Embedding ant system in genetic algorithm for re-entrant hybrid flow shop scheduling problems with time window constraints

Chamnanlor, Chettha; Sethanan, Kanchana; Gen, Mitsuo; Chien, Chen–Fu

doi:10.1007/s10845-015-1078-9

Cited by 50 publications

(17 citation statements)

References 47 publications

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“…Our performance comparison between the differential evolution algorithm and particle swarm optimization showed that the differential evolution algorithm performed better than particle swarm optimization in terms of quality, resolution and computation time, while hybrid DEPSO algorithms provided better answers than the differential evolution algorithm and the particle swarm optimization algorithm, which took more time to compute. The superiority of hybrid DEPSO over the differential evolution algorithm and particle swarm optimization has also been demonstrated in other research fields, such as flexible flow shop scheduling (Chamnanlor et al (2017), Sangsawang et al (2015), Batur et al (2016)) and hybrid DEPSO algorithms (Jayabarathi et al (2007), Li et al (2008), Li et al (2014)).…”

Section: Discussionmentioning

confidence: 89%

“…In scheduling flexible flow shop problems, Baumann and Trautmann (2011) developed mixed integer programming for minimizing changeover times, but this method can only solve small problems. Chamnanlor and Sethanan (2015), Chamnanlor et al (2017), andSangsawang et al (2015) developed hybrid particle swarm optimization with a Cauchy distribution for the optimal sequencing of problems involving a reentrant hybrid flow shop scheduling problem with time window for minimizing makespan. Batur et al (2016) used a simulated annealing based heuristic for scheduling problems arising in hybrid flexible flow shop problems that repeatedly produce a set of multiple part types.…”

Section: Discussionmentioning

confidence: 99%

“…The flexible flow shop problem is an NPhard problem, and metaheuristic algorithms have been presented as the most optimal way to handle such large problems (Hoogeveen et al, 1996;Gupta et al, 2002;Wang and Hunsucker, 2003;Baumann and Trautmann, 2011). Many algorithms have been designed to help solve these problems, including tabu search algorithms (Widmer, 1991), hybridization of particle swarm optimization with Cauchy distribution for optimal sequences , a modified genetic algorithm for hybridized ant colony optimization (Chamnanlor et al, 2017), and a simulated annealing based heuristic (Batur et al, 2016). Flexible flow shop problems are complex.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for the Sugarcane Cultivation Scheduling Problem

Worasan¹,

Sethanan²,

Moonsri³

2018

CMUJNS

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This paper focuses on optimizing scheduling solutions for the flexible flow shop problem, with tooling constraints and machine eligibility, to minimize makespan for cultivating sugarcane. Normally, preparing the soil for planting sugarcane requires six steps: 1) 7 power harrow and rototiller, 2) rotary mini combine, 3) 22/24 disc harrow, 4) rotary mini combine, 5) sugarcane plantation, and 6) sugarcane sprayer. Each of these steps requires a variety of tools. With limited availability of tools and equipment, resource allocation is important. The objective of this research was to minimize the makespan. For optimal convergence, meta-heuristics, such as a Differential Evolution algorithm, a ParticleSwarm optimization algorithm, and a Hybrid DEPSO algorithm were developed to solve the problem. Experimental results showed that all three methods efficiently solved flexible flow shop problems.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for the Sugarcane Cultivation Scheduling Problem

Worasan¹,

Sethanan²,

Moonsri³

2018

CMUJNS

Self Cite

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“…Sangsawang et al [22] designed a two-stage RHFS problem with blocking constraints, aiming at minimizing makespan. Chamnanlor et al [23] studied the RHFS problem with time window constraints, which often occurred in the 2…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Two-Stage Joint Optimization Problem of Green Manufacturing and Maintenance for Semiconductor Wafer

Dong

2020

Mathematical Problems in Engineering

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This paper proposes a two-stage joint optimization problem of green manufacturing and maintenance for semiconductor wafer (TSGMM-SW) considering manufacturing stage, inspection, and repair stage simultaneously, which is a typical NP-hard problem with practical research significance and value. Aiming at this problem, a green scheduling model with the objective of minimizing makespan, total carbon emissions, and total preventive maintenance (PM) costs is constructed, and an improved hybrid multiobjective multiverse optimization (IHMMVO) algorithm is proposed in this paper. The joint optimization of green manufacturing and maintenance is realized by designing synchronous scheduling and maintenance strategy for wafer manufacturing and equipment PM. The diversity of the population is expanded and the optimization performance of IHMMVO is improved by designing the initial population fusion strategy and subpopulation evolution strategy. In the experimental phase, we perform the simulation experiments of 900 test cases randomly generated from 90 parameter combinations. The IHMMVO algorithm is compared with other existing algorithms to verify the effectiveness and feasibility for TSGMM-SW.

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“…Chamnanlor et al. () addressed the minimization of makespan in an MOHFS problem with time window constraints, and for solving it they proposed a hybrid algorithm that combines genetic algorithms and ant colony optimization (Dorigo and Stützle, ) methods. The results showed great efficiency of the proposed algorithm.…”

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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Embedding ant system in genetic algorithm for re-entrant hybrid flow shop scheduling problems with time window constraints

Cited by 50 publications

References 47 publications

Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for the Sugarcane Cultivation Scheduling Problem

Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for the Sugarcane Cultivation Scheduling Problem

Research on Two-Stage Joint Optimization Problem of Green Manufacturing and Maintenance for Semiconductor Wafer

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

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