A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks

Nezamoddini, Nasim; Gholami, Amirhosein; Aqlan, Faisal

doi:10.1016/j.ijpe.2019.107569

Cited by 41 publications

(17 citation statements)

References 47 publications

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“… I17 Supply chain network design SC network design represents the facility location problem, and SC management contains facility location determination, magnitude, network capabilities and the material flow among the located facilities. Pishvaee and Razmi (2012); Fattahi et al (2020 b); Benedito et al (2020) ; Hamdan & Diabat (2020) ; Fazli-Khalaf et al (2019) ; Diabat et al (2019) ; Snoeck et al (2019) ; Li & Zhang (2018) ; Jabbarzadeh et al (2018) ; Fattahi et al (2017) ; Jabbarzadeh et al (2013); Azad (2014); Hasani et al (2020) ; Nezamoddini et al (2020) . I18 Supply chain resilience SC resilience define as the SC adaptive ability to respond to disruptions, react to unexpected occasions, and then recover by continuously maintaining operations at the desired balanced of connectedness and control over the SC function and structure.…”

Section: Resultsmentioning

confidence: 99%

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

177

119

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Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

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Section: Resultsmentioning

confidence: 99%

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

177

119

View full text Add to dashboard Cite

show abstract

“…Govindan (2016) published the results of a literature search on the application of evolutionary algorithms applied to SCM and predicted growing interest in their application to advanced problems in SCM such as the one addressed in this article. Nezamoddini et al (2020) used a GA and an artificial neural network to manage risk by making strategic and tactical decisions for an SCN in the presence of uncertainty in disruption and demand. Sajedinejad and Chaharsooghi (2018) apply a GA to the problem of supplier selection in an SCN through multi-objective optimization.…”

Section: Scnd With Sbo and Gamentioning

confidence: 99%

A Simulation-based Optimization Approach to Logistic and Supply Chain Network Design

Jones¹,

Mazzuchi²,

Sarkani³

2021

Defense ARJ

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The Department of Defense (DoD) operates a world-wide supply chain, which in 2017 contained nearly 5 million items collectively valued at over $90 billion. Since at least 1990, designing and operating this supply chain, and adapting it to ever-changing military requirements, are highly complex and tightly coupled problems, which the highest levels of DoD recognize as weaknesses. Military supply chains face a wide range of challenges. Decisions made at the operational and tactical levels of logistics can alter the effectiveness of decisions made at the strategic level. Decisions must be made with incomplete information. As a result, practical solutions must simultaneously incorporate decisions made at all levels as well as take into account the uncertainty faced by the logistician. The design of modern military supply chains, particularly for large networks where many values are not known precisely, is recognized as too complex for many techniques found in the academic literature. Much of the literature in supply chain network design makes simplifying assumptions, such as constant per-unit transportation costs regardless of the size of the shipment, the shipping mode selected, the time available for the delivery, or the route taken. This article avoids these assumptions to provide an approach the practitioner can use when designing and adapting supply chain networks. This research proposes a simulation-based optimization approach to find a near-optimal solution to a large supply chain network design problem of the scale faced by a theater commander, while recognizing the complexity and uncertainty that the practicing military logistician must deal with.

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“…We compared our 3SBBO with traditional GA [15] and standard BBO [16]. The detailed results of every run are shown in Table 3.…”

Section: Comparison Of Bbo With Other Optimization Algorithmsmentioning

confidence: 99%

“…We believe that the order of WRE is of great significance for image feature detection. However, existing optimization algorithms can only optimize weights and biases of FNN, such as genetic algorithm (GA) [15] and biogeographybased optimization (BBO) [16]. Therefore, we proposed a novel three-segment biogeography-based optimization (3SBBO) that can not only optimize the order of Renyi entropy (RE), but also optimizes the weights and biases of FNN.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

Wang¹,

Wu²,

Zhang³

et al. 2020

IJCIS

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Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation of COVID-19 based on chest CT is mainly done manually by radiologists and takes about 5 to 15 minutes for one patient. To shorten the time of interpreting the CT image and improve the reliability of identification of COVID-19. In this paper, a novel chest CT-based method for the automatic detection of COVID-19 was proposed. Our algorithm is a hybrid method composed of (i) wavelet Renyi entropy, (ii) feedforward neural network, and (iii) a proposed three-segment biogeography-based optimization (3SBBO) algorithm. The wavelet Renyi entropy is used to extract the image features. The novel optimization method of 3SBBO can optimize weights, biases of the network, and Renyi entropy order. Finally, we used 296 chest CT images to evaluate the detection performance of our proposed method. In order to reduce randomness and get unbiased result, the 10 runs of 10-fold cross validation are introduced. Experimental outcomes show that our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, and F1.

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A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks

Cited by 41 publications

References 47 publications

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

A Simulation-based Optimization Approach to Logistic and Supply Chain Network Design

Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

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