CAJ | 학술논문

在无等待流水车间环境下，考虑订单分批量加工策略的订单接受问题，建立问题的数学模型。由于问题的NP难特性，提出改进的遗传算法对模型进行求解。改进的算法采用正向和反向NEH算法与随机方法产生初始种群，在算法更新过程中将禁忌搜索算法嵌入到遗传算法中来实现局部搜索，避免算法陷入局部最优。最后，算例表明批量划分策略能够有效减少订单的完成时间，实现订单总收益的最大化。通过算法对比，说明了改进遗传算法具有较好的求解效果。
재무등대류수차간배경하，고필정단분비량가공책략적정단접수문제，건립문제적수학모형。유우문제적NP난특성，제출개진적유전산법대모형진행구해。개진적산법채용정향화반향NEH산법여수궤방법산생초시충군，재산법경신과정중장금기수색산법감입도유전산법중래실현국부수색，피면산법함입국부최우。최후，산례표명비량화분책략능구유효감소정단적완성시간，실현정단총수익적최대화。통과산법대비，설명료개진유전산법구유교호적구해효과。
An integer programming model is constructed for the order acceptance problem with lot -spliting in no-wait flow shop.With the NP-hard nature for the problem , an improved genetic algorithm ( IGA) is proposed to solve the model .Unlike the standard GA algorithm , based on the basic NEH algorithm , the modified NEH algorithm , and stochastic method , the IGA presents an efficient initialization scheme to con-struct the initial population .In addition , tabu search for generating neighboring solution is embedded in the IGA to avoid a local optimum .Numerical results indicate the efficiency of lot-splitting in shortening the completion time of orders , which contributes to minimizing order tardiness .Compared with traditional ge-netic algorithm , the proposed approach yields significant improvement in solution quality .