A two-level Particle Swarm Optimisation algorithm on Job-Shop Scheduling Problems

An Efficient PSO Algorithm for Finding Pareto-Frontier in Multi-Objective Job Shop Scheduling Problems

“…PSO has been successfully applied in many fields due to its ease of implementation and computational efficiency. It also demonstrated its efficiency in solving single objective JSP (Pongchairerks and Kachitvichyanukul, 2009a;Pratchayaborirak and Kachitvichyanukul, 2011;Rookkapibal and Kachitvichyanukul, 2006). However, the applications of PSO for solving MOJSP are still very limited.…”

“…These approaches include, but not limited to, tabu search (Nowicki and Smutnicky, 1996), simulated annealing (Matsuo et al, 1988;Van Laarhoven et al, 1992), genetic algorithm (Goncalves et al, 2005;Yamada and Nakano, 1995), ant colony optimization (Udomsakdigool and Kachitvichyanukul, 2006), particle swarm optimization (PSO) (Pongchairerks and Kachitvichyanukul, 2009a;Rookkapibal and Kachitvichyanukul, 2006), and differential evolution (Liu et al, 2009;Wisittipanich and Kachitvichyanukul, 2012). The number of research works on multi-objective JSP is much less than that for single objective JSP.…”

A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem

“…Recently, many researchers proposed hybrid algorithms based on PSO and local search method to deal with different optimization problems, such as PSO-Tabu search (Gao et al, 2006), PSO-SA (Niknam et al, 2009) and so on. Some hybrid PSO algorithms are also proposed to deal with JSP (Xia & Wu, 2006, Sha & Hsu, 2006, Pongchairerks et al, 2009. VNS, proposed by Mladenovic et al (1997), has quickly gained widespread successful utilization in many domains, such asly graphs (Caporossi et al, 2000), traveling salesman problem (Felipe et al, 2009) and vehicle routing problem (Kuo & Wang, 2012).…”

Application of Adaptive Particle Swarm Optimization to Bi-level Job-Shop Scheduling Problem

“…The effectiveness of PSO was compared with GA and differential evolution in and PSO was mention that its mechanism facilitated in solution improvement within short computational time compared with GA. PSO was widely applied in many scheduling problems, for examples, flow-shop scheduling by Rahimi-Vahed and Mirghorbani (2007) and Pan et al (2008); job-shop scheduling (JSP) by Sha and Hsu (2006), Lei (2008), Lian et al (2006), Xia and Wu (2005), Pongchairerks and Kachitvichyanukul (2009), Zhang et al (2009), Pratchayaborirak and Kachitvichyanukul (2011), Wisittipanich and Kachitvichyanukul (2013). However, most researchers in this field used PSO in a single and multiple objective optimizations, in contrast, there was only a few researchers working on multi-level programming problems.…”

“…One method to avoid the step of parameter optimization is to design multi-level PSO proposed by Pongchairerks and Kachitvichyanukul (2009) that separated the top level PSO to fine-tune and assigned the parameter values for PSO and the lower level PSO developed for finding JSP solution. However, this approach has the disadvantage of long computational time.…”