A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

Pongchairerks, Pisut

doi:10.1155/2019/8683472

Cited by 27 publications

(44 citation statements)

References 43 publications

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“…In this research, we evaluate the merit and the limitation of the approaches by comparing the results of some heuristic algorithms, including Ant Colony Optimization (ACO) [15], Particle Swarm Optimization (PSO) [33], Tabu Search (TS) [14], Upper-level algorithm (UPLA) [26], Differentialbased Harmony Search (DHS) [16], Grey Wolf Optimization (GWO) [28], Bacterial Foraging Optimization (BFO) [30], Parallel Bat Optimization (PBA) [17], and the proposed Genetic Algorithm (GA). The performances are measured based on the solution quality, the number of instances solved (NIS) optimally, and the relative error.…”

Section: Comparison Of Some Heuristic Methodsmentioning

confidence: 99%

“…However, the two-level PSO is different from the suggested two-level metaheuristic algorithm that uses GLN-PSO's framework [25]. The authors assessed the algorithms' performance on 53 well-known benchmark instances, including FT06, FT10, FT20, and LA01-LA40 [26]. Considering the similarity and difference, they also compared their results with those of the two-level PSO [24].…”

Section: Upper-level Algorithm (Upla)mentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

Syarif¹,

Pamungkas²,

Kumar³

et al. 2021

IJIES

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Scheduling is a famous optimization problem that seeks the best strategy of allocating resources over time to perform jobs/tasks satisfying specific criteria. It exists everywhere in everyday life, particularly in manufacturing or industrial applications. An essential class of scheduling problems is a job shop scheduling problem (JSSP), an NPhard optimization problem. Several researchers have reported the use of heuristic methods to solve JSSP. This paper aims to investigate the performance of various heuristic algorithms to solve JSSP. Firstly, we developed a Genetic Algorithm (GA and compared the performance of some heuristic algorithms, including Particle Swarm Optimization (PSO), Upper-level algorithm (UPLA), Differential-based Harmony Search (DHS), Grey Wolf Optimization (GWO), Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Parallel Bat Optimization (PBA), and Tabu Search (TS). The experimental results of the 28 benchmark test problems validated that the algorithms, except ACO, can provide the optimal solution of JSSP. PBA delivers the most impressive performance that solves 26 cases optimally, with the average error equal to 0.05%. Among those 28 test problems, TS, DHS, and PBA can solve 26 instances optimally, followed by GA that solves 21 cases.

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Section: Comparison Of Some Heuristic Methodsmentioning

confidence: 99%

Section: Upper-level Algorithm (Upla)mentioning

confidence: 99%

Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

Syarif¹,

Pamungkas²,

Kumar³

et al. 2021

IJIES

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show abstract

“…Genetic algorithms invented by John Holland [20], have been proven to be an effective technique for many hard problems such as production-distribution planning problems [21], transportation and network design [45], Scientific workflow scheduling [43], task scheduling in cloud computing [34], [44], topology or size optimization [24]. However, it is not suitable to directly apply the aforementioned algorithms to the problems of VONs mapping in EONs, and it is necessary to make some improvements or revisions on them.…”

Section: B Bi-level Mathematical Problem and Genetic Algorithmmentioning

confidence: 99%

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

et al. 2020

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Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader's and follower's objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bilevel mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.

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“…Cruz-Chávez et al [ 19 ] proposed a parallel algorithm that generated a set of parallel working threads, where each thread performed a simulated annealing process to solve JSSP. For JSSP, Pongchairerks [ 20 ] proposed a new two-level meta-heuristic algorithm composed of an upper-level algorithm and a lower-level algorithm.…”

Section: Introductionmentioning

confidence: 99%

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

Tian¹,

Liu²,

Zhang³

et al. 2020

PLoS ONE

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A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.

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A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

Cited by 27 publications

References 43 publications

Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

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