Solving Resource Constraint Project Scheduling Problems Using Modified Ant Colony Optimization

Abstract

Resource Constraints Project Scheduling Problem (RSPSP) seeks proper sequence of implementation of project activities in a way that the precedence relations and different type of resource constraints are met concurrently. RCPSP tends to optimize some measurement function as make-span, cost of implementation, number of tardy tasks and etc. As RCPSP is assumed as an NP-Hard problem so, different meta-heuristic approaches have been proposed to solve different variants of it. In this paper, a modified Ant Colony Optimization (ACO) approach has been developed to deal with RCPSP. The definition of probabilistic selection rule has been modified in proposed approach in favor of better performance. Moreover, the parameters of algorithm have been determined in an adaptive manner and the stagnation behavior has been prevented in high iterations of algorithm. Uncertainty of parameters of RCPSP has also been discussed. The proposed algorithm has been coded using Visual Basic software and tested on benchmark instance in this area. The results are promising and have been compared with optimal or best known solutions.