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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2014
    In:  Journal of Combinatorial Optimization Vol. 27, No. 1 ( 2014-1), p. 164-181
    In: Journal of Combinatorial Optimization, Springer Science and Business Media LLC, Vol. 27, No. 1 ( 2014-1), p. 164-181
    Type of Medium: Online Resource
    ISSN: 1382-6905 , 1573-2886
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
    detail.hit.zdb_id: 2005418-X
    SSG: 3,2
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2020
    In:  Mathematical Programming Vol. 183, No. 1-2 ( 2020-09), p. 215-247
    In: Mathematical Programming, Springer Science and Business Media LLC, Vol. 183, No. 1-2 ( 2020-09), p. 215-247
    Abstract: We study a fundamental online job admission problem where jobs with deadlines arrive online over time at their release dates, and the task is to determine a preemptive single-server schedule which maximizes the number of jobs that complete on time. To circumvent known impossibility results, we make a standard slackness assumption by which the feasible time window for scheduling a job is at least $$1+\varepsilon $$ 1 + ε times its processing time, for some $$\varepsilon 〉 0$$ ε 〉 0 . We quantify the impact that different provider commitment requirements have on the performance of online algorithms. Our main contribution is one universal algorithmic framework for online job admission both with and without commitments. Without commitment, our algorithm with a competitive ratio of  $$\mathcal {O}(1/\varepsilon )$$ O ( 1 / ε ) is the best possible (deterministic) for this problem. For commitment models, we give the first non-trivial performance bounds. If the commitment decisions must be made before a job’s slack becomes less than a $$\delta $$ δ -fraction of its size, we prove a competitive ratio of $$\mathcal {O}(\varepsilon /((\varepsilon -\delta )\delta ^2))$$ O ( ε / ( ( ε - δ ) δ 2 ) ) , for $$0 〈 \delta 〈 \varepsilon $$ 0 〈 δ 〈 ε . When a provider must commit upon starting a job, our bound is  $$\mathcal {O}(1/\varepsilon ^2)$$ O ( 1 / ε 2 ) . Finally, we observe that for scheduling with commitment the restriction to the “unweighted” throughput model is essential; if jobs have individual weights, we rule out competitive deterministic algorithms.
    Type of Medium: Online Resource
    ISSN: 0025-5610 , 1436-4646
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 1463397-8
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  • 3
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Mathematical Programming Vol. 197, No. 2 ( 2023-02), p. 1009-1048
    In: Mathematical Programming, Springer Science and Business Media LLC, Vol. 197, No. 2 ( 2023-02), p. 1009-1048
    Type of Medium: Online Resource
    ISSN: 0025-5610 , 1436-4646
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1463397-8
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  • 4
    Online Resource
    Online Resource
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2011
    In:  INFORMS Journal on Computing Vol. 23, No. 2 ( 2011-05), p. 189-204
    In: INFORMS Journal on Computing, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 23, No. 2 ( 2011-05), p. 189-204
    Abstract: Large-scale maintenance in industrial plants requires the entire shutdown of production units for disassembly, comprehensive inspection, and renewal. We derive models and algorithms for this so-called turnaround scheduling that include different features such as time-cost trade-off, precedence constraints, external resource units, resource leveling, different working shifts, and risk analysis. We propose a framework for decision support that consists of two phases. The first phase supports the manager in finding a good makespan for the turnaround. It computes an approximate project time-cost trade-off curve together with a stochastic evaluation. Our risk measures are the expected tardiness at time t and the probability of completing the turnaround within time t. In the second phase, we solve the actual scheduling optimization problem for the makespan chosen in the first phase heuristically and compute a detailed schedule that respects all side constraints. Again, we complement this by computing upper bounds for the same two risk measures. Our experimental results show that our methods solve large real-world instances from chemical manufacturing plants quickly and yield an excellent resource utilization. A comparison with solutions of a mixed-integer program on smaller instances proves the high quality of the schedules that our algorithms produce within a few minutes.
    Type of Medium: Online Resource
    ISSN: 1091-9856 , 1526-5528
    RVK:
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2011
    detail.hit.zdb_id: 2070411-2
    detail.hit.zdb_id: 2004082-9
    SSG: 3,2
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  • 5
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Annals of Operations Research Vol. 304, No. 1-2 ( 2021-09), p. 85-107
    In: Annals of Operations Research, Springer Science and Business Media LLC, Vol. 304, No. 1-2 ( 2021-09), p. 85-107
    Abstract: We consider a natural generalization of classical scheduling problems to a setting in which using a time unit for processing a job causes some time-dependent cost, the time-of-use tariff, which must be paid in addition to the standard scheduling cost. We focus on preemptive single-machine scheduling and two classical scheduling cost functions, the sum of (weighted) completion times and the maximum completion time, that is, the makespan. While these problems are easy to solve in the classical scheduling setting, they are considerably more complex when time-of-use tariffs must be considered. We contribute optimal polynomial-time algorithms and best possible approximation algorithms. For the problem of minimizing the total (weighted) completion time on a single machine, we present a polynomial-time algorithm that computes for any given sequence of jobs an optimal schedule, i.e., the optimal set of time slots to be used for preemptively scheduling jobs according to the given sequence. This result is based on dynamic programming using a subtle analysis of the structure of optimal solutions and a potential function argument. With this algorithm, we solve the unweighted problem optimally in polynomial time. For the more general problem, in which jobs may have individual weights, we develop a polynomial-time approximation scheme (PTAS) based on a dual scheduling approach introduced for scheduling on a machine of varying speed. As the weighted problem is strongly NP-hard, our PTAS is the best possible approximation we can hope for. For preemptive scheduling to minimize the makespan, we show that there is a comparably simple optimal algorithm with polynomial running time. This is true even in a certain generalized model with unrelated machines.
    Type of Medium: Online Resource
    ISSN: 0254-5330 , 1572-9338
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2021913-1
    SSG: 3,2
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  • 6
    Online Resource
    Online Resource
    Elsevier BV ; 2015
    In:  Discrete Optimization Vol. 15 ( 2015-02), p. 26-36
    In: Discrete Optimization, Elsevier BV, Vol. 15 ( 2015-02), p. 26-36
    Type of Medium: Online Resource
    ISSN: 1572-5286
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 2148551-3
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  • 7
    Online Resource
    Online Resource
    Society for Industrial & Applied Mathematics (SIAM) ; 2022
    In:  SIAM Journal on Discrete Mathematics Vol. 36, No. 2 ( 2022-06), p. 1249-1273
    In: SIAM Journal on Discrete Mathematics, Society for Industrial & Applied Mathematics (SIAM), Vol. 36, No. 2 ( 2022-06), p. 1249-1273
    Type of Medium: Online Resource
    ISSN: 0895-4801 , 1095-7146
    RVK:
    Language: English
    Publisher: Society for Industrial & Applied Mathematics (SIAM)
    Publication Date: 2022
    detail.hit.zdb_id: 1468404-4
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  • 8
    Online Resource
    Online Resource
    Elsevier BV ; 2006
    In:  Theoretical Computer Science Vol. 361, No. 2-3 ( 2006-09), p. 329-341
    In: Theoretical Computer Science, Elsevier BV, Vol. 361, No. 2-3 ( 2006-09), p. 329-341
    Type of Medium: Online Resource
    ISSN: 0304-3975
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2006
    detail.hit.zdb_id: 193706-6
    detail.hit.zdb_id: 1466347-8
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  • 9
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2017
    In:  ACM Transactions on Algorithms Vol. 13, No. 1 ( 2017-01-31), p. 1-34
    In: ACM Transactions on Algorithms, Association for Computing Machinery (ACM), Vol. 13, No. 1 ( 2017-01-31), p. 1-34
    Abstract: We propose a new approach to competitive analysis in online scheduling by introducing the novel concept of competitive-ratio approximation schemes. Such a scheme algorithmically constructs an online algorithm with a competitive ratio arbitrarily close to the best possible competitive ratio for any online algorithm. We study the problem of scheduling jobs online to minimize the weighted sum of completion times on parallel, related, and unrelated machines, and we derive both deterministic and randomized algorithms that are almost best possible among all online algorithms of the respective settings. We also generalize our techniques to arbitrary monomial cost functions and apply them to the makespan objective. Our method relies on an abstract characterization of online algorithms combined with various simplifications and transformations. We also contribute algorithmic means to compute the actual value of the best possible competitive ratio up to an arbitrary accuracy. This strongly contrasts with nearly all previous manually obtained competitiveness results, and, most importantly, it reduces the search for the optimal competitive ratio to a question that a computer can answer. We believe that our concept can also be applied to many other problems and yields a new perspective on online algorithms in general.
    Type of Medium: Online Resource
    ISSN: 1549-6325 , 1549-6333
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2017
    detail.hit.zdb_id: 2198259-4
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  • 10
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2023
    In:  ACM Transactions on Algorithms Vol. 19, No. 1 ( 2023-01-31), p. 1-25
    In: ACM Transactions on Algorithms, Association for Computing Machinery (ACM), Vol. 19, No. 1 ( 2023-01-31), p. 1-25
    Abstract: We consider a fundamental online scheduling problem in which jobs with processing times and deadlines arrive online over time at their release dates. The task is to determine a feasible preemptive schedule on a single or multiple possibly unrelated machines that maximizes the number of jobs that complete before their deadline. Due to strong impossibility results for competitive analysis on a single machine, we require that jobs contain some slack ɛ 〉 0, which means that the feasible time window for scheduling a job is at least 1+ɛ times its processing time on each eligible machine. Our contribution is two-fold: (i) We give the first non-trivial online algorithms for throughput maximization on unrelated machines, and (ii), this is the main focus of our paper, we answer the question on how to handle commitment requirements which enforce that a scheduler has to guarantee at a certain point in time the completion of admitted jobs. This is very relevant, e.g., in providing cloud-computing services, and disallows last-minute rejections of critical tasks. We present an algorithm for unrelated machines that is \(\Theta (\frac{1}{\varepsilon })\) -competitive when the scheduler must commit upon starting a job. Somewhat surprisingly, this is the same optimal performance bound (up to constants) as for scheduling without commitment on a single machine. If commitment decisions must be made before a job’s slack becomes less than a δ-fraction of its size, we prove a competitive ratio of \(\mathcal {O}(\frac{1}{\varepsilon - \delta })\) for 0 〈 δ 〈 ɛ. This result nicely interpolates between commitment upon starting a job and commitment upon arrival. For the latter commitment model, it is known that no (randomized) online algorithm admits any bounded competitive ratio. While we mainly focus on scheduling without migration, our results also hold when comparing against a migratory optimal solution in case of identical machines.
    Type of Medium: Online Resource
    ISSN: 1549-6325 , 1549-6333
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2023
    detail.hit.zdb_id: 2198259-4
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