An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem

Dai, Min; Tang, Dunbing; Zheng, Kun; Cai, Qixiang

doi:10.1155/2013/124903

Cited by 11 publications

(12 citation statements)

References 35 publications

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“…This approach has already been successfully applied by Vallada and Ruiz (2011). The specific combination of simulated annealing and genetic algorithms, as proposed later in this paper, is not entirely novel and similar applications have been developed successfully by several authors (Wang, Yang, and Li 2009;Liu, Olszewski, and Goh 2011;Dai et al 2013).…”

Section: Literature On the Parallel Machine Scheduling Problemmentioning

confidence: 90%

Scheduling of unrelated parallel machines with limited server availability on multiple production locations: a case study in knitted fabrics

Kerkhove

Vanhoucke

2013

International Journal of Production Research

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This paper studies a complex variation of the parallel machine scheduling (PMS) problem, as encountered at a Belgian producer of knitted fabrics. The aim is to assign N J jobs to N M unrelated parallel machines, minimising a weighted combination of job lateness and tardiness. Jobs are assigned specific release, and due dates and changeover times are sequence dependent. Current literature is extended by including geographically dispersed production locations, which influence job due dates and objective function coefficients. Furthermore, the changeover interference due to limited availability of technicians is also studied in this paper. The scheduling problem is solved using a hybrid meta-heuristic, which combines elements from simulated annealing and genetic algorithms. This hybrid meta-heuristic is capable of solving real-scale scheduling problems of up to 750 jobs, 75 machines and 10 production locations within reasonable computation time. This hybrid scheduling procedure is extended with heuristic dispatching rules capable of reducing the impact of changeover interference by 23% on average compared to the random scenario, for the case where a single technician is expected to serve up to 12 machines

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Section: Literature On the Parallel Machine Scheduling Problemmentioning

confidence: 90%

Scheduling of unrelated parallel machines with limited server availability on multiple production locations: a case study in knitted fabrics

Kerkhove

Vanhoucke

2013

International Journal of Production Research

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“…The annealing rate has a significant effect on the efficiency of the simulated annealing algorithm [55]. In order to improve the search efficiency of the algorithm, a new annealing rate method based on the hormone regulation mechanism [27] is used in this paper.…”

Section: Annealing Ratementioning

confidence: 99%

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Wei

Jiang

et al. 2018

Applied Sciences

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Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

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“…According to our previous study in [19], the elements and their associated positions in the matrix can denote the constraints among the jobs, which makes one matrix-chromosome always represent a legal and feasible schedule. In this paper, the matrix encoding approach is adopted for FFSP based on energy consumption.…”

Section: The Matrix Encoding and Decoding Rulementioning

confidence: 99%

“…subject to n down F (K) 1/(1 K ) T T(K 1) T(K) (19) where represents a small constant, and K represents iterations' number.…”

Section: Sa Design Based On Hormone Modulation Mechanismmentioning

confidence: 99%

Energy-aware scheduling model and optimization for a flexible flow shop problem

Dai

Tang

Zhang

et al. 2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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Nowadays, manufacturing enterprises consumes a significant amount of energy; consequently, it has a significant potential to reduce resource consumption. However, key performance indicators of the traditional production do not completely take into account environmental impacts like energy consumption in production planning and scheduling. Against this background, an energy-aware scheduling model for a flexible flow shop problem is proposed. First, a mixed integer programming model based on energy savings is established to minimize energy consumption and makespan. Then, an improved heuristic algorithm, which combines genetic algorithm with simulated-annealing algorithm, is adopted to solve the mathematical model. Finally, a case study in a plant is tested. The experimental results show effectiveness of the model.

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An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem

Cited by 11 publications

References 35 publications

Scheduling of unrelated parallel machines with limited server availability on multiple production locations: a case study in knitted fabrics

Scheduling of unrelated parallel machines with limited server availability on multiple production locations: a case study in knitted fabrics

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Energy-aware scheduling model and optimization for a flexible flow shop problem

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