Multi-objective teaching-learning-based meta-heuristic algorithms to solve multi-skilled project scheduling problem

Abstract

In this paper, a novel multi-objective multi-skilled resource-constrained project scheduling problem based on learning effect is presented in which efficiency of the workforce is proportional with the time they spend to execute that skill. A mixed integer nonlinear programming formulation is presented to optimize conflicting objectives. Two hybrid multi-objective teaching–learning based optimization (TLBO) algorithms are developed with a new solution structure based on primary concepts of multi-objective particle swarm optimization and multi-objective invasive weeds optimization algorithms to find near optimal solutions of the model’s complicated instances in a reasonable computational time. The proposed algorithms encompass a novel solution structure that satisfies all the constraints and generates feasible solutions. The classical multi-objective teaching-learning based optimization algorithm and NSGA II are developed with the same chromosome structure to validate performance of the developed algorithms. In addition, all the algorithms are employed to solve small- and large-scale benchmark instances that are available on literature of project scheduling problems as well as 30 randomly generated test problems. Taguchi grey relational analysis is used to tune parameters of the algorithms and enhance their performance for generating more qualified solutions. In addition, statistical test is employed to compare performance of the proposed algorithms in terms of some multi-objective metrics. Finally, hybrid multi-attribute decision making approach is employed to prioritize algorithms and determine the algorithm with higher performance on solving small, medium and large scale instances.