Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System

Abstract

Problems encountered in real manufacturing environments are complex to solve optimally, and they are expected to fulfill multiple objectives. Such problems are called multi-objective optimization problems(MOPs) involving conflicting objectives. The use of multi-objective evolutionary algorithms (MOEAs) to find solutions for these problems has increased over the last decade. It has been shown that MOEAs are well-suited to search solutions for MOPs having multiple objectives. In this chapter, in addition to comprehensive information, two different MOEAs are implemented to solve a MOP for comparison purposes. One of these algorithms is the non-dominated sorting genetic algorithm (NSGA-II), the effectiveness of which has already been demonstrated in the literature for solving complex MOPs. The other algorithm is fast Pareto genetic algorithm (FastPGA), which has population regulation operator to adapt the population size. These two algorithms are used to solve a scheduling problem in a Hybrid Manufacturing System (HMS). Computational results indicate that FastPGA outperforms NSGA-II.

Comparison of lot streaming division methodologies for multi-objective hybrid flowshop scheduling problem by considering limited waiting time

“…The NSGA-II algorithm is one of the state-of-the-art metaheuristics offered by Deb et al [16] and is applicable to solving complex multi-objective optimization problems [15,35]. Because the NSGA-II algorithm shows its effectiveness to generate high-quality solutions from both diversification and intensification aspects [67], it is preferred to be used in this study.…”

An Airport Stand Assignment Problem considering the Passenger Boarding Distance

“…For a multiobjective optimization problem, a set of solutions usually exists which is not comparable in merit among them. Te set of solutions is called Pareto optimal, in which none of the objective functions can be improved without degrading some of the other objective values [35]. If a solution is inferior to the Pareto solution set in each objective function, it is called a solution dominated by the Pareto solution set.…”

Dynamic cyclic kitting part-feeding scheduling for mixed-model assembly line by a hybrid quantum-behaved particle swarm optimization

“…As a consequence, novel methodologies such as meta-heuristics, hybrid and intelligent algorithms, machine learning and deep learning are proposed to encounter large-sized problems (Boysen and Emde, 2014;Fathi et al, 2014;Rao et al, 2013;Peng and Zhou, 2018;Moshayedi et al, 2023). Among which, metaheuristic algorithms are powerful in solving optimization problems and have some advantage over classical methods (Moshayedi et al, 2022a(Moshayedi et al, , 2022b(Moshayedi et al, , 2022c, thus were implemented in a vast range of research fields such as robotics (Moshayedi et al, 2020), manufacturing (Yilmaz et al, 2017), for path planning (Moshayedi et al, 2019), line balancing (Wu et al, 2021), scheduling (Yilmaz and Durmusoglu, 2019), defect detection (Moshayedi et al, 2022a(Moshayedi et al, , 2022b(Moshayedi et al, , 2022c. These methodologies mainly include the genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), artificial bee (or ant) colony (ABC, AC), non-dominated sorting GA-II (NSGA-II) along with their improved versions.…”