Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization

Yu, Shun-Chi

doi:10.1016/j.asoc.2014.04.016

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They investigated the master-slave approaches and developed several different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by communication strategies in multiprocessor architectures. Yu [210] proposed the incorporation of a local search heuristic into the basic PSO algorithm. The new, hybrid metaheuristic was called twin PSO (TPSO).…”

Section: Parallel Implementationmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

698

380

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

show abstract

Section: Parallel Implementationmentioning

confidence: 99%

“…Waintraub et al [209] and Yu [210] GPU Hung and Wang [211], Rymut et al [212], Kumar et al [213], Awwad et al [214], and Chen et al [215] Cloud Liu et al [216], Xu and You [217], Ramezani et al [218], Govindarajan et al [219], and Ramezani et al [220] Table 5: Application areas of PSO algorithm.…”

Section: Trends Of Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

698

380

View full text Add to dashboard Cite

show abstract

“…At present, many algorithms are used to solve flowline scheduling problems, such as genetic algorithm [37], particle swarm optimization algorithm [38], simulated annealing algorithm [39] and so on. The whale optimization algorithm (WOA) is a new meta heuristic optimization algorithm which simulates the hunting behavior of humpback whales by Mirjalili in 2016 [40].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Whale Optimization Algorithm for Plane Block Parallel Blocking Flowline Scheduling Optimization With Deterioration Effect in Lean Shipbuilding

Guo

2021

IEEE Access

View full text Add to dashboard Cite

Plane block parallel blocking flowline is a special parallel blocking flowline, which can also be called incomplete hybrid flowline. However, the existing scheduling work has less research on this problem and lacks the constraint of considering the deterioration effect. Therefore, this paper studies the plane block parallel blocking flowline scheduling problem considering the deterioration effect (PBFSP-DE). This is a typical NP hard problem, so this paper proposes a hybrid whale optimization algorithm (HWOA) for this problem. In the HWOA, tent map of chaotic map is introduced to initialize the population, to improve the diversity of the initial population and effectively improve the optimization performance of the algorithm. Then, a hybrid strategy combining adaptive weight factor and Gaussian random disturbance is used to update the whale's position, to provide strong global exploration ability and avoid falling into local optimum. In addition, Levy flight mechanism is applied to the current optimal individuals to improve the efficiency of local search. Finally, the results show that the HWOA proposed in this paper has strong engineering practicability.

show abstract

“…Almost all of the authors studying HFSMT proposed metaheuristics, such as a particle swarm optimization by Tseng et al (Tseng and Liao, 2008), a heuristic coupled with two local search methods by Ying et al (Ying and Lin, 2009), a memetic algorithm that combines GA and constraint programming by Jouglet et al (2009), a parallel greedy approach by Kahraman et al (2010), a simulated annealing including three different decoding methods by Wang et al (2011), PSO algorithm by Chou (2013), a shuffled frog-leaping algorithm for HFSMT by Xu et al (2013), a new discrepancy search called climbing depth-bounded adjacent discrepancy search by Lahimer et al (2013), a new hybrid metaheuristic called “twin particle swarm optimization” algorithm by Yu (2014) and an improved cuckoo search metaheuristic algorithm by Marichelvam et al (2014), were presented for the HFSMT problem. Engin et al (2011) proposed a GA for HFSMT and they stated that their results suggested that the computational performance of GA depends on the factors of initial population, reproduction, crossover and mutation operators and coefficients.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid flow shop with multiprocessor task scheduling based on earliness and tardiness penalties

Engin

2018

JEIM

View full text Add to dashboard Cite

Purpose Hybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under the environment of a common time window. The window size and location are considered to be given parameters. The research deals with the criterion of total penalty cost minimization incurred by earliness and tardiness of jobs. In this research, a new memetic algorithm in which a global search algorithm is accompanied with the local search mechanism is developed to solve the HFSMT with jobs having a common time window. The operating parameters of memetic algorithm have an important role on the quality of solution. In this paper, a full factorial experimental design is used to determining the best parameters of memetic algorithm for each problem type. Memetic algorithm is tested using HFSMT problems. Design/methodology/approach First, hybrid flow shop scheduling system and hybrid flow shop scheduling with multiprocessor task are defined. The applications of the hybrid flow shop system are explained. Also the background of hybrid flow shop with multiprocessor is given in the introduction. The features of the proposed memetic algorithm are described in Section 2. The experiment results are presented in Section 3. Findings Computational experiments show that the proposed new memetic algorithm is an effective and efficient approach for solving the HFSMT under the environment of a common time window. Originality/value There is only one study about HFSMT scheduling with time window. This is the first study which added the windows to the jobs in HFSMT problems.

show abstract

Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization

Cited by 8 publications

References 16 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Hybrid Whale Optimization Algorithm for Plane Block Parallel Blocking Flowline Scheduling Optimization With Deterioration Effect in Lean Shipbuilding

Hybrid flow shop with multiprocessor task scheduling based on earliness and tardiness penalties

Contact Info

Product

Resources

About