Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

Abstract

In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms.Note to Practitioners-The flexible job shop scheduling problem (FJSP) has important applications in textile, automobile assembly, semiconductor manufacturing, and many other industries. In the flexible job shop, a group of machines are capable for each operation, which is different from the traditional job shop environment where each operation can be processed by only a single machine. The FJSP is quite challenging, since the decisions include not only operation sequencing but also machine assignment. In the literature, the majority of studies for the FJSP are centered on optimizing the makespan. However, a single objective is deemed as insufficient for real and practical applications. Indeed, in the industry, production managers are usually concerned with more than one objective. This paper aims to simultaneously minimize the makespan, total workload, and critical workload for the FJSP, which can lead to both high throughput and load balance of machines. This problem is solved in the posterior approach, whose goal is to seek for a set of Pareto optimal solutions. We propose ef-fective memetic algorithms (MAs) that combine a classical multiobjective evolutionary technique referred as NSGA-II with a novel problem-specific local search. To enhance the ability to deal with multiple objectives, a hierarchical strategy is used in local search which gives varying degrees of consideration to each objective. The effectiveness of the proposed MAs is well demonstrated by extensive comparisons aga...

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

“…, , 푘 ∈ cor }, which contains ∑ 푛 푖=1 cor actions. Now, a hierarchical strategy [27] is adopted to calculate Δ and Δ which, respectively, represent…”

“…With the aim of keeping population diversity, immune and entropy principle were adopted in multiobjective genetic algorithm (MOGA) [25]. Two memetic algorithms (MAs) were, respectively, proposed, both of which integrate nondominated sorting genetic algorithm II (NSGA-II) [6] with effective local search techniques [26,27]. Several effective neighborhood approaches were used in variable neighborhood search to enhance the convergence ability in a hybrid Pareto-based local search (PLS) [28].…”

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.…”

Solving the Problem of Multi-objective Flexible Job Shop Based on Hybrid Genetic Algorithm and Particle Swarm Optimization

“…Ho and Tay [6,7] proposed a hybrid evolutionary algorithm with local search, and meanwhile introduced an elitism memory to store all non-dominated solutions that have been found. Yuan and Xu proposed a new memetic algorithms (MAs) by incorporating a local search algorithm into the adapted NSGA-II for MOFJSP [8]. Xia and Wu [9] proposed a hybrid method to solve MOFJSP.…”