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Research On Urban Distribution Network Optimization Of Fresh Chain Enterprises For New Retail

Posted on:2020-01-23Degree:MasterType:Thesis
Country:ChinaCandidate:Z Z YaoFull Text:PDF
GTID:2439330599453388Subject:Business Administration
Abstract/Summary:PDF Full Text Request
In recent years,the development of fresh chain business has developed rapidly,and new retail chain enterprises such as Freshippo,super speciesand Guolin have emerged.The urban distribution network is one of the key factors affecting the logistics cost of the fresh chain enterprises.The latest new retail practices such as pre-warehouse,omni-channel,online and offline integration have proposed new research topics for the optimization of urban distribution logistics network.Based on the fresh retail business model,this paper constructs a nonlinear mixed integer programming model that comprehensively considers the layout and coverage of multi-storey stores,cold chain facility configuration,and cold collection selection,and designs a hybrid Lagrangian relaxation algorithm to solve the model.The effectiveness of the proposed algorithm is verified by comparison with CPLEX.According to the actual data of typical fresh retail enterprise called Guolin in Chongqing,the model and algorithm of this paper are used to obtain the total cost of Chongqing Guolin city distribution system,the layout plan of multi-format retail stores,the online coverage of stores,the best category and the cold chain facility configuration plan.By the sensitivity analysis of the model,this paper discusses the impact of the scale of demand,consumer willingness,online order size and weather changes on the distribution system,and reaches the following conclusions:(1)comparing toGuolin's existing distribution network,the average cost of store has been reduced by 2.52%.Among them,the cold chain equipment in the store only reduces the total cost by 0.32%,and the effect is small;(2)the change of demand scale has little impact on the distribution network and proportion of all kinds of cost,which proves that the model in this paper has strong robustness,and the optimized distribution network has strong adaptability;(3)The increase of demand scale has little effect on the proportion of the cost of the system,but it decreases the percentage of the cost of loss reduction in configuring the cold chain facility;(4)consumers' willingness of self-pick have no effect on urban distribution network structure.It only affects the online order distribution cost,but has a small impact on the total cost.When the increaseof willingness is 10%,the total cost only increases by 0.15%.(5)The online order size has no impact on the urban distribution network structure,only affecting online order delivery cost.The smaller the online order size,the higher the average cost per kilogram of distribution,but the change in order size has little impact on the total cost;(6)weather changes have no effect on the distribution network structure,only affecting the cost of loss.The effect of temperatureincrease is greater than the effect of temperature reduction;the larger the demand for the enterprise,the smaller the effect of temperature on total cost and loss cost.Based on the above results,this paper puts forward two suggestions for the new retail chain of fresh chain.Firstly,the cold chain facility should be integrated to balance the relative size of the cold chain configuration input and the cost of loss reduction.The second is the impact of online order size on the total distribution is small,and the strategy of raising the free distribution threshold is ineffective for fresh new retail chain enterprises.In view of the high cost of cold chain facility configuration,it basically offsets or even exceeds the cost of loss caused by the configuration of the cold chain.This paper suggests that governments should strengthen the subsidy for the configuration of cold chain facilities.
Keywords/Search Tags:fresh, new retail, optimization of urban distribution, nonlinear mixed integer programming, Lagrangian relaxation
PDF Full Text Request
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