Megow, Nicole
Overview
Works:  8 works in 12 publications in 1 language and 47 library holdings 

Roles:  Author 
Publication Timeline
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Most widely held works by
Nicole Megow
Coping with incomplete information in scheduling : stochastic and online models by
Nicole Megow(
)
2 editions published in 2007 in English and held by 19 WorldCat member libraries worldwide
2 editions published in 2007 in English and held by 19 WorldCat member libraries worldwide
Models and algorithms for stochastic online scheduling(
)
1 edition published in 2005 in English and held by 16 WorldCat member libraries worldwide
1 edition published in 2005 in English and held by 16 WorldCat member libraries worldwide
Approximation results for preemptive stochastic online scheduling by
Nicole Megow(
Book
)
3 editions published in 2006 in English and held by 5 WorldCat member libraries worldwide
We present first constant performance guarantees for preemptive stochastic scheduling to minimize the sum of weighted completion times. For scheduling jobs with release dates on identical parallel machines we derive policies with a guaranteed performance ratio of 2 which matches the currently best known result for the corresponding deterministic online problem. Our policies apply to the recently introduced stochastic online scheduling model inwhich jobs arrive online over time. In contrast to the previously considered nonpreemptivesetting, our preemptive policies extensively utilize information on processing time distributions other than the first (and second) moments. In order to derive our results we introduce a new nontrivial lower bound on the expected value of an unknown optimal policy that we derive from an optimal policy for the basic problem on a single machine without release dates. This problem is known to be solved optimally by a Gittins index priority rule. This priority index also inspires the design of our policies
3 editions published in 2006 in English and held by 5 WorldCat member libraries worldwide
We present first constant performance guarantees for preemptive stochastic scheduling to minimize the sum of weighted completion times. For scheduling jobs with release dates on identical parallel machines we derive policies with a guaranteed performance ratio of 2 which matches the currently best known result for the corresponding deterministic online problem. Our policies apply to the recently introduced stochastic online scheduling model inwhich jobs arrive online over time. In contrast to the previously considered nonpreemptivesetting, our preemptive policies extensively utilize information on processing time distributions other than the first (and second) moments. In order to derive our results we introduce a new nontrivial lower bound on the expected value of an unknown optimal policy that we derive from an optimal policy for the basic problem on a single machine without release dates. This problem is known to be solved optimally by a Gittins index priority rule. This priority index also inspires the design of our policies
Stochastic online scheduling on parallel machines by
Nicole Megow(
Book
)
2 editions published in 2004 in English and held by 4 WorldCat member libraries worldwide
2 editions published in 2004 in English and held by 4 WorldCat member libraries worldwide
Approximating preemptive stochastic scheduling by
Nicole Megow(
)
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
We present constant approximative policies for preemptive stochastic scheduling. We derive policies with a guaranteed performance ratio of 2 for scheduling jobs with release dates on identical parallel machines subject to minimizing the sum of weighted completion times. Our policies as well as their analysis apply also to the recently introduced more general model of stochastic online scheduling. The performance guarantee we give matches the best result known for the corresponding deterministic online problem. In contrast to previous results for nonpreemptive stochastic scheduling, our preemptive policies yield an approximation guarantee that is independent of the processing time distributions. However, our policies extensively utilize information on the distributions other than the first (and second) moments. To obtain our results, we introduce a new nontrivial lower bound on the expected value of an unknown optimal policy. It relies on a relaxation to the basic problem on a single machine without release dates, which is known to be solved optimally by the Gittins index priority rule. This dynamic priority index is crucial to the analysis and also inspires the design of our policies
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
We present constant approximative policies for preemptive stochastic scheduling. We derive policies with a guaranteed performance ratio of 2 for scheduling jobs with release dates on identical parallel machines subject to minimizing the sum of weighted completion times. Our policies as well as their analysis apply also to the recently introduced more general model of stochastic online scheduling. The performance guarantee we give matches the best result known for the corresponding deterministic online problem. In contrast to previous results for nonpreemptive stochastic scheduling, our preemptive policies yield an approximation guarantee that is independent of the processing time distributions. However, our policies extensively utilize information on the distributions other than the first (and second) moments. To obtain our results, we introduce a new nontrivial lower bound on the expected value of an unknown optimal policy. It relies on a relaxation to the basic problem on a single machine without release dates, which is known to be solved optimally by the Gittins index priority rule. This dynamic priority index is crucial to the analysis and also inspires the design of our policies
Scheduling to minimize average completion time revisited : deterministic online algorithms by
Nicole Megow(
Book
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
We consider the scheduling problem of minimizing the average weighted completion time on identical parallel machines when jobs are arriving over time. For both the preemptive and the nonpreemptive setting, we show that straightforward extensions of Smith's ratio rule yield smaller competitive ratios compared to the previously bestknown deterministic online algorithms, which are (4+epsilon)competitive in either case. Our preemptive algorithm is 2competitive, which actually meets the competitive ratio of the currently best randomized online algorithm for this scenario. Our nonpreemptive algorithm has a competitive ratio of 3.28. Both results are characterized by a surprisingly simple analysis; moreover, the preemptive algorithm also works in the less clairvoyant environment in which only the ratio of weight to processing time of a job becomes known at its release date, but neither its actual weight nor its processing time. In the corresponding nonpreemptive situation, every online algorithm has an unbounded competitive ratio. Keywords: Scheduling, Sequencing, Approximation Algorithms, Online Algorithms, Competitive Ratio
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
We consider the scheduling problem of minimizing the average weighted completion time on identical parallel machines when jobs are arriving over time. For both the preemptive and the nonpreemptive setting, we show that straightforward extensions of Smith's ratio rule yield smaller competitive ratios compared to the previously bestknown deterministic online algorithms, which are (4+epsilon)competitive in either case. Our preemptive algorithm is 2competitive, which actually meets the competitive ratio of the currently best randomized online algorithm for this scenario. Our nonpreemptive algorithm has a competitive ratio of 3.28. Both results are characterized by a surprisingly simple analysis; moreover, the preemptive algorithm also works in the less clairvoyant environment in which only the ratio of weight to processing time of a job becomes known at its release date, but neither its actual weight nor its processing time. In the corresponding nonpreemptive situation, every online algorithm has an unbounded competitive ratio. Keywords: Scheduling, Sequencing, Approximation Algorithms, Online Algorithms, Competitive Ratio
Approximating preemptive stochastic scheduling by
Nicole Megow(
)
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
The Online Target Date Assignment Problem by
Stefan Heinz(
Book
)
1 edition published in 2005 in English and held by 0 WorldCat member libraries worldwide
Many online problems encountered in reallife involve a twostage decision process: upon arrival of a new request, an irrevocable firststage decision (the assignment of a specific resource to the request) must be made immediately, while in a second stage process, certain subinstances (that is, the instances of all requests assigned to a particular resource) can be solved to optimality (offline) later. We introduce the novel concept of an Online Target Date Assignment Problem (ONLINETDAP) as a general framework for online problems with this nature. Requests for the ONLINETDAP become known at certain dates. An online algorithm has to assign a target date to each request, specifying on which date the request should be processed (e.g., an appointment with a customer for a washing machine repair). The cost at a target date is given by the downstream cost, the optimal cost of processing all requests at that date w.r.t. some fixed downstream offline optimization problem (e.g., the cost of an optimal dispatch for service technicians). We provide general competitive algorithms for the ONLINETDAP independently of the particular downstream problem, when the overall objective is to minimize either the sum or the maximum of all downstream costs
1 edition published in 2005 in English and held by 0 WorldCat member libraries worldwide
Many online problems encountered in reallife involve a twostage decision process: upon arrival of a new request, an irrevocable firststage decision (the assignment of a specific resource to the request) must be made immediately, while in a second stage process, certain subinstances (that is, the instances of all requests assigned to a particular resource) can be solved to optimality (offline) later. We introduce the novel concept of an Online Target Date Assignment Problem (ONLINETDAP) as a general framework for online problems with this nature. Requests for the ONLINETDAP become known at certain dates. An online algorithm has to assign a target date to each request, specifying on which date the request should be processed (e.g., an appointment with a customer for a washing machine repair). The cost at a target date is given by the downstream cost, the optimal cost of processing all requests at that date w.r.t. some fixed downstream offline optimization problem (e.g., the cost of an optimal dispatch for service technicians). We provide general competitive algorithms for the ONLINETDAP independently of the particular downstream problem, when the overall objective is to minimize either the sum or the maximum of all downstream costs
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Nicole Megow German discrete mathematician and theoretical computer scientist
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