11 results match your criteria Annals Of Operations Research[Journal]

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Integer programming model extensions for a multi-stage nurse rostering problem.

Ann Oper Res 2019 1;275(1):123-143. Epub 2017 Sep 1.

Database and Artificial Intelligence Group, Vienna University of Technology, Vienna, Austria.

In the variant of the well studied nurse rostering problem proposed in the Second International Nurse Rostering Competition, multiple stages have to be solved sequentially which are dependent on each other. We propose an integer programming model for this problem and show that a set of newly developed extensions in the form of additional constraints to deal with the incomplete information can significantly improve the quality of the generated solutions. We compare our solution approaches with the results obtained in the competition and show that the extended model achieves results competitive with the competition finalists. Read More

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http://dx.doi.org/10.1007/s10479-017-2623-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394597PMC
September 2017

Modeling and solving staff scheduling with partial weighted maxSAT.

Ann Oper Res 2019 7;275(1):79-99. Epub 2017 Nov 7.

Database and Artificial Intelligence Group, Vienna University of Technology, Vienna, Austria.

Employee scheduling is a well known problem that appears in a wide range of different areas including health care, air lines, transportation services, and basically any organization that has to deal with workforces. In this paper we model a collection of challenging staff scheduling instances as a weighted partial Boolean maximum satisfiability (maxSAT) problem. Using our formulation we conduct a comparison of four different cardinality constraint encodings and analyze their applicability on this problem. Read More

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http://dx.doi.org/10.1007/s10479-017-2693-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394591PMC
November 2017

Optimal spare parts management for vessel maintenance scheduling.

Ann Oper Res 2019 1;272(1):323-353. Epub 2018 Jun 1.

3Department of Mathematics, Centre for Operational Research and Logistics (CORL), University of Portsmouth, Portsmouth, PO1 3HF UK.

Condition-based monitoring is used as part of predictive maintenance to collect real-time information on the healthy status of a vessel engine, which allows for a more accurate estimation of the remaining life of an engine or its parts, as well as providing a warning for a potential failure of an engine part. An engine failure results in delays and down-times in the voyage of a vessel, which translates into additional cost and penalties. This paper studies a spare part management problem for maintenance scheduling of a vessel operating on a given route that is defined by a sequence of port visits. Read More

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http://dx.doi.org/10.1007/s10479-018-2907-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394336PMC

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports.

Ann Oper Res 2019 22;272(1):69-98. Epub 2017 May 22.

1Department of Mathematical Sciences, University of Essex, Colchester, UK.

This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Read More

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http://dx.doi.org/10.1007/s10479-017-2539-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394332PMC

The multi-stripe travelling salesman problem.

Ann Oper Res 2017 18;259(1):21-34. Epub 2017 May 18.

Lehrstuhl für Informatik 1, RWTH Aachen, 52056 Aachen, Germany.

In the classical Travelling Salesman Problem (TSP), the objective function sums the costs for travelling from one city to the next city along the tour. In the -stripe TSP with [Formula: see text], the objective function sums the costs for travelling from one city to each of the next cities in the tour. The resulting -stripe TSP generalizes the TSP and forms a special case of the quadratic assignment problem. Read More

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http://dx.doi.org/10.1007/s10479-017-2513-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5691149PMC

Modeling high school timetabling with bitvectors.

Ann Oper Res 2017 22;252(2):215-238. Epub 2016 Jul 22.

Database and Artificial Intelligence Group, Technische Universität Wien, Vienna, Austria.

High school timetabling (HSTT) is a well known and wide spread problem. The problem consists of coordinating resources (e.g. Read More

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http://dx.doi.org/10.1007/s10479-016-2220-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411413PMC
July 2016
26 Reads

Developing policy analytics for public health strategy and decisions-the Sheffield alcohol policy model framework.

Ann Oper Res 2016;236:149-176. Epub 2013 Oct 8.

School of Health and Related Research (ScHARR), The University of Sheffield, Regent Court, Regent Street, Sheffield, S1 4DA UK.

This paper sets out the development of a methodological framework for detailed evaluation of public health strategies for alcohol harm reduction to meet UK policy-makers needs. Alcohol is known to cause substantial harms, and controlling its affordability and availability are effective policy options. Analysis and synthesis of a variety of public and commercial data sources is needed to evaluate impact on consumers, health services, crime, employers and industry, so a sound evaluation of impact is important. Read More

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http://dx.doi.org/10.1007/s10479-013-1451-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710054PMC
October 2013
5 Reads

Distribution-dependent robust linear optimization with applications to inventory control.

Ann Oper Res 2015 Aug;231(1):229-263

Department of Electrical & Computer Eng. and Division of Systems Eng., Boston University, Boston, MA 02215, USA, , url: http://ionia.bu.edu/

This paper tackles linear programming problems with data uncertainty and applies it to an important inventory control problem. Each element of the constraint matrix is subject to uncertainty and is modeled as a random variable with a bounded support. The classical robust optimization approach to this problem yields a solution with guaranteed feasibility. Read More

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http://dx.doi.org/10.1007/s10479-013-1467-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4560268PMC
August 2015
1 Read

Evader Interdiction: Algorithms, Complexity and Collateral Damage.

Ann Oper Res 2014 Nov;222(1):341-359

Cornell University.

In network interdiction problems, evaders (e.g., hostile agents or data packets) are moving through a network toward targets and we wish to choose locations for sensors in order to intercept the evaders. Read More

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http://dx.doi.org/10.1007/s10479-013-1372-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201385PMC
November 2014

Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories.

Ann Oper Res 2013 Sep;208(1):383-416

University of Liége, Belgium.

In this paper, we consider the batch mode reinforcement learning setting, where the central problem is to learn from a sample of trajectories a policy that satisfies or optimizes a performance criterion. We focus on the continuous state space case for which usual resolution schemes rely on function approximators either to represent the underlying control problem or to represent its value function. As an alternative to the use of function approximators, we rely on the synthesis of "artificial trajectories" from the given sample of trajectories, and show that this idea opens new avenues for designing and analyzing algorithms for batch mode reinforcement learning. Read More

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http://dx.doi.org/10.1007/s10479-012-1248-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3773886PMC
September 2013
5 Reads

Adaptive importance sampling for network growth models.

Ann Oper Res 2011 Sep;189(1):187-203

Stanford University, Stanford, CA, USA.

Network Growth Models such as Preferential Attachment and Duplication/Divergence are popular generative models with which to study complex networks in biology, sociology, and computer science. However, analyzing them within the framework of model selection and statistical inference is often complicated and computationally difficult, particularly when comparing models that are not directly related or nested. In practice, ad hoc methods are often used with uncertain results. Read More

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http://dx.doi.org/10.1007/s10479-010-0685-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4863242PMC
September 2011
7 Reads
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