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Documents authored by Schulz, Andreas S.

Document
APPROX
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2019.25
Robust Appointment Scheduling with Heterogeneous Costs

Authors: Andreas S. Schulz and Rajan Udwani

Published in: LIPIcs, Volume 145, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)

Abstract
Designing simple appointment systems that under uncertainty in service times, try to achieve both high utilization of expensive medical equipment and personnel as well as short waiting time for patients, has long been an interesting and challenging problem in health care. We consider a robust version of the appointment scheduling problem, introduced by Mittal et al. (2014), with the goal of finding simple and easy-to-use algorithms. Previous work focused on the special case where per-unit costs due to under-utilization of equipment/personnel are homogeneous i.e., costs are linear and identical. We consider the heterogeneous case and devise an LP that has a simple closed-form solution. This solution yields the first constant-factor approximation for the problem. We also find special cases beyond homogeneous costs where the LP leads to closed form optimal schedules. Our approach and results extend more generally to convex piece-wise linear costs. For the case where the order of patients is changeable, we focus on linear costs and show that the problem is strongly NP-hard when the under-utilization costs are heterogeneous. For changeable order with homogeneous under-utilization costs, it was previously shown that an EPTAS exists. We instead find an extremely simple, ratio-based ordering that is 1.0604 approximate.
Cite as

Andreas S. Schulz and Rajan Udwani. Robust Appointment Scheduling with Heterogeneous Costs. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 145, pp. 25:1-25:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{schulz_et_al:LIPIcs.APPROX-RANDOM.2019.25,
  author =	{Schulz, Andreas S. and Udwani, Rajan},
  title =	{{Robust Appointment Scheduling with Heterogeneous Costs}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)},
  pages =	{25:1--25:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-125-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{145},
  editor =	{Achlioptas, Dimitris and V\'{e}gh, L\'{a}szl\'{o} A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2019.25},
  URN =		{urn:nbn:de:0030-drops-112407},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2019.25},
  annote =	{Keywords: Appointment scheduling, approximation algorithms, robust optimization}
}
Document
DOI: 10.4230/LIPIcs.ESA.2016.74
Min-Sum Scheduling Under Precedence Constraints

Authors: Andreas S. Schulz and José Verschae

Published in: LIPIcs, Volume 57, 24th Annual European Symposium on Algorithms (ESA 2016)

Abstract
In many scheduling situations, it is important to consider non-linear functions of job completions times in the objective. This was already recognized by Smith (1956). Recently, the theory community has begun a thorough study of the resulting problems, mostly on single-machine instances for which all permutations of jobs are feasible. However, a typical feature of many scheduling problems is that some jobs can only be processed after others. In this paper, we give the first approximation algorithms for min-sum scheduling with (nonnegative, non-decreasing) non-linear functions and general precedence constraints. In particular, for 1|prec|sum w_j f(C_j), we propose a polynomial-time universal algorithm that performs well for all functions f simultaneously. Its approximation guarantee is 2 for all concave functions, at worst. We also provide a (non-universal) polynomial-time algorithm for the more general case 1|prec|sum f_j(C_j). The performance guarantee is no worse than 2+epsilon for all concave functions. Our results match the best bounds known for the case of linear functions, a widely studied problem, and considerably extend the results for minimizing sum w_jf(C_j) without precedence constraints.
Cite as

Andreas S. Schulz and José Verschae. Min-Sum Scheduling Under Precedence Constraints. In 24th Annual European Symposium on Algorithms (ESA 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 57, pp. 74:1-74:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{schulz_et_al:LIPIcs.ESA.2016.74,
  author =	{Schulz, Andreas S. and Verschae, Jos\'{e}},
  title =	{{Min-Sum Scheduling Under Precedence Constraints}},
  booktitle =	{24th Annual European Symposium on Algorithms (ESA 2016)},
  pages =	{74:1--74:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-015-6},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{57},
  editor =	{Sankowski, Piotr and Zaroliagis, Christos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2016.74},
  URN =		{urn:nbn:de:0030-drops-64157},
  doi =		{10.4230/LIPIcs.ESA.2016.74},
  annote =	{Keywords: scheduling, approximation algorithms, linear programming relaxations, precedence constraints}
}
Document
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2014.356
Robust Appointment Scheduling

Authors: Shashi Mittal, Andreas S. Schulz, and Sebastian Stiller

Published in: LIPIcs, Volume 28, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014)

Abstract
Health care providers are under tremendous pressure to reduce costs and increase quality of their services. It has long been recognized that well-designed appointment systems have the potential to improve utilization of expensive personnel and medical equipment and to reduce waiting times for patients. In a widely influential survey on outpatient scheduling, Cayirli and Veral (2003) concluded that the "biggest challenge for future research will be to develop easy-to-use heuristics." We analyze the appointment scheduling problem from a robust-optimization perspective, and we establish the existence of a closed-form optimal solution--arguably the simplest and best `heuristic' possible. In case the order of patients is changeable, the robust optimization approach yields a novel formulation of the appointment scheduling problem as that of minimizing a concave function over a supermodular polyhedron. We devise the first constant-factor approximation algorithm for this case.
Cite as

Shashi Mittal, Andreas S. Schulz, and Sebastian Stiller. Robust Appointment Scheduling. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014). Leibniz International Proceedings in Informatics (LIPIcs), Volume 28, pp. 356-370, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{mittal_et_al:LIPIcs.APPROX-RANDOM.2014.356,
  author =	{Mittal, Shashi and Schulz, Andreas S. and Stiller, Sebastian},
  title =	{{Robust Appointment Scheduling}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014)},
  pages =	{356--370},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-74-3},
  ISSN =	{1868-8969},
  year =	{2014},
  volume =	{28},
  editor =	{Jansen, Klaus and Rolim, Jos\'{e} and Devanur, Nikhil R. and Moore, Cristopher},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2014.356},
  URN =		{urn:nbn:de:0030-drops-47089},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2014.356},
  annote =	{Keywords: Robust Optimization, Health Care Scheduling, Approximation Algorithms}
}
Document
DOI: 10.4230/DagSemProc.09261.27
Sharing Supermodular Costs

Authors: Andreas S. Schulz and Nelson A. Uhan

Published in: Dagstuhl Seminar Proceedings, Volume 9261, Models and Algorithms for Optimization in Logistics (2009)

Abstract
We apply ideas from cooperative game theory to study situations in which a set of agents face supermodular costs. These situations appear in a variety of scheduling contexts, as well as in some settings related to facility location and network design. Intuitively, cooperation amongst rational agents who face supermodular costs is unlikely. However, in circumstances where the failure to cooperate may lead to negative externalities, one might be interested in methods of encouraging cooperation. The least core value of a cooperative game is the minimum penalty we need to charge a coalition for acting independently that encourages cooperation by ensuring the existence of an efficient and stable cost allocation. The set of all such cost allocations is called the least core. In this work, we study the computational complexity and approximability of computing the least core value of supermodular cost cooperative games. We show that computing the least core value of supermodular cost cooperative games is strongly NP-hard, and build a framework to approximate the least core value of these games using oracles that approximately determine maximally violated constraints. This framework yields a $(3+epsilon)$-approximation algorithm for computing the least core value of supermodular cost cooperative games. As a by-product, we show how to compute accompanying approximate least core cost allocations for these games. We also apply our approximation framework to obtain better results for two particular classes of supermodular cost cooperative games that arise from scheduling and matroid optimization.
Cite as

Andreas S. Schulz and Nelson A. Uhan. Sharing Supermodular Costs. In Models and Algorithms for Optimization in Logistics. Dagstuhl Seminar Proceedings, Volume 9261, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{schulz_et_al:DagSemProc.09261.27,
  author =	{Schulz, Andreas S. and Uhan, Nelson A.},
  title =	{{Sharing Supermodular Costs}},
  booktitle =	{Models and Algorithms for Optimization in Logistics},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9261},
  editor =	{Cynthia Barnhart and Uwe Clausen and Ulrich Lauther and Rolf H. M\"{o}hring},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09261.27},
  URN =		{urn:nbn:de:0030-drops-21702},
  doi =		{10.4230/DagSemProc.09261.27},
  annote =	{Keywords: Cooperative games, approximation algorithms, algorithmic game theory}
}
Document
DOI: 10.4230/DagSemProc.05031.18
New Old Algorithms for Stochastic Scheduling

Authors: Andreas S. Schulz

Published in: Dagstuhl Seminar Proceedings, Volume 5031, Algorithms for Optimization with Incomplete Information (2005)

Abstract
We consider the stochastic identical parallel machine scheduling problem and its online extension, when the objective is to minimize the expected total weighted completion time of a set of jobs that are released over time. We give randomized as well as deterministic online and offline algorithms that have the best known performance guarantees in either setting, online or offline and deterministic or randomized. Our analysis is based on a novel linear programming relaxation for stochastic scheduling problems that can be solved online.
Cite as

Andreas S. Schulz. New Old Algorithms for Stochastic Scheduling. In Algorithms for Optimization with Incomplete Information. Dagstuhl Seminar Proceedings, Volume 5031, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{schulz:DagSemProc.05031.18,
  author =	{Schulz, Andreas S.},
  title =	{{New Old Algorithms for Stochastic Scheduling}},
  booktitle =	{Algorithms for Optimization with Incomplete Information},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5031},
  editor =	{Susanne Albers and Rolf H. M\"{o}hring and Georg Ch. Pflug and R\"{u}diger Schultz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05031.18},
  URN =		{urn:nbn:de:0030-drops-1931},
  doi =		{10.4230/DagSemProc.05031.18},
  annote =	{Keywords: stochastic scheduling , online algorithms , competitive analysis , approximation algorithms , linear programming relaxations}
}
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