In:
Operations Research, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 65, No. 6 ( 2017-12), p. 1638-1656
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 .
Type of Medium:
Online Resource
ISSN:
0030-364X
,
1526-5463
DOI:
10.1287/opre.2017.1656
Language:
English
Publisher:
Institute for Operations Research and the Management Sciences (INFORMS)
Publication Date:
2017
detail.hit.zdb_id:
2019440-7
detail.hit.zdb_id:
123389-0
SSG:
3,2
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