24,199 research outputs found
Approximation Results for Preemptive Stochastic Online Scheduling
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.computer science applications;
Project scheduling under undertainty â survey and research potentials.
The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;
Dynamic resource constrained multi-project scheduling problem with weighted earliness/tardiness costs
In this study, a conceptual framework is given for the dynamic multi-project scheduling problem with weighted earliness/tardiness costs (DRCMPSPWET) and a mathematical programming formulation of the problem is provided. In DRCMPSPWET, a project arrives on top of an existing project portfolio and a due date has to be quoted for the new project while minimizing the costs of schedule changes. The objective function consists of the weighted earliness tardiness costs of the activities of the existing projects in the current baseline schedule plus a term that increases linearly with the anticipated completion time of the new project. An iterated local search based approach is developed for large instances of this problem. In order to analyze the performance and behavior of the proposed method, a new multi-project data set is created by controlling the total number of activities, the due date tightness, the due date range, the number of resource types, and the completion time factor in an instance. A series of computational experiments are carried out to test the performance of the local search approach. Exact solutions are provided for the small instances. The results indicate that the local search heuristic performs well in terms of both solution quality and solution time
Solution and quality robust project scheduling: a methodological framework.
The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a methodological framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of Critical Chain scheduling and indicate in which environments it is useful.Framework; Information; Management; Processes; Project management; Project scheduling; Project scheduling under uncertainty; Stability; Robust scheduling; Quality; Scheduling; Stability; Uncertainty;
Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments
In the current work we introduce a novel estimation of distribution algorithm
to tackle a hard combinatorial optimization problem, namely the single-machine
scheduling problem, with uncertain delivery times. The majority of the existing
research coping with optimization problems in uncertain environment aims at
finding a single sufficiently robust solution so that random noise and
unpredictable circumstances would have the least possible detrimental effect on
the quality of the solution. The measures of robustness are usually based on
various kinds of empirically designed averaging techniques. In contrast to the
previous work, our algorithm aims at finding a collection of robust schedules
that allow for a more informative decision making. The notion of robustness is
measured quantitatively in terms of the classical mathematical notion of a norm
on a vector space. We provide a theoretical insight into the relationship
between the properties of the probability distribution over the uncertain
delivery times and the robustness quality of the schedules produced by the
algorithm after a polynomial runtime in terms of approximation ratios
Synthesis and Stochastic Assessment of Cost-Optimal Schedules
We present a novel approach to synthesize good schedules for a class
of scheduling problems that is slightly more general than the
scheduling problem FJm,a|gpr,r_j,d_j|early/tardy. The idea is to prime
the schedule synthesizer with stochastic information more meaningful
than performance factors with the objective to minimize the expected
cost caused by storage or delay. The priming information is
obtained by stochastic simulation of the system environment. The generated
schedules are assessed again by simulation. The approach is
demonstrated by means of a non-trivial scheduling problem from
lacquer production. The experimental results show that our approach
achieves in all considered scenarios better results than the
extended processing times approach
Sample Efficient Policy Search for Optimal Stopping Domains
Optimal stopping problems consider the question of deciding when to stop an
observation-generating process in order to maximize a return. We examine the
problem of simultaneously learning and planning in such domains, when data is
collected directly from the environment. We propose GFSE, a simple and flexible
model-free policy search method that reuses data for sample efficiency by
leveraging problem structure. We bound the sample complexity of our approach to
guarantee uniform convergence of policy value estimates, tightening existing
PAC bounds to achieve logarithmic dependence on horizon length for our setting.
We also examine the benefit of our method against prevalent model-based and
model-free approaches on 3 domains taken from diverse fields.Comment: To appear in IJCAI-201
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