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This heuristic has been applied with Genetic Algorithm (GA) for further minimization of makespan.
The data mining is for mining the knowledge of job scheduling about the objective of minimization of makespan in a flow shop environment.
Although these models establish advantages over discrete-time representations, they are still inefficient in solving moderate-size problems, such as maximization of profit in long horizon, and minimization of makespan.
In particular, the application of differential evolution algorithm to minimization of makespan, flowtime and tardiness in a flow shop manufacturing system is given in order to illustrate the capabilities and the practical use of the method.
Inspired by the phenomenon of teaching and learning introduced by the teaching-learning based optimization (TLBO) algorithm, this paper presents a hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism (HDTPL) to solve the no-wait flow shop scheduling (NWFSSP) with minimization of makespan.
As objective function, the minimization of makespan was pursued.
Similar(52)
The objective is the minimization of the makespan.
The optimization criterion considered is the minimization of the makespan.
Two objectives, namely, the minimization of the makespan and the energy consumptions, are considered simultaneously.
Three models are then established to sequence the container handling tasks under the minimization of the makespan.
Ronconi [18] analyzed the minimization of the makespan criterion for the flowshop problem with blocking by proposing constructive heuristics, namely MM, MME and PFE.
More suggestions(15)
minimization of research
minimization of pain
minimization of tax
minimization of sweating
minimization of congestion
minimization of distortion
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minimization of crossover
minimization of formation
minimization of noise
minimization of waste
minimization of power
minimization of surface
minimization of side
minimization of system
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