dc.description.abstract | The decision on infrastructure investment is complex. It involves satisfying all constraints, in terms of time, activity precedence and resources, and maximizing the overall utility simultaneously. The decision itself is not about choosing the best among all potential alternatives identified, but to form a most favorable combinatorial plan of alternatives within the limitation of resources, in terms of money, time, and manpower. Since different alternatives are often in a competing position for an identical resource and such resource requirements vary from one period to another throughout the planning horizon, it is quite apparent that the determination of the combinatorial plan is a typical multi-objective resource scheduling problem. This work characterizes an infrastructure investment problem in four aspects: (1) multiple objectives, (2) multiple alternative projects with sub-projects, (3) definitive time-dependent resource constraints and resource demands, and (4) definitive time-logic constraints among sub-projects.
This study proposes a time-series combinatorial planning model to represent the infrastructure investment problem. The model is formulated based on mathematical programming. It is observed that the model is a 0-1, non-linear, multi-objective knapsack problem, which exhibits the NP-hard nature. Since finding the optimal solution is computationally difficult, this work proposes an innovative approach, based on the concept of genetic algorithms, to tackle the non-linear optimization problem. Based on experimental results, the proposed approach is effective in producing quality solutions.
The methodological significance of the proposed approach lies in the highly effective multi-point search mechanism afforded by the genetic algorithms. Another merit of the proposed approach is that it provides a bank of feasible solutions, rather than just the best solution found. This bank can serve as a good reference to the decision-maker. | en_US |