Multiple colony ant algorithm for job-shop scheduling problem

An efficient job-shop scheduling algorithm based on particle swarm optimization☆

“…Because JSSP is an NP-hard problem, it is difficult to develop a perfect algorithm to find a solution within a reasonable time especially for higher dimensions. Recently, many researchers made use of evolution algorithm to solve the problem, such as tabu search method (Nowicki & Smutnicki, 2005;Ponnambalam, Aravindan, & Rajesh, 2000), genetic algorithm (Goncalves, Mendes, & Resende, 2005;Park, Choi, & Kim, 2003;Wang & Zheng, 2001;Watanabe, Ida, & Gen, 2005), simulated annealing (Van Laarhoven, Aarts, & Lenstra, 1992;Steinhöel, Albrecht, & Wong, 1999;Suresh & Mohanasundaram, 2005), ant colony (Udomsakdigool & Kachitvichyanukul, 2008;Zhou, Li, & Zhang, 2004) and particle swarm optimization (Ge, Du, & Qian, 2007;Ge, Sun, Liang, & Qian, 2008;Lian, Gu, & Jiao, 2006). In this paper, we focus on exploiting particle swarm optimization algorithm to achieve the better solution for JSSP.…”

An adaptive multi-population genetic algorithm for job-shop scheduling problem

“…For instance, the optimal solution of the well-known JSP benchmark problem FT10 had not been found until several decades after the problem was originally proposed [2]. Recently, the metaheuristics algorithms have been employed to deal with JSP by many scholars, such as genetic algorithm (GA) [6][7][8][9], tabu search method [10,11], particle swarm optimization [5,12], ant colony optimization [13], and simulated annealing (SA) [14]. In this paper, we develop an improved GA in order to obtain better optimization result for JSP.…”

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“…The results of the execution of JSP-PSO are compared to the optimal solutions and the best results taken from an efficient ant colony optimization 8 in Table 1. JSP-PSO can find the optimal solutions to 17 out of 33 instances, and it outperforms the ant colony optimization in 13 instances, and does worse than it in only 9 instances.…”

“…This difficulty is perhaps the reason why most scheduling is still done in an ad hoc manner. Because of these two difficulties, many researchers have thus turned their attention to population-based stochastic search methods, e.g., genetic algorithms [1][2][3][4] , ant colony optimizations [5][6][7][8] , and particle swarm optimizations 9 , which are able to find near-optimal solutions within an acceptable computation time.…”