A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem

Wang, Hongfeng; Fu, Yaping; Huang, Min; Huang, George Q.; Wang, Junwei

doi:10.1016/j.cie.2017.09.009

Cited by 72 publications

(20 citation statements)

References 39 publications

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“…During the last year, memetic algorithm used some scheduling problems in the literature: for parallel machine scheduling [44], flow shop [45][46][47] job shop [48] open shop [49] staff shop [50].…”

Section: Memetic Global and Local Search Algorithmmentioning

confidence: 99%

A new memetic global and local search algorithm for solving hybrid flow shop with multiprocessor task scheduling problem

Engin¹,

Engin

2020

SN Appl. Sci.

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Hybrid flow shop (HFS) scheduling problem is combining of the flow shop and parallel machine scheduling problem. Hybrid flow shop with multiprocessor task (HFSMT) scheduling problem is a special structure of the hybrid flow shop scheduling problem. The HFSMT scheduling is a well-known NP-hard problem. In this study, a new memetic algorithm which combined the global and local search methods is proposed to solve the HFSMT scheduling problems. The developed new memetic global and local search (MGLS) algorithm consists of four operators. These are natural selection, crossover, mutation and local search methods. A preliminary test is performed to set the best values of these developed new MGLS algorithm operators for solving HFSMT scheduling problem. The best values of the MGLS algorithm operators are determined by a full factorial experimental design. The proposed new MGLS algorithm is applied the 240 HFSMT scheduling instances from the literature. The performance of the generated new MGLS algorithm is compared with the genetic algorithm (GA), parallel greedy algorithm (PGA) and efficient genetic algorithm (EGA) which are used in the previous studies to solve the same set of HFSMT scheduling benchmark instances from the literature. The results show that the proposed new MGLS algorithm provides better makespan than the other algorithms for HFSMT scheduling instances. The developed new MGLS algorithm could be applicable to practical production environment.

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Section: Memetic Global and Local Search Algorithmmentioning

confidence: 99%

A new memetic global and local search algorithm for solving hybrid flow shop with multiprocessor task scheduling problem

Engin¹,

Engin

2020

SN Appl. Sci.

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“…Wang et al. () proposed a multiobjective memory algorithm to solve an MOHFS problem. The objectives are to minimize the total flowtime and the number of overdue jobs.…”

Section: Literature Reviewmentioning

confidence: 99%

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

Siqueira

Souza

2019

Int Trans Operational Res

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This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO‐GVNS) metaheuristic, named adapted MO‐GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO‐GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA‐II), and NSGA‐III, and another MO‐GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.

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“…Recently, combinations of decomposition mechanism and NSGA-II components were successfully used to solve combinatorial problems [47]. Latest research studies also suggest the effectiveness of NSGA-II for solving similar scheduling problems [48][49][50]. us, in this paper, we choose NSGA-II as the baseline algorithm for comparison.…”

Section: Effect Of Algorithm Componentsmentioning

confidence: 99%

Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided Neighborhoods

2019

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Real-world multiobjective optimization problems are characterized by multiple types of decision variables. In this paper, we address weapon selection and planning problems (WSPPs), which include decision variables of weapon-type selection and weapon amount determination. Large solution space and discontinuous, nonconvex Pareto front increase the difficulty of problem solving. This paper solves the addressed problem by means of a multiobjective evolutionary algorithm based on decomposition (MOEA/D). Two mechanisms are designed for the complex combinatorial characteristic of WSPPs. The first is that the neighborhood of each individual is divided as selection and replacement neighborhoods. The second is that the neighborhood size is changing during the evolution by introducing a distance parameter to constrain the search scope of each subproblem. The proposed algorithm is termed as MOEA/D with distance-based divided neighborhoods (MOEA/D-DDNs) which can overcome possible drawbacks of original MOEA/D with weighted sum approach for complex combinatorial problems. Benchmark instances are generated to verify the proposed approach. Experimental results suggest the effectiveness of the proposed algorithm.

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A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem

Cited by 72 publications

References 39 publications

A new memetic global and local search algorithm for solving hybrid flow shop with multiprocessor task scheduling problem

A new memetic global and local search algorithm for solving hybrid flow shop with multiprocessor task scheduling problem

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided Neighborhoods

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