Distributed Scheduling Model
Recently, a new Distributed Scheduling Method, DSM, was introduced to facilitate the planning of resources in distributed and repetitive projects (Hegazy et al., 2004), and was implemented in a computer software program, called BAL. The DSM helps in scheduling multi-site M&R programs that are delivered using combination of in-house resources and outsourcing. The DSM model determines the optimum site execution order and assigns available resources to the various sites in a manner that maintains crew work continuity, and meets the cost, and resources constraints. To meet deadlines, speedy delivery options (often expensive) for various activities are stored and used in the scheduling process. One of the key features of the DSM is its legible representation of the large amount of schedule information, as shown in Figure 1. The figure shows time on the horizontal axis and site index on the vertical axis. All crew movements among sites are clearly shown. For example, crew 1 of the second activity will proceed to site 4 once it completes its work on site 1. Clearly, the time
Fig. 2. ations and productivity factors.
and cost of moving resources from site 1 to site 4 depends on the distance between the sites and the speed of moving along possible travel routes. As such, the site order (vertical index) becomes an indicator of the sequence of operations and its consequent travel time and cost.
The DSM model is capable of generating schedules by manually changing the options for construction methods, number of crews, the site order, and the amount of interruption at various sites. However, with the large number of possibilities, even for a small network of sites, a cost optimization model becomes necessary to identify the optimum combination of these variables to meet schedule constraints. The optimization model in the DSM involves the setup of the objective function and optimization constraints. The objective function of the model is to minimize total construction cost. Along with proper ranges for the variables, two soft constraints are used: Project duration should be less than or equal to the deadline duration; and total aggregated amount of a given resource is less than or equal to the amount available. To handle the large-scale optimization involved, a non-traditional optimization technique, Genetic Algorithms, has been successfully used in the DSM.