A solution technique is developed for Chance Constrained Programming (CCP) problems where the coefficients appearing in the objective function and constraints are assumed to follow a two-parameter exponential distribution.  (f(x)=1/λ e^(–1/λ(x–μ)), xμ) is presented. Here the parameters μ and λ are called location and scale parameters of the exponential distribution. We call the location parameter μ as the minimum guarantee time and, λ is the mean time after the guarantee time is observed. This distribution is quite familiar in real life situations where the random variables X has a shift i.e., Xμ. For example in inventory systems the demand X is a random variable X satisfying a minimum quantity as the demand may not start with zero. So we aim to present a complete deterministic equivalent of the CCP where the coefficients follow a two parameter exponential distribution. Also, the CCP problem is derived in a mixed environment i.e., the coefficients are random whose parameters are fuzzy numbers. In both cases, the problem is converted to its deterministic equivalent. Here the Fuzzy Probability theory due to Buckley [6] is used. Also, a real-life situation is presented. The method is justified by numerical examples.