Evolutionary Algorithm For Resource Constrained Project Scheduling Problem

Genetic algorithms (GA) over the decades are increasingly used to solve the real-worldproblems. Genetic algorithm has ability to incorporate various techniques within itsframework to produce a hybrid algorithm.In this dissertation paper, different forms of integrations between GA’s and optimizationtechniques/methods will be focused on. These techniques have capability for enhancements,feasibility techniques, fitness function techniques, substitution to operation techniques. Thesearching techniques included in this paper are tuning method, distribution based system, andfitness centered local search potential techniques. We then turn our focus to the comparisonsof different Hybrid that have applied to solve RCPS problem which include comparisons fromValls et al’s,Hybrid GA (HGA), Lin et al’s application of SGS with hybrid named (ANGEL),Elloumi et al’s Evolutionary Algorithm (EA), Wang et al’s Estimation Distribution Algorithm(EDA).Furthermore, our attention is specified on schedules that were included in our case study.A genetic algorithm is presented to solve this problem. Our focus was on a garment industrywhere we applied our genetic algorithm with Elite Population Structure and VariableNeighborhood Searching to find the optimized problems with variables. In actual productionwe have found that unloading and unloading of the machines proved to reduce the cost whenrelated to waiting costs, fewer machine switching operators thus reducing the run down times.The new algorithm proved that it could be used for decomposing effects in combinational andcontinuous problems.