Nonpermutation flow line scheduling by ant colony optimization

Rossi, Andrea; Lanzetta, Michele

doi:10.1017/s0890060413000176

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Inspired from the nature, new optimization techniques are devised and being investigated in various fields recently (Eby et al ., 1999). Among these techniques, some have proved to be drastically effective in engineering-related subjects such as GA (Su and Yan, 2015), artificial ant colony (Rossi and Lanzetta, 2013), SA (Brown and Cagan, 1997), and PSO (Badamchizadeh et al ., 2010).…”

Section: Intelligent Optimization Proceduresmentioning

confidence: 99%

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Pourmostaghimi

Zadshakoyan

Badamchizadeh

2020

AIEDAM

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This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

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Section: Intelligent Optimization Proceduresmentioning

confidence: 99%

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Pourmostaghimi

Zadshakoyan

Badamchizadeh

2020

AIEDAM

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“…Finally, our model could be extended to other shop scheduling problems (e.g. flow-shop and open-shop) since they can be represented by disjunctive graphs [41,42]. While the first advantage will be demonstrated in the experiments, we plan to further explore the latter two in the future.…”

Section: Parameterizing the Policymentioning

confidence: 99%

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Zhang,

Song,

Cao

et al. 2020

Preprint

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Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw features, and demonstrates strong performance against the best existing PDRs. The learned policies also perform well on much larger instances that are unseen in training.

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“…In addition, CPT is a commonly used soil exploration survey in engineering applications because it is quick, economic, and could continuously obtain physical soil parameters (Eslami, 1997; Abu-Farsakh and Titi, 2004). In recent years, some researchers developed concepts of advanced artificial intelligence (AI) in different engineering applications, such as engineering mechanics, design of energy-saving buildings, automation, and construction (Coello and Aguirre, 2002; Nariman-Zadeh et al ., 2003; Hanna, 2007; Padmini et al ., 2008; Rossi and Lanzetta, 2013; Hu et al ., 2014; Zeng et al ., 2014; Kontovourkis et al ., 2015; Salido et al ., 2015; Adeniran and El-Ferik, 2017; Persson and Ölvander, 2017; Thimmisetty et al ., 2017; Al-Refaie et al ., 2018; Keles, 2018; Lin and Huang, 2018; Mirabi and Seddighi, 2018; Zimmermann et al ., 2018). In recent years, applications of AI models have become widespread through civil engineering projects (e.g., Armaghani et al ., 2017, 2018; Koopialipoor et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

Harandizadeh

2020

AIEDAM

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This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

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Nonpermutation flow line scheduling by ant colony optimization

Cited by 7 publications

References 21 publications

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

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