5 research outputs found
Energy Efficient Scheduling and Routing via Randomized Rounding
We propose a unifying framework based on configuration linear programs and
randomized rounding, for different energy optimization problems in the dynamic
speed-scaling setting. We apply our framework to various scheduling and routing
problems in heterogeneous computing and networking environments. We first
consider the energy minimization problem of scheduling a set of jobs on a set
of parallel speed scalable processors in a fully heterogeneous setting. For
both the preemptive-non-migratory and the preemptive-migratory variants, our
approach allows us to obtain solutions of almost the same quality as for the
homogeneous environment. By exploiting the result for the
preemptive-non-migratory variant, we are able to improve the best known
approximation ratio for the single processor non-preemptive problem.
Furthermore, we show that our approach allows to obtain a constant-factor
approximation algorithm for the power-aware preemptive job shop scheduling
problem. Finally, we consider the min-power routing problem where we are given
a network modeled by an undirected graph and a set of uniform demands that have
to be routed on integral routes from their sources to their destinations so
that the energy consumption is minimized. We improve the best known
approximation ratio for this problem.Comment: 27 page
Structural Properties of an Open Problem in Preemptive Scheduling
Structural properties of optimal preemptive schedules have been studied in a
number of recent papers with a primary focus on two structural parameters: the
minimum number of preemptions necessary, and a tight lower bound on `shifts',
i.e., the sizes of intervals bounded by the times created by preemptions, job
starts, or completions. So far only rough bounds for these parameters have been
derived for specific problems. This paper sharpens the bounds on these
structural parameters for a well-known open problem in the theory of preemptive
scheduling: Instances consist of in-trees of unit-execution-time jobs with
release dates, and the objective is to minimize the total completion time on
two processors. This is among the current, tantalizing `threshold' problems of
scheduling theory: Our literature survey reveals that any significant
generalization leads to an NP-hard problem, but that any significant
simplification leads to tractable problem.
For the above problem, we show that the number of preemptions necessary for
optimality need not exceed ; that the number must be of order
for some instances; and that the minimum shift need not be
less than . These bounds are obtained by combinatorial analysis of
optimal schedules rather than by the analysis of polytope corners for
linear-program formulations, an approach to be found in earlier papers. The
bounds immediately follow from a fundamental structural property called
`normality', by which minimal shifts of a job are exponentially decreasing
functions. In particular, the first interval between a preempted job's start
and its preemption is a multiple of 1/2, the second such interval is a multiple
of 1/4, and in general, the -th preemption occurs at a multiple of .
We expect the new structural properties to play a prominent role in finally
settling a vexing, still-open question of complexity
Modeling and Solution Procedure for a Preemptive Multi-Objective Multi-Mode Project Scheduling Model in Resource Investment Problems
In this paper, a preemptive multi-objective multi-mode project scheduling model for resource investment problem is proposed. The first objective function is to minimize the completion time of project (makespan);the second objective function is to minimize the cost of using renewable resources. Non-renewable resources are also considered as parameters in this model. The preemption of activities is allowed at any integer time units, and for each activity, the best execution mode is selected according to the duration and resource. Since this bi-objective problem is the extension of the resource-constrained project scheduling problem (RCPSP), it is NP-hard problem, and therefore, heuristic and metaheuristic methods are required to solve it. In this study, Non-dominated Sorting Genetic AlgorithmII (NSGA-II) and Non-dominated Ranking Genetic Algorithm (NRGA) are used based on results of Pareto solution set.We also present a heuristic method for two approaches of serial schedule generation scheme (S-SGS) and parallel schedule generation scheme (P-SGS) in the developed algorithm in order to optimize the scheduling of the activities.The input parameters of the algorithm are tuned with Response Surface Methodology (RSM). Finally, the algorithms are implemented on some numerical test problems, and their effectiveness is evaluated. </em
Properties of optimal schedules in preemptive shop scheduling
International audienceIn this work we show that certain classical preemptive shop scheduling problems with integral data satisfy the following integer preemption property: there exists an optimal preemptive schedule where all interruptions and all starting and completion times occur at integral dates. We also give new upper bounds on the minimal number of interruptions for various shop scheduling problems