Integrated bi-objective project selection and scheduling using Bayesian networks: A risk-based approach

This paper presents a novel formulation for the integrated bi-objective problem of project selection and scheduling. the first objective was to minimize the aggregated risk by evaluating the expected value of schedule delay and the second objective was to maximize the achieved benefit. to evaluate the expected aggregated impacts of risks, an objective function based on the bayesian networks was proposed. in the extant mathematical models of the joint problem of project selection and scheduling, projects are selected and scheduled without considering the risk network of the projects indicating the individual and interaction effects of risks impressing the duration of the activities. to solve the model, two solution approaches were developed, one exact and one metaheuristic approach. goal programming (gp) method was adopted to optimally select and schedule projects. since the problem was np-hard (non-deterministic polynomial-time), an algorithm combining gp method and genetic algorithm (ga) was proposed, hence named gpga. finally, the efficiency of the proposed algorithm was assessed not only based on small-size instances, but also by generating and testing representative datasets of larger instances. the results of the computational experiments indicated that it had acceptable performance in handling large-size and more realistic problems.