A Hybrid Algorithm for Price Discount Based Project Scheduling with Parameter Setting by Using Stepwise Regression

The project scheduling problem is known as a NP-hard problem in literature. In this research, a resource constrained project scheduling problem which is known as a NP-Hard problem is considered. This problem has attracted many researchers during recent years. The aim of this problem is to determine the optimal starting times of activities considering both precedence and available resources constraints such that the total project completion time is minimized. In this paper a combination of discount based pricing policy and project scheduling is proposed, whereas in classical models it is assumed that price of required resources is fixed. To solve the proposed model, a hybrid algorithm based on two algorithms, i.e. genetic algorithm and variable neighborhood search is proposed. In this method, genetic algorithm as a main framework and variable neighborhood search as a new operator are designed. Moreover, since the parameter values of evolutionary algorithms have great influences on algorithm efficiency, to set the parameters of proposed algorithm a new statistical approach based on stepwise regression technique is devised. Computational results show the good performance of proposed approach with regard to the other methods.