21,329 research outputs found
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
Optimal Composition Ordering Problems for Piecewise Linear Functions
In this paper, we introduce maximum composition ordering problems. The input
is real functions and a constant
. We consider two settings: total and partial compositions. The
maximum total composition ordering problem is to compute a permutation
which maximizes , where .
The maximum partial composition ordering problem is to compute a permutation
and a nonnegative integer which maximize
.
We propose time algorithms for the maximum total and partial
composition ordering problems for monotone linear functions , which
generalize linear deterioration and shortening models for the time-dependent
scheduling problem. We also show that the maximum partial composition ordering
problem can be solved in polynomial time if is of form
for some constants , and . We
finally prove that there exists no constant-factor approximation algorithm for
the problems, even if 's are monotone, piecewise linear functions with at
most two pieces, unless P=NP.Comment: 19 pages, 4 figure
Combining time and position dependent effects on a single machine subject to rate-modifying activities
We introduce a general model for single machine scheduling problems, in which the actual processing times of jobs are subject to a combination of positional and time-dependent effects, that are job-independent but additionally depend on certain activities that modify the processing rate of the machine, such as, maintenance. We focus on minimizing two classical objectives: the makespan and the sum of the completion times. The traditional classification accepted in this area of scheduling is based on the distinction between the learning and deterioration effects on one hand, and between the positional effects and the start-time dependent effects on the other hand. Our results show that in the framework of the introduced model such a classification is not necessary, as long as the effects are job-independent. The model introduced in this paper covers most of the previously known models. The solution algorithms are developed within the same general framework and their running times are no worse than those available earlier for problems with less general effects
Some scheduling problems with deteriorating jobs and learning effects
Author name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Approximation schemes for scheduling on a single machine subject to cumulative deterioration and maintenance
We consider a scheduling problem on a single machine to minimize the makespan. The processing conditions are subject to cumulative deterioration, but can be restored by a single maintenance. We link the problem to the Subset-sum problem (if the duration of maintenance is constant) and to the Half-Product Problem (if the duration of maintenance depends on its start time). For both versions of the problem, we adapt the existing fully polynomial-time approximation schemes to our problems by handling the additive constants
A Novel Approach to the Common Due-Date Problem on Single and Parallel Machines
This paper presents a novel idea for the general case of the Common Due-Date
(CDD) scheduling problem. The problem is about scheduling a certain number of
jobs on a single or parallel machines where all the jobs possess different
processing times but a common due-date. The objective of the problem is to
minimize the total penalty incurred due to earliness or tardiness of the job
completions. This work presents exact polynomial algorithms for optimizing a
given job sequence for single and identical parallel machines with the run-time
complexities of for both cases, where is the number of jobs.
Besides, we show that our approach for the parallel machine case is also
suitable for non-identical parallel machines. We prove the optimality for the
single machine case and the runtime complexities of both. Henceforth, we extend
our approach to one particular dynamic case of the CDD and conclude the chapter
with our results for the benchmark instances provided in the OR-library.Comment: Book Chapter 22 page
Common Due-Date Problem: Exact Polynomial Algorithms for a Given Job Sequence
This paper considers the problem of scheduling jobs on single and parallel
machines where all the jobs possess different processing times but a common due
date. There is a penalty involved with each job if it is processed earlier or
later than the due date. The objective of the problem is to find the assignment
of jobs to machines, the processing sequence of jobs and the time at which they
are processed, which minimizes the total penalty incurred due to tardiness or
earliness of the jobs. This work presents exact polynomial algorithms for
optimizing a given job sequence or single and parallel machines with the
run-time complexities of and respectively, where
is the number of jobs and the number of machines. The algorithms take a
sequence consisting of all the jobs as input and
distribute the jobs to machines (for ) along with their best completion
times so as to get the least possible total penalty for this sequence. We prove
the optimality for the single machine case and the runtime complexities of
both. Henceforth, we present the results for the benchmark instances and
compare with previous work for single and parallel machine cases, up to
jobs.Comment: 15th International Symposium on Symbolic and Numeric Algorithms for
Scientific Computin
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