Particle Swarm Optimization for Parallel MachineScheduling Problem with Machine EligibilityConstraints
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Graphical Abstract
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Abstract
Particle swarm optimization is a popular
global optimization technology in continuous and discrete
optimization ¯elds in recent years. This paper presents
a particle swarm optimization-based scheduling algorithm
for large-scale parallel machine scheduling problem with
machine eligibility constraints and the objective of min-
imizing the total weighted tardiness. In the proposed
method, we ¯rst design a Machine assignment heuristic
(MAH) which is used to assign a processing machine of
each job based on the dynamic load-balancing mechanism,
and then we propose a particle swarm optimization algo-
rithm to optimize the sequence of all jobs in which MAH
is used in the decoding process of each particle, also, the
mechanisms of particle-moving and velocity-updating are
devised based on the problem characteristics. Numerical
computational results show that the proposed algorithm is
e®ective for large-scale parallel machine scheduling prob-
lems with machine eligibility constraints.
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