A bi-level stochastic optimization model for reliable supply chain in competitive environments: Hybridizing exact method and genetic algorithm

Setak, Mostafa; feizizadeh, fariba; Tikani, Hamid; Ardakani, Elham Shaker

doi:10.1016/j.apm.2019.05.037

Cited by 28 publications

(11 citation statements)

References 49 publications

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“…The bi-level model was simplified single level using the KKT approach and evaluated through a case study. Another study was devoted to modeling supply chain competition under uncertainty [39]. The model was firstly proposed to determine distribution facilities' locations and number, retailers' allocation, and selling price.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Adopting Quick Response and Agility on Supply Chain Competition with Strategic Customer Behavior

2020

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A growing trend towards computerization and competition in supply chains results in uncertainty and quick variability that make the decisions difficult for both levels of retailers and manufacturers. In this paper, two Bi-Level Stackelberg models are developed under nonand agile conditions in the presence of strategic customers. Our main novelty approach in this paper is to consider both levels competing with each other in a sequential game to determine the optimal production and order quantities and prices with and without agile abilities. In addition, both proposed models are simplified single-level using the Karush-Kuhn-Tucker (KKT) approach. Then, they are remodeled by a Robust Optimization technique due to existing uncertain parameters. To have a better assessment of the models' efficiency and applicability, they have been implemented in a real case and finally, the results are compared and analyzed.

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Section: Literature Reviewmentioning

confidence: 99%

Impact of Adopting Quick Response and Agility on Supply Chain Competition with Strategic Customer Behavior

2020

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“…Researchers always seek to improve the effectiveness of the methods that they use. Metaheuristics have become a popular approach in tackling the complexity of practical optimization problems [29][30][31][32][33]. Owing to the continuous development of artificial intelligence (AI) technology, many intelligent algorithms are now used in parameter optimization, including the genetic algorithm (GA) [34] and the particle swarm optimization algorithm (PSO) [35].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management

Chou

Pham

2021

Applied Sciences

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Multi-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. Multi-class classification is more complex than binary classification. In binary classification, only the decision boundaries of one class are to be known, whereas in multiclass classification, several boundaries are involved. The objective of this investigation is to propose a metaheuristic, optimized, multi-level classification learning system for forecasting in civil and construction engineering. The proposed system integrates the firefly algorithm (FA), metaheuristic intelligence, decomposition approaches, the one-against-one (OAO) method, and the least squares support vector machine (LSSVM). The enhanced FA automatically fine-tunes the hyperparameters of the LSSVM to construct an optimized LSSVM classification model. Ten benchmark functions are used to evaluate the performance of the enhanced optimization algorithm. Two binary-class datasets related to geotechnical engineering, concerning seismic bumps and soil liquefaction, are then used to clarify the application of the proposed system to binary problems. Further, this investigation uses multi-class cases in civil engineering and construction management to verify the effectiveness of the model in the diagnosis of faults in steel plates, quality of water in a reservoir, and determining urban land cover. The results reveal that the system predicts faults in steel plates with an accuracy of 91.085%, the quality of water in a reservoir with an accuracy of 93.650%, and urban land cover with an accuracy of 87.274%. To demonstrate the effectiveness of the proposed system, its predictive accuracy is compared with that of a non-optimized baseline model, single multi-class classification algorithms (sequential minimal optimization (SMO), the Multiclass Classifier, the Naïve Bayes, the library support vector machine (LibSVM) and logistic regression) and prior studies. The analytical results show that the proposed system is promising project analytics software to help decision makers solve multi-level classification problems in engineering applications.

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“… Afshari et al, 2016 ; Aydin, 2020 ; Ayvaz et al, 2015 ; Bertsimas and Sim, 2004 ; Chakraborty et al, 2018 ; De Rosa et al, 2013 ; Demirel et al, 2016 ; Doan et al, 2018 ; Fang et al, 2011 ; Feitó-Cespón et al, 2017 ; Ghadge et al, 2016 ; Gu and Gao, 2012 ; Hamdan and Diabat, 2020 ; Hamidieh et al, 2017 ; Hatefi and Jolai, 2014 ; Hatefi et al, 2015 ; Hatefi et al, 2016 ; Heydari and Ghasemi, 2018 ; Heydari et al, 2018 ; Hosseini-Motlagh et al, 2019 ; Hosseini-Motlagh et al, 2020 ; Hosseini and Sarder, 2019 ; Hosseini and Ivanov, 2019 ; Hosseini et al, 2019a ; Hosseini et al, 2019b ; Ivanov et al, 2014a ; Ivanov et al, 2014b ; Ivanov et al, 2016 ; Ivanov et al, 2017 ; Jabbarzadeh et al, 2014 ; Jain et al, 2017 ; John et al, 2018 ; Kinra et al, 2019 ; Liao, 2018 ; Mohapatra et al, 2019 ; Niknejad and Petrovic, 2014 ; Pavlov et al, 2019 ; Pishvaee et al, 2011 ; Phuc et al, 2017 ; Qiaolun and Tiegang, 2012 ; Qiaolun and Tiegang, 2013 ; Ren et al, 2020 ; Sawik, 2017 ; Setak et al, 2019 ; Shuang et al, 2019 ; Suryawanshi and Dutta, 2019 ; Tosarkani and Amin, 2019 ; Tucker et al, 2020 ; Yu and Solvang, 2017 ; Zhang et al, 2018 ; Zhao et al, 2018 .…”

Section: Uncited Referencesunclassified

Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model

Yılmaz

Özçelik

Yeni

2021

Journal of Cleaner Production

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The devastating impact of the ripple effect increases the importance of the reverse supply chain (RSC) design to ensure sustainability in the long-term. That being the case, in this study, a two-stage stochastic mixed-integer optimization model is proposed to design an RSC network under uncertainty sourcing from the ripple effect (i.e. external side of RSC) by considering the environmental and economic dimensions of sustainability. The environmental and economic disruptions of the ripple effect are represented by the increase in the carbon emission levels and the distance of roads, and the decrease in the capacity of facilities, respectively. Accordingly, a set of scenarios is considered based on the disruption levels (low- and high-impact) in case of the ripple effect. Furthermore, an -reliability constraint is integrated into the model to further analyze the occurrence of scenarios. The model allows us to make integrated operational and strategic decisions by placing an emphasis on the carbon emission levels (i.e. environmental dimension) and the total cost (i.e. economic dimension). To obtain some remarkable insights, the proposed model is validated through computational experiments based on data extracted from a real case. The computational results show that the ripple effect increases the emission level and total cost up to 40%. For this reason, it suggested that the regulations regarding WEEE (Waste Electrical and Electronic Equipment) should be prepared by considering sustainability in the entire RSC network. Besides, it is realized that the centralized distribution strategy leads to a more resilient RSC network design.

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A bi-level stochastic optimization model for reliable supply chain in competitive environments: Hybridizing exact method and genetic algorithm

Cited by 28 publications

References 49 publications

Impact of Adopting Quick Response and Agility on Supply Chain Competition with Strategic Customer Behavior

Impact of Adopting Quick Response and Agility on Supply Chain Competition with Strategic Customer Behavior

Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management

Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model

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