Experience With a Global Optimisation Approach to Project Scheduling and Resource Allocation

Abstract In recent years there has been a growing trend in major projects towards considering the whole project life cycle and its costs. This analysis initially considered concept to commissioning, and later, particularly after Brent Spar, concept to decommissioning. Within this project life cycle, optimal solutions are sought. However, the field of optimisation still tends to focus on improving the performance of elements of the project or small subsystems within the project as a whole. An optimisation across the whole project life cycle is not normally carried out. This paper proposes a general system for such a global optimisation model, which will allow the whole project to be considered within a single optimisation. The strategy consists of a basic scheme for input to an objective function and the definition of its constraints. The objective function can map to a value relating to cost, time, performance or risk, allowing the most important criteria to be maximised or minimised. Other values not mapped to by the objective function can be used as constraints to restrict any detrimental impact to them during the optimisation. The size and complexity of a true globally optimising model is extremely large. If the model is to be able to optimise globally, then it must also be able to arrive at an optimal solution for a local optimisation problem. Therefore its suitability for optimisation is tested on project scheduling and resource allocation, a subsystem of the project. A new approach to modelling project schedule and resource allocation, which will allow the model to fit into the globally optimising model, is developed. Using examples from the literature, the performance of this model on specific locally optimising problems is investigated, using Monte Carlo random search. This, although not normally considered as being an efficient means of finding an optimum, was considered the best approach to initial investigation where the nature of the optimising surface is unknown. In particular, the model is used to solve resource constrained scheduling problems. The difficulty of finding the optimum within the model is also studied, and extensions to the model for further research are proposed.