This is the second of a series of two papers presenting a reliability based methodology to quantify the financial risks involved in an engineering process systems operation which can be represented as a flow of discrete entities. Each component in the process chain is abstracted as a risk item and a logic based on finite automata is incorporated in the model to transition between the states of no-failure, maintenance, failure and lead time. The process operation is abstracted into a model composed of several servers and queues and employs discrete event simulation techniques. To illustrate the proposed method, the model is applied to the case of a copper ore production system and the risk profile is characterised. Multi-objective evolutionary algorithms are used to optimise these models and obtain a Pareto optimal set of solutions with different risk profiles. This kind of queuing theory based modelling can be used to represent many types of systems like those in production engineering, supply chain management, manufacturing engineering, logistics etc. Hence this risk model can easily be adapted to different disciplines with minor modifications.