A Bayesian network model for resilience-based supplier selection

Hosseini, Seyedmohsen; Barker, Kash

doi:10.1016/j.ijpe.2016.07.007

Cited by 247 publications

(123 citation statements)

References 65 publications

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“…Product volume changes, short set-up time, conflict resolution, using flexible machines [18,64,81,82,91,[94][95][96] Table 3. The environmental supplier selection criteria.…”

Section: Criteria Explanations Related Attributes Referencesmentioning

confidence: 99%

An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation

Zhou

Zhang

2018

Sustainability

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With a great emphasis on both social and environmental responsibilities, sustainable supply chain management has gradually been adopted and promoted as an innovative business model. In sustainable supply chain practice, the choice of sustainable suppliers relates to the long-term development of a company. While the environmental performance of suppliers has been commonly considered, the social dimension has not yet received enough attention. This paper first proposes a novel criteria system for evaluating sustainable suppliers from three aspects and six dimensions, and then introduces an integrated evaluation model with a novel hybrid information aggregation. To verify the applicability and effectiveness of the proposed method, a real case of a large supermarket is introduced and analyzed. The research results show that (1) the indicator system based on Triple Bottom Line theory can serve as a framework of sustainable supplier selection for manufacturing and circulation enterprises; (2) the introduction of hybrid information aggregation can effectively handle the uncertainties of indicator scores under the realistic fuzzy environment and objectively reflect the intentions of the scorers; and (3) in comparison with the TOPSIS algorithm, the priority order finally obtained is consistent, but the proposed model shows more robustness in the sensitivity test.

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“…Product volume changes, short set-up time, conflict resolution, using flexible machines [18,64,81,82,91,[94][95][96] Table 3. The environmental supplier selection criteria.…”

Section: Criteria Explanations Related Attributes Referencesmentioning

confidence: 99%

An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation

Zhou

Zhang

2018

Sustainability

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“…However, some approaches considering several types of resilience (“Multiple” item in Table ) have been pinpointed: for instance, TOHE types (Labaka et al., ; Labaka, Hernantes, & Sarriegi, ); technical, organizational, and environmental ones (Hosseini & Barker, ; Omer, Mostashari, & Nilchiani, ). Moreover, examples of combinations of two dimensions can be found in the literature (Domain x Resource in O'Rourke (O'Rourke, )).…”

Section: Limits Linked To the Principle Of Current Approachesmentioning

confidence: 99%

Resilience of Critical Infrastructures: Review and Analysis of Current Approaches

Curt¹,

Tacnet

2018

Risk Analysis

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In crisis situations, systems, organizations, and people must react and deal with events that are inherently unpredictable before they occur: vital societal functions and thus infrastructures must be restored or adapted as quickly as possible. This capacity refers to resilience. Progress concerning its conceptualization has been made but it remains difficult to assess and apply in practice. The results of this article stem from a literature review allowing the analysis of current advances in the development of proposals to improve the management of infrastructure resilience. The article: (i) identifies different dimensions of resilience; (ii) highlights current limits of assessing and controlling resilience; and (iii) proposes several directions for future research that could go beyond the current limits of resilience management, but subject to compliance with a number of constraints. These constraints are taking into account different hazards, cascade effects, and uncertain conditions, dealing with technical, organizational, economical, and human domains, and integrating temporal and spatial aspects.

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“…Bayesian network (also known as belief network and causal network) is a probabilistic graphical model that represents a set of random variables and their conditional dependence by means of a directed acyclic graph (DAG) and CPTs [19,20]. Each node in DAG represents a variable for k = 1 to MaxIntv; Compute E(S) according to Equation (1); tempN = N; tempj = 0; for j = 2 to N; sp = 1; tempj = j Get the value of x at the position j for (updated) sample S and suppose it is T j ; |S 1 j |, |S 2 j |←the number of samples in the two intervals S 1 j and S 2 j separated by T j ; |S l j1 |, |S l j2 |, |S l j3 |, |S l j4 |←the number of samples with Rel = {1, 2, 3, 4} in S l j , l = 1, 2, respectively;…”

Section: Bayesian Networkmentioning

confidence: 99%

Section: Bayesian Networkmentioning

confidence: 99%

“…The main reason for choosing BN is that it has the clearest common sense interpretation and can be viewed as causal models of the underlying domains. It also owns the powerful capability of dealing with uncertainty and causality inference and has been widely used in predicting and classifying problems [14][15][16][17][18][19][20][21]. Figure 1 illustrates the framework of the proposed approach in which all procedures will be discussed in detail except for decision making marked with the dashed boxes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Kim

Jin

et al. 2018

Applied Sciences

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Featured Application: The research was motivated by the requirement of a printed circuit board (PCB) manufacturer and the application of the work is to identify the repeated PCB orders for batch production according to the predicted reorder level.Abstract: Identifying the printed circuit board (PCB) orders with high reorder frequency for batch production can facilitate production capacity balance and reduce cost. In this paper, the repeated orders identification problem is transformed to a reorder level prediction problem. A prediction model based on a modified Bayesian network (BN) with Monte Carlo simulations is presented to identify related variables and evaluate their effects on the reorder level. From the historically accumulated data, different characteristic variables are extracted and specified for the model. Normalization and principal component analysis (PCA) are employed to reduce differences and the redundancy of the datasets, respectively. Entropy minimization based binning is presented to discretize model variables and, therefore, reduce input type and capture better prediction performance. Subsequently, conditional mutual information and link strength percentage are combined for the establishment of BN structure to avoid the defect of tree augmented naïve BN that easily misses strong links between nodes and generates redundant weak links. Monte Carlo simulation is conducted to weaken the influence of uncertainty factors. The model's performance is compared to three advanced approaches by using the data from a PCB manufacturer and results demonstrate that the proposed method has high prediction accuracy.

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A Bayesian network model for resilience-based supplier selection

Cited by 247 publications

References 65 publications

An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation

An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation

Resilience of Critical Infrastructures: Review and Analysis of Current Approaches

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

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