DESIGN OF ROBUST DISTRIBUTION NETWORKS RUN BY THIRD PARTY LOGISTICS SERVICE PROVIDERS

2012 ◽  
Vol 15 (05) ◽  
pp. 1150024 ◽  
Author(s):  
M. P. M. HENDRIKS ◽  
D. ARMBRUSTER ◽  
M. LAUMANNS ◽  
E. LEFEBER ◽  
J. T. UDDING

We consider a third party logistics service provider (LSP), who faces the problem of distributing different products from suppliers to consumers having no control on supply and demand. In a third party set-up, the operations of transport and storage are run as a black box for a fixed price. Thus the incentive for an LSP is to reduce its operational costs. The objective of this paper is to find an efficient network topology on a tactical level, which still satisfies the service level agreements on the operational level. We develop an optimization method, which constructs a tactical network topology based on the operational decisions resulting from a given model predictive control (MPC) policy. Experiments suggest that such a topology typically requires only a small fraction of all possible links. As expected, the found topology is sensitive to changes in supply and demand averages. Interestingly, the found topology appears to be robust to changes in second order moments of supply and demand distributions.

2021 ◽  
Vol 12 (1) ◽  
pp. 135-146
Author(s):  
E.A. Ejem ◽  
C.M. Uka ◽  
D.N. Dike ◽  
C.C. Ikeogu ◽  
C.C. Igboanusi ◽  
...  

Abstract This paper is focused on solving the evaluation and selection of 3PL’s by applying multi-criteria decision-making methods. Nigerian Breweries, Nigerian Bottling Company (NBC), AG Leventis, Kobo logistics, and Flour Mills of Nigeria (FMN) were understudied. The main criteria on which evaluation is based were established: Cost, Service level, Financial Capability, Reputation and Long-term relationship. A combination of two quantitative models was adopted in the study. Relevant data were collected through an oral interview with managers and key decision-makers at the companies. SWARA was first applied to the collated data to determine the relative weights of the criteria. Afterwards, the TOPSIS was applied to the weights developed using SWARA and on the performance of the selected service providers. After the analysis, the best service provider was identified as supplier 2 while the worst was supplier 5.


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