A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

Abstract

The Flexible Job-Shop Scheduling Problem is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying several parallel goals. We introduce a Memetic Algorithm, based on the NSGAII (NonDominated Sorting Genetic Algorithm II) acting on two chromosomes, to solve this problem. The algorithm adds, to the genetic stage, a local search procedure (Simulated Annealing). We have assessed its efficiency by running the algorithm on multiple objective instances of the problem. We draw statistics from those runs, which indicate that this Memetic Algorithm yields good and low-cost solutions.

Multi-Objective for a Partial Flexible Open Shop Scheduling Problem Using a Priority-Based Evolutionary Algorithm

“…Li et al [50] developed an hybrid method for combining the variable neighborhood search and the EAs to solve the multi-objective FOSP. Frutos et al [25] used the EA with simulated annealing to join local and global search for solving multi-objective FOSP. Wang et al [77] and Gao et al [30] used a EA which is related on immune and entropy principles to solve the multi-objective FOSP.…”

“…Rahmati et al [67] developed non-dominated sorting of EA and non dominated ranking EA for multi-objective PFOSP and he proposed new multi-objective Pareto-based modules and a new measure for the multi-objective evaluation. [42] 2002 FOSP EA + AL Baykasoglu et al [7] 2004 FOSP TS + PDR Xia and Wu [79] 2005 FOSP PSO + SA Gao et al [26] 2006 FOSP EA Gao et al [27] 2007 FOSP EA + BSP Zribi et al [89] 2007 FOSP EA + BBA + LS Gao et al [28] 2008 FOSP EA + VNS Tay and Ho [75] 2008 FOSP EA + PDR Wang et al [76] 2008 FOSP FBS + PDR Zhang et al [87] 2009 FOSP PSO + TS Li et al [50] 2010 FOSP EA + VNS Frutos et al [25] 2010 FOSP EA + SA Wang et al [77] 2010 FOSP EA + AIS Gao et al [30] 2010 FOSP EA + AIS Grobler et al [35] 2010 FOSP PSO + PDR Li et al [48] 2010 FOSP TS + VNS Moradi et al [58] 2011 FOSP EA + PDR Moslehi and Mahnam [59] 2011 FOSP PSO + LS Li et al [49] 2011 FOSP PSO Li et al [47] 2011 FOSP PSO Rajkumar et al [68] 2011 FOSP GRASP Chiang and Lin [17] 2013 FOSP EA Rahmati et al [67] 2013 FOSP Gas Shao et al [72] 2013 FOSP PSO + SA Gao et al [29] 2014 FOSP HSA + LS Jia and Hu [41] 2014 FOSP TS Karthikeyan et al [45] 2014 FOSP DFA + LS Li et al [51] 2014 FOSP PSO + TS Rohaninejad et al [69] 2015 FOSP EA Yuan and Xu [84] 2015 FOSP EA + LS Rohaninejad et al [69] proposed a nonlinear IP model and also the hybridized EA with meta-heuristic, which is a multi-attribute decision making method, for multi-objective PFOSP with machines capacity constraints. The computational results are obtained by well-known multi objective algorithms from the literature showed that the proposed algorithm to obtain throughout better performance, especially in the closeness of the solutions result to the Pareto optimal front.…”

Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes

“…The results obtained by means of HGA were compared to those yield by Greedy Randomized Adaptive Search Procedures (GRASP) (Binato et al, 2001), Taboo Search (TS) (Armentano & Scrich, 2000) and Ant Colony Optimization (ACO) (Heinonen & Pettersson, 2007). For the problems MF01, MF02, MF03, MF04 and MF05 (Frutos et al, 2010), we show the results for the multi-objective analysis based on Makespan (f 1 , (1)) and Total Operation Costs (f 2 , (2)). They were obtained by running each algorithm 10 times.…”

“…HGA (1) GRASP (2) TS ( (Frutos et al, 2010), (2) (Binato et al, 2001), (3) (Armentano & Scrich, 2000) and (4) (Heinonen & Pettersson, 2007) (Frutos et al, 2010), (2) (Binato et al, 2001), (3) (Armentano & Scrich, 2000) and (4) (Heinonen & Pettersson, 2007) (Frutos et al, 2010), (2) (Binato et al, 2001), (3) (Armentano & Scrich, 2000) and (4) (Heinonen & Pettersson, 2007) (Frutos et al, 2010), (2) (Binato et al, 2001), (3) (Armentano & Scrich, 2000) and (4) (Heinonen & Pettersson, 2007) (Frutos et al, 2010), (2) (Binato et al, 2001), (3) (Armentano & Scrich, 2000) and (4) (Heinonen & Pettersson, 2007) In order to compare the results of the algorithms and establish the better option for the Flexible JSSP, several tests were applied over the solutions. First, we consider a dominance ranking among the different algorithms.…”

“…It requires to rely on efficient optimization procedures. In fact, the JobShop Scheduling Problem (JSSP) is a NP-Hard problem (Ullman, 1975), so ad-hoc algorithms have to be applied to its solution (Frutos et al, 2010). This is similar to other combinatorial programming problems (Olivera et al, 2006), (Cortés et al, 2004).…”