Simulation and modelling of hybrid heuristics distribution algorithm on flow shop scheduling problem to optimize makespan in an Indian manufacturing industry
This study presents a heuristic formulation of the flow shop scheduling problem by the hybrid algorithm fitness function. The genetic algorithm is used to model the time-estimates such as makespan and completion time. This paper aims to optimize the sequence-independent and sequence-dependent time-estimates. The production scheduling parameters such as permutation, non-permutation, no-wait, tardiness, and several workstations are identified from a piston manufacturing industry, in Northern India. The MATLAB programming in heuristics algorithm distribution function resulted in reduction of makespan of the product by five times. The reduced completion time is 23 minutes for the piston ring product and 26 minutes in cumulative validation of the proposed model. The cumulative optimized standard error of 0.26; (n=3) simulate and synthesize the suggested model with its validation. The system efficiency through completion time optimization ranged from 70-82 percent in piston ring, and 63-89 percent in cumulative validation of the model has been worked out for each machine type. The data generated through system optimization helps the scientific world and entrepreneurs in advancement of sequence-based transportation.
Completion time; Genetic algorithm; Meta-heuristics; Non-permutation; Permutation; Sequence-based
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