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Date of Publication:2017-01-01

Journal:管理工程学报

Affiliation of Author(s):经济管理学院

Issue:2

Page Number:209-215

ISSN No.:1004-6062

Abstract:Retrieving items from warehouse in order to satisfy customer orders is one of the most important parts in e-commerce warehousing.In traditional warehouse operation,order picking is the most labor-intensive process that determines warehouse performance.Up to 55％ of operational cost in a warehouse can be attributed to order picking.The integration strategies of synchronized zoning and order batching are often used in e-commerce order picking.However,imbalance workload distribution in each zone will reduce the whole efficiency of the picking system,such as reducing equipment utilization rate,delaying classification and package time,and increasing pickers' injustice.Currently,there are few theoretical researches about integration optimization of synchronized zoning and order batching.In this paper,we evaluate operational efficiency of e-commerce manual synchronized zone order picking system by focusing on the order batehing optimization strategy in the synchronized zone order picking system.Total service time and balance degree in each zone are two critical factors to measure efficiency of the system.Firstly,bi-objective order batching optimization model is established by minimizing total service time and balancing workload in each zone.Let us suppose that a picking area is divided into several equal zones and each zone is serviced by only one picker.Total service time means the whole time needed to finish all the batched orders.Balance degree means the sum value for each batch that uses the maximum service time to minus minimum service time in each zone.Secondly,as the order batching problem is a NP-hard one,we propose a bi-objective genetic algorithm to tackle the problem.The fitness function ranks the gene by comparing their performance in each objective function through the sorting matrix created by the objective function.To maintain the diversity of population and control the population growth towards the direction of the optimal solution,we proposed the crossover,mutation probability function based on individual fitness value and the optimal preservation strategy.At last,in order to determine the solution quality of the model and algorithm,an extensive series of numerical experiments are carried out to compare the results of the bi-objeetive model with those of the single objective model which minimizes the total service time.The experiments are under three different order environments.Through a series of experiments,we discover four important findings.First,the total service time and balance degree are two conflictive objectives.Second,comparing with single-objective model,bi-objective model's total service time increases slightly.However,the increasing range is much smaller than the improvement of the balance degree.Third,synchronized zone order batchmg model which considers workload balance for each zone can improve the whole efficiency of the picking system.Fourth,the model improves the greatest balance degree for the small scale order environment.The findings obtained in this paper provide guidance for e-commerce order picking operation.

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