41,418 research outputs found
Single machine scheduling with job-dependent machine deterioration
We consider the single machine scheduling problem with job-dependent machine
deterioration. In the problem, we are given a single machine with an initial
non-negative maintenance level, and a set of jobs each with a non-preemptive
processing time and a machine deterioration. Such a machine deterioration
quantifies the decrement in the machine maintenance level after processing the
job. To avoid machine breakdown, one should guarantee a non-negative
maintenance level at any time point; and whenever necessary, a maintenance
activity must be allocated for restoring the machine maintenance level. The
goal of the problem is to schedule the jobs and the maintenance activities such
that the total completion time of jobs is minimized. There are two variants of
maintenance activities: in the partial maintenance case each activity can be
allocated to increase the machine maintenance level to any level not exceeding
the maximum; in the full maintenance case every activity must be allocated to
increase the machine maintenance level to the maximum. In a recent work, the
problem in the full maintenance case has been proven NP-hard; several special
cases of the problem in the partial maintenance case were shown solvable in
polynomial time, but the complexity of the general problem is left open. In
this paper we first prove that the problem in the partial maintenance case is
NP-hard, thus settling the open problem; we then design a -approximation
algorithm.Comment: 15 page
Scheduling unit processing time arc shutdown jobs to maximize network flow over time: complexity results
We study the problem of scheduling maintenance on arcs of a capacitated
network so as to maximize the total flow from a source node to a sink node over
a set of time periods. Maintenance on an arc shuts down the arc for the
duration of the period in which its maintenance is scheduled, making its
capacity zero for that period. A set of arcs is designated to have maintenance
during the planning period, which will require each to be shut down for exactly
one time period. In general this problem is known to be NP-hard. Here we
identify a number of characteristics that are relevant for the complexity of
instance classes. In particular, we discuss instances with restrictions on the
set of arcs that have maintenance to be scheduled; series parallel networks;
capacities that are balanced, in the sense that the total capacity of arcs
entering a (non-terminal) node equals the total capacity of arcs leaving the
node; and identical capacities on all arcs
Single machine scheduling with general positional deterioration and rate-modifying maintenance
We present polynomial-time algorithms for single machine problems with generalized positional deterioration effects and machine maintenance. The decisions should be taken regarding possible sequences of jobs and on the number of maintenance activities to be included into a schedule in order to minimize the overall makespan. We deal with general non-decreasing functions to represent deterioration rates of job processing times. Another novel extension of existing models is our assumption that a maintenance activity does not necessarily fully restore the machine to its original perfect state. In the resulting schedules, the jobs are split into groups, a particular group to be sequenced after a particular maintenance period, and the actual processing time of a job is affected by the group that job is placed into and its position within the group
A generic method for energy-efficient and energy-cost-effective production at the unit process level
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
Time4: Time for SDN
With the rise of Software Defined Networks (SDN), there is growing interest
in dynamic and centralized traffic engineering, where decisions about
forwarding paths are taken dynamically from a network-wide perspective.
Frequent path reconfiguration can significantly improve the network
performance, but should be handled with care, so as to minimize disruptions
that may occur during network updates.
In this paper we introduce Time4, an approach that uses accurate time to
coordinate network updates. Time4 is a powerful tool in softwarized
environments, that can be used for various network update scenarios.
Specifically, we characterize a set of update scenarios called flow swaps, for
which Time4 is the optimal update approach, yielding less packet loss than
existing update approaches. We define the lossless flow allocation problem, and
formally show that in environments with frequent path allocation, scenarios
that require simultaneous changes at multiple network devices are inevitable.
We present the design, implementation, and evaluation of a Time4-enabled
OpenFlow prototype. The prototype is publicly available as open source. Our
work includes an extension to the OpenFlow protocol that has been adopted by
the Open Networking Foundation (ONF), and is now included in OpenFlow 1.5. Our
experimental results show the significant advantages of Time4 compared to other
network update approaches, and demonstrate an SDN use case that is infeasible
without Time4.Comment: This report is an extended version of "Software Defined Networks:
It's About Time", which was accepted to IEEE INFOCOM 2016. A preliminary
version of this report was published in arXiv in May, 201
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