Project Coordination Model

Many transportation agencies such as Departments of Transportation in the USA are using Asset Management Optimization models to plan their maintenance and rehabilitation projects. Currently available commercial solutions are often focused only on global asset performance/money spent aspect of the problem and do not take into account the benefits of synergy between the projects,i.e. the benefits of scheduling nearby projects in the same year in order to take advantage of fixed cost savings due to economies of scale. At the strategic level adding variables and constraints to account for these economies of scale are not practical since such models often already have millions of variables and constraints and adding anything new could reduce the performance significantly. Looking at the problem from a tactical perspective however it is possible to run an analysis on the global work plan that can improve cost savings from economies of scale with subtle changes to the originally analyzed performance. For example, in case of pavement management it is a common result to see gaps in the produced theoretical work plans along a route that would result in increased mobilizations to accomplish the plan. This results in extra costs both for the agency and road user. A common result might suggest fixing miles 1-8 and 9-10 in the one year and then fixing mile 8-9 in a subsequent year. Even if this solution provided the best performance for the money it is impractical to actually do it, it would make much more sense to simply fix entire 10 mile segment while achieving almost the same performance and saving mobilization costs which can be significant cost savings. A project coordination optimization model would analyze economies of scale problems such as these while also considering network constraints such as limited budgets and resources available within the administrative unit managing the work plan.This paper proposes an optimization model, referred to as Project Coordination model throughout the paper, that would attempt to improve an already created work plan by slightly changing the project schedules such that the adjusted plan would achieve similar performance while providing a more practical solution by maximizing economies of scale with reduced delivery and mobilization costs. The results of applying proposed model to one illustrative example and two real life work plans are presented and thoroughly analyzed.