I want to create model for truck company in which trucks delivers the car for customers.i have two data sets. one is customer details like how many cars they want from particular area or terminal and all and in another dataset i have car company details like here we have this much of cars today. So from this details we have build loads for trucks like in one load we can add 6 or5 or 4 cars .so totally i have two input datasets and one output dataset .output dataset is dependent on both input datasets .one particular problem here there is no common in input datasets so we can't merge or combine them. This is my problem for this which type of algorithm or model i can use and how to preprocess the dataset?
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1$\begingroup$ Hi Prema, Would you like to share a screenshot of the dataset? It is a bit confusing to determine the dataset columns this way. $\endgroup$– KritiNov 9 at 16:12
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It looks like your problem is more suitable for optimization (operational research) techniques than machine learning algorithms. If I understood it right, you could build a model that can decide how many cars should be allocated on each truck load and model the dynamics of how each truck composes the cars that will get to which clients, etc, optimizing some objective function that makes sense for the problem and use a solver framework to find an optimal solution.
If you can share more details like some description or print of tables and more about the problem dynamics it would help people help you.
