.1656" @default.
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