build model with complicated types of feature variables I have been asked to build a model to predict a life span of a material based on a couple of features. The features can be classed into the following categories:
1)  The feature variables just have 0 or 1 value
2)  The feature variables are ordered variables, such as a={1,2,3,4,5} or b={0, 200, 210, 840,…}
3)  The feature variables are continuous
4)  The feature variables are not exactly ordered, such as c={42, 43.4, 57, -1, 0, 0,…}
The predicted life span is a numerical value.
I would like to get some advice on how to handle these mixed type of features. Can multiple regression handle this kind of scenario?  Are there any other statistical models that can help?
 A: A question like this sounds like we may be picking wires in the bomb without understanding what they actually do :). To side note the question:  multiple regression, GLM, etc modeling would be conducted, for example, to examine what effects, and perhaps to what degree of effect, modeled variables have on an outcome. 
That is not the problem domain here. What you are asking is not really a predictive question as one of estimation. In particular you should be conducting a survival analysis with respect to some well-defined time-to-event criteria. 
In other words, what is the defined end-of-life for the material? The time at which it breaks? Crumbles? Goes poof? Or would we examine something more microscopic, such as material decay, oxidation, etc that would render the risk of the material too great to consider further use? This is the first thing you need to know.
Once your question is refined and definitions provided, you need to understand your data. In order to determine an end-of-life based upon some definition, it is pretty important to have had data that contains observations of end-of-life. That is, you need a history. 
If you do not have this, but have well defined criteria for what end-of-life is considered to be, then your problem becomes rather Bayesian - in the sense that you will need to incorporate variables, rules, and even expert opinion to inform what you expect to be an end-of-life outcome without having observed it yet. This sort of problem is also a data gathering venture, such that when the data overwhelm the prior information (i.e. you capture actual observations of 'end-of-life'), the observations themselves inform the survival estimation. 
