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Particle swarm optimization for integer programming
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Computers and Operations Research
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
PSO Combined with ILS for Flowshop-Based Parallel Task Assignment Problem
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Optimizing material procurement planning problem by two-stage fuzzy programming
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A real-integer-discrete-coded particle swarm optimization for design problems
Applied Soft Computing
Sequential metamodelling with genetic programming and particle swarms
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A hybrid meta-heuristic algorithm for optimization of crew scheduling
Applied Soft Computing
Parallel-machine scheduling to minimize tardiness penalty and power cost
Computers and Industrial Engineering
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A particle swarm optimizer for grouping problems
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Minimizing resource consumption on uniform parallel machines with a bound on makespan
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Computers and Electrical Engineering
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As a novel evolutionary technique, particle swarm optimization (PSO) has received increasing attention and wide applications in a variety of fields. To our knowledge this paper investigates the first application of PSO algorithm to tackle the parallel machines scheduling problem. Proposing equations analogous to those of the classical PSO equations, we present a discrete PSO algorithm (DPSO) to minimize makespan (C"m"a"x) criterion. We also investigate the effectiveness of DPSO algorithm through hybridizing it with an efficient local search heuristic. To verify the performance of DPSO algorithm and its hybridized version (HDPSO), comparisons are made through using a recently proposed simulated annealing algorithm for the problem, addressed in the literature, as a comparator algorithm. Computational results signify that the proposed DPSO algorithm is very competitive and can be rapidly guided when hybridizing with a local search heuristic.