HIGH-PERFORMANCE HEURISTIC ALGORITHM FOR CONTROLLING STOCHASTIC NETWORK PROJECTS

An activity-on-are network project of PERT type with random activity d urations is considered. The progress of the project cannot be inspecte d and measured continuously, but only at preset inspection points. An on-line control model has to determine both inspection points and cont rol actions to be introduced at those points to alter the progress of the project in the desired direction. On-line control is carried out t o minimize the number of inspection points needed to meet the target, subject to the chance constraint. In the recently developed control mo dels, determining the next inspection point is carried out via extensi ve simulation with a constant lime step. This determination is based o n sequential statistical analysis at each intermediate point to maximi ze the time span between two adjacent control points. The main shortco ming of the control algorithm is its long computational time due to th e need to make numerous decisions. In this paper we present a newly de veloped heuristic control algorithm in which the timing of inspection points does not comprise intermediate decision making. Given a routine inspection point t(i), the adjacent point t(i+1) is determined so tha t even if the project develops most unfavorably in the interval [t(i), t(i+1)], introducing proper control action at moment t(i+1), enables the project to meet its target on time, subject to the chance constrai nt. The newly developed control algorithm is essentially more efficien t than the step-by-step control procedures. The computational time is reduced by a factor of 25-30 while the algorithm provides better solut ions than would be attained by using on-line sequential statistical an alysis. Extensive experimentation has been undertaken ro illustrate th e comparative efficiency of the presented algorithm. (C) 1998 Elsevier Science B.V. All rights reserved.