This modelling approach could be used to establish one important part of reverse logistics network by defining optimum locations for collection points.The objective of this project is to determine the maximum number of post-consumer (supply point )by per-defined number of collection facilities opened in which the probability of each supply point being covered by facility collections is not lower than the specified value, defined as critical geometric radius.In this area the question is,how can we integrate the probability with this problem?
Replace the X_ij 1/0 assignment variable with P_ij = the probability that i can be served by j. Use Yj as a 1/0 variable to define the open facilities. The P_ij can be defined as a function that drops to zero if d_ij > some critical dist. This is then a bit like a probabilistic p-median. There are some papers on this (see spatial interaction based location-allocation models) in my RG publications. Ask for more details if this seems to be a useful answer.
I do not understand your question . I suppose that you talk about LES. This method is dedicated to CFD
Hi Iman,
had you looked at "floor-space management" methods and literatures?
Hi Iman,
Could you let me explain critical geometric radius? Why and how do you use it in your model?
Replace the X_ij 1/0 assignment variable with P_ij = the probability that i can be served by j. Use Yj as a 1/0 variable to define the open facilities. The P_ij can be defined as a function that drops to zero if d_ij > some critical dist. This is then a bit like a probabilistic p-median. There are some papers on this (see spatial interaction based location-allocation models) in my RG publications. Ask for more details if this seems to be a useful answer.
Dear Researcher,
this problem tend to extend for multi objective obtimization,
however ,the problem is coverage with one objective maximum distance,
I am looking for other conflict objective inthis regard such as time or even effieciency.
the problem could extend to idea that Prof Morton Edward has mentioned earlier in this page.
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