Optimal pricing and service level in supply chain considering misreport behavior and fairness concern

Qu, Shaojian; Shu, Lingli; Yao, Jingyuan

doi:10.1016/j.cie.2022.108759

Cited by 42 publications

(21 citation statements)

References 35 publications

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“…In this paper, accuracy (acc), precision (pre), recall (rec) and F1-score are adopted to evaluate the accuracy of model classification [56,[61][62][63]. The specific calculation formulas are as follows:…”

Section: Comparison Of Experiments Resultsmentioning

confidence: 99%

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Wang,

Zhu,

Rao

2024

Computer Modeling in Engineering &Amp; Sciences

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Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk assessment for CVD is established. Then, a two-stage model combining K-means cluster analysis and random forest (RF) is proposed to evaluate and predict the risk of CVD, and the predicted results are compared with the methods of Bayesian discrimination, K-means cluster analysis and RF. The results show that the prediction effect of the proposed two-stage model is better than that of the compared methods. Moreover, several suggestions for the government, the medical industry and the public are provided based on the research results.

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Section: Comparison Of Experiments Resultsmentioning

confidence: 99%

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Wang,

Zhu,

Rao

2024

Computer Modeling in Engineering &Amp; Sciences

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“…They pointed out that using blockchain technology and "discount" price reduction sponsorship contracts could help solve such moral risks. Information has important value for enterprises, and with the development of information technology, informatization has become the key point for the success of supply chain operations [17]. Choi [18] believes that using digital technology in the rental service process can help customers understand the product's relevant information, thereby increasing the customers' willingness to lease and help manufacturers to better achieve value creation.…”

Section: Manufacturing Rental Service Modelmentioning

confidence: 99%

A Rental Platform Service Supply Chain Network Equilibrium Model Considering Digital Detection Technology Investment and Big Data Marketing

Peng

2023

Sustainability

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Digital transformation is reshaping the decision making management of the rental service mode in the manufacturing industry, and improving digital detection technology and big data marketing have become effective ways to create value. Based on the three-level rental platform service supply chain network structure composed of manufacturers, rental platform operators and the demand market, a supply chain network equilibrium model considering the digital detection technology input and big data marketing is constructed by using variational inequality and the Nash equilibrium theory, and the optimal decision making conditions of the manufacturers and rental platform operators are derived. Combined with the Euler algorithm design procedure and numerical examples, the influences of the digital detection technology level, big data marketing cost coefficient and cost sharing ratio on the equilibrium state are analyzed. The results show that the input of digital detection technology leads to the increase in profits of each participant in the rental platform service supply chain network and promotes a more coordinated development of the supply chain. When the rental platforms implement big data marketing, the manufacturers share the cost, which can continuously improve the profits of both partners and make the cooperation more stable and efficient.

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“…Neither one is a trivial task. A decision maker may have certain preferences about the solutions to a MOO problem [49,50]. When these preferences are known ahead of the optimization process, the decision maker may employ a priori methods to focus the search on certain preferred regions.…”

Section: Simulation-based Multi-objective Optimization and Multi-crit...mentioning

confidence: 99%

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

et al. 2023

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In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

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Optimal pricing and service level in supply chain considering misreport behavior and fairness concern

Cited by 42 publications

References 35 publications

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

A Rental Platform Service Supply Chain Network Equilibrium Model Considering Digital Detection Technology Investment and Big Data Marketing

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

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