A typical optimization problem emanating from the actual optimization that is conducted on a routine basis at a leading retail store, Staples, Inc. is used to illustrate the concepts of Simulation-optimization. This is done by solving a transportation problem of 10 cities (a subset of the actual 40-city problem) using optimization principles. The same problem is then solved using probabilistic principles using the total traveled distance between two cities as a random variable (depending upon the route taken). In a typical Simulation-optimization problem, Monte Carlo simulation is used for simulation of random paths. In this paper, an alternate but efficient method is prposed and used based on the probability principles. This is based on the concept of capturing uncertainities ranging from 68.3% to 99.7% in the physical system based on (μ +/- σ, μ +/- 3)σ concept. The results from probabilistic optimization and the corresponding deterministic optimization are the compared. It is found that sumulation-optimization results in savings of cost as compared to traditional optimization