A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints

Abstract

A greedy randomised adaptive search procedure (GRASP) is an iterative multistart metaheuristic for difficult combinatorial optimisation. The GRASP iteration consists of two phases: a construction phase, in which a feasible solution is found and a local search phase, in which a local optimum in the neighbourhood of the constructed solution is sought. In this paper, a GRASP algorithm is presented to solve the flexible job-shop scheduling problem (FJSSP) with limited resource constraints. The main constraint of this scheduling problem is that each operation of a job must follow an appointed process order and each operation must be processed on an appointed machine. These constraints are used to balance between the resource limitation and machine flexibility. The model objectives are the minimisation of makespan, maximum workload and total workload. Representative benchmark problems are solved in order to test the effectiveness and efficiency of the GRASP algorithm. The computational result shows that the proposed algorithm produced better results than other authors' algorithms.

GRASP to minimize total weighted tardiness in a permutation flow shop environment

“…Similarly Armentano and Araujo (2006) minimized total tardiness considering setup times and Armentano and de França Filho (2007) also used for a parallel machine problem. Moreover, Arroyo and de Souza Pereira (2010) and Shahul Hamid Khan et al (2007) solved multi-objective PFS problems and Rajkumar et al (2011) solved a flexible job shop problem. Therefore, this research proposes the use of GRASP metaheuristic to solve the FS|prmu|∑wjTj.…”

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

“…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.…”

“…Zee [88] developed a heuristic algorithm for PFOSP minimizing the make span which can obtain high quality solutions in very short time duration. Rajkumar et al [68] proposed a GRASP algorithm to prove the multi objectives of FOSP to be solved with limited resource constraints. Karimi et al [44] proposed a knowledge-based variable neighborhood search algorithm is to search the solution space for neighborhood solutions, extracts the knowledge of good solution and feed it back to the algorithm.…”

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

“…Among metaheuristics, GRASP (Greedy Randomized Adaptive Search Procedure) is an iterative multistart metaheuristic for difficult combinatorial problems (Rajkumar et al, 2011). The GRASP Iteration consists of a construction phase and an improvement phase.…”

“…The best overall solution, when a given termination criterion is met, is returned as the output (João et al, 2014). Compared to other metaheuristics, GRASP appears to be competitive with respect to the number of parameters to tune (only two: the size of the candidate list and the stopping criterion), the quality of the solutions, and the low implementation complexity (Rajkumar et al, 2011). Successful applications of GRASP to solve scheduling problems can be found in (João et al, 2014;Rajkumar et al, 2011;Rajkumar et al, 2010).…”