| The increasingly fierce competition in the global market,coupled.with the promotion of industrial strategy of 4.0,drives the manufacturing industry from large-scale single-species to large-scale small batch of changes which is the focus of strategic issues.Based on less changing existing production facilities,mixed model assembly line helps manufacturers reducing the cost,maintaining quality and fulfilling customization with a reasonable combination of the resource and scheduling optimization.Presently,mixed model assembly line operation suffers several challenging in terms of workstations idle time,resulting in inefficiency of the entire assembly line and low production of equipment usage.In this paper,we focus on mixed model assembly line balancing and sequencing problem(MALBS).A model including balance and sequence objects is built and methods solving this model was proposed for the lower cost and high efficiency.This paper builds one model for mixed model assembly line balance and sequence problem with given cycle time and workstation and designs four intelligence algorithm to solve it.In modeling part,MALBS can be divided into two stages:balancing and sequencing.In order to maximize the balance between workstations,the balance stage uses the comprehensive job sequence diagram and the comprehensive processing time,rate as the goal to seeks the optimized task assignment solution;in sequence stage,we set three objects which are minimizing total processing time,maximizing balance rate between workstations and minimizing processing time smoothness in workstation.In method part,based on dual genetic algorithm?particle swarm algorithm and Pareto Principle,we proposed four improved algorithms to make up drawbacks of single algorithm for better efficiency.In number experiment part,firstly,we verify the reliability of the algorithm and then explore the influence of parameters for result.Finally,the results of those four algorithms are compares in two different scale examples. |
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