• Bidisha Roy
• Dr. Asim Kumar Sen

Many problems in the field of Operations Research and Project Management can be classified as Optimization Problems which are classically NP-Hard. One such important problem is the Resource Constrained Project Scheduling Problem, abbreviated as the RCPSP. It is a Combinatorial Optimization problem involving resource as well as precedence constraints with temporal conditions. But, the RCPSP requires exponential computational resources as the problem complexity increases, as it is intractable. Thus, Soft Computing based approaches provide better results towards solving the problem optimally. This paper presents the usage of a discrete version of the Teaching Learning Based Optimization (TLBO) metaheuristic to solve the problem. Also, a hybrid model using 2-point crossover inspired by the Genetic Algorithm is presented. The proposed models are extensively tested on well-known benchmark test instances and analyzed. The promising results demonstrate the efficiency of the proposed solutions for solving the RCPSP problem of varying magnitudes.

Scheduling, Optimization; RCPSP (Resource Constrained Project Scheduling Problem), Soft Computing, Metaheuristics, Swarm Intelligence, Nature Inspired Algorithms, Teaching Learning Based Optimization Algorithm (TLBO), 2-point Crossover.

" A Modified Discrete Teaching Learning Based Optimization Algorithm for the Resource Constrained Project Scheduling Problem", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.f673-f684, August-2022, Available :http://www.jetir.org/papers/JETIR2208571.pdf

" A Modified Discrete Teaching Learning Based Optimization Algorithm for the Resource Constrained Project Scheduling Problem", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppf673-f684, August-2022, Available at : http://www.jetir.org/papers/JETIR2208571.pdf