scholarly journals Online Algorithms for a Generalized Parallel Machine Scheduling Problem

MACRo 2015 ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 193-200
Author(s):  
István Szalkai ◽  
György Dósa
Keyword(s):  
Online Algorithms ◽  
Parallel Machines ◽  
Heuristic Algorithms ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
The Other ◽  
Workflow Scheduling ◽  
Scheduling Problem ◽  
Parallel Machine Scheduling Problem

AbstractWe consider different online algorithms for a generalized scheduling problem for parallel machines, described in details in the second section. This problem is the generalization of the classical parallel machine scheduling problem, when the makespan is minimized; in that case each job contains only one task. On the other hand, the problem in consideration is still a special version of the workflow scheduling problem. We present several heuristic algorithms and compare them by computer tests.

ONLINE ALGORITHMS FOR SCHEDULING WITH MACHINE ACTIVATION COST

2007 ◽  
Vol 24 (02) ◽  
pp. 263-277 ◽  
Author(s):  
YONG HE ◽  
SHUGUANG HAN ◽  
YIWEI JIANG
Keyword(s):  
Lower Bound ◽  
Online Algorithms ◽  
Competitive Ratio ◽  
Online Algorithm ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problem ◽  
Identical Machines ◽  
Parallel Machine Scheduling Problem

In this paper, we consider a variant of the classical parallel machine scheduling problem. For this problem, we are given m potential identical machines to non-preemptively process a sequence of independent jobs. Machines need to be activated before starting to process, and each machine activated incurs a fixed machine activation cost. No machines are initially activated, and when a job is revealed the algorithm has the option to activate new machines. The objective is to minimize the sum of the makespan and activation cost of machines. We first present two optimal online algorithms with competitive ratios of 3/2 and 5/3 for m = 2, 3 cases, respectively. Then we present an online algorithm with a competitive ratio of at most 2 for general m ≥ 4, while the lower bound is 1.88.

BicriteriaHeuristic for Unrelated Parallel Machine Scheduling Problem

2014 ◽  
Vol 1016 ◽  
pp. 824-828
Author(s):  
Yang Kuei Lin ◽  
Hao Chen Lin
Keyword(s):  
Parallel Machines ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Release Dates ◽  
Scheduling Problem ◽  
Unrelated Parallel Machines ◽  
Weighted Tardiness ◽  
Unrelated Parallel Machine Scheduling ◽  
Parallel Machine Scheduling Problem

In this research, a bi-criteria heuristic is proposed to find non-dominated solutions for scheduling unrelated parallel machines with release dates that minimizes makespan andtotal weighted tardiness.

scholarly journals IDENTICAL PARALLEL MACHINES SCHEDULING USING GENETIC ALGORITHM

2018 ◽  
Vol 18 (2) ◽  
pp. 321-330
Author(s):  
Aseel J Haleel
Keyword(s):  
Genetic Algorithm ◽  
Processing Time ◽  
Parallel Machines ◽  
Machine Scheduling ◽  
Setup Time ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problem ◽  
Identical Parallel Machine ◽  
Parallel Machine Scheduling Problem

Minimizing the scheduling production time consider one of the most important factors forcompanies which their objectives is achieve the maximum profits. This paper studies theidentical parallel machine scheduling problem which involves the assignment numbers ofjob (N) to set of identical parallel machine (M) in order to minimize the makespan(maximum completion time of all job). There are numerous troubles in solving the largesize of “parallel machine scheduling” problem with an excessive jobs and machines, sothe genetic algorithm was proposed in this paper which is consider an efficient algorithmthat fits larger size of identical “parallel machine scheduling” for minimizing themakespan. Most studies in the scheduling field suppose setup time is insignificant orincluded in the processing time, in this paper both the sequence independent setup timesand processing time were considered. The solutions of algorithms are coding in(MATLAB). A numerical example of (11) jobs are schedule on (3) machines todemonstrative the effectiveness of algorithm solution. The result show the algorithm caneffectively solve large size of scheduling problem and given the best schedule withminimum makespan.

Sign in / Sign up

Export Citation Format

Share Document