Taking an environment-friendly green closed-loop supply chain as the research object, this work established a two-stage closed-loop supply chain game model. Considering the influence of the environmental protection input on the whole supply chain, there are different decisions among the participants in the supply chain, and the different choices will have impacts on the benefits of the whole supply chain when manufacturers select a closed-loop supply chain model of third-party recycling. Hence, this work compared and analyzed the impact of centralized decision-making and decentralized decision-making on the returns and pricing strategies of each participant. Finally, an optimized cooperative mechanism decision model considering a cost profit sharing contract was further designed. The model is conducive to obtaining the maximum profit value in centralized decision-making and avoids the negative impact of a “double marginal effect” on supply chain income in decentralized decision-making, and finally, improves the overall coordination and profit of a green closed-loop supply chain. The numerical examples are conducted to verify the effectiveness and practicality of the proposed models. This work provides a helpful decision support and guidance for enterprises and the government on the used products recycling decisions to better manage the green closed-loop supply chain.

https://www.mdpi.com/2071-1050/11/19/5335/pdf

A Green Closed-Loop Supply Chain Coordination Mechanism Based on Third-Party Recycling

Whether the upstream and downstream members in a supply chain (considering environmental objectives) simultaneously stabilize economic benefits has become an important problem in the process of green development. However, few quantitative studies on green supply chains have considered environmental and economic benefits to realize multi-objective optimization. To study operation and cooperation strategies with a consideration of the different objective on the level of supply chain, we first establish a green supply chain game model with profit and environment objectives simultaneously considered by the manufacturer. Then, we analyze the multi-objective decisions of the supply chain members under centralized control using a manufacturer-led Stackelberg game and revenue-sharing contract. Using the manufacturer’s environmental preference as a variable, the effects of environmental benefits on the supply chain are also investigated. Finally, this study determines that the manufacturer’s profit will be reduced after considering the objective of environmental benefits, while the retailer’s profit, product greenness, and environmental benefits will be improved. Meanwhile, the total profit of the green supply chain will first increase and then decrease. In particular, a revenue-sharing contract can facilitate the coordination of multiple objectives; in this way, both the manufacturer and the retailer achieve higher profits and environmental benefits compared to a decentralized control condition, which is of great significance in achieving a win–win situation for the economy and the environment.

A Multi-Objective Optimization Model for Green Supply Chain Considering Environmental Benefits

Due to the increasing complexity in socioeconomic environments and the fuzziness in human cognition, the cognitive information over alternatives provided by a decision organization consisting of several experts is usually uncertain and hesitant. Consequently, in order to solve the fuzziness and uncertainty of preference information in the matching process of complex product manufacturing tasks on the cloud manufacturing platform, a novel two-sided matching model based on dual hesitant fuzzy preference information is proposed. Firstly, the two-sided matching problem and the dual hesitant fuzzy set (DHFS) are described. Then, the dual hesitant fuzzy preference information evaluation matrix is constructed and normalized according to the preference information provided by agents on both sides. Sequentially, the dual hesitant fuzzy preference information evaluation matrix is transformed into the satisfaction degree matrix by the projection technology. Simultaneously, considering the stable matching constraint, a multi-objective two-sided matching optimization model which maximizes the satisfaction degree and minimizes the difference degree of two-sided agents is established. Further, the matching relative competition degree is used to convert the multi-objective optimization model into a single-objective optimization model, and the matching algorithm is designed for solving the model. Moreover, an illustrative example is employed to demonstrate the practicality and feasibility of the developed model. Subsequently, the sensitivity analysis is performed to validate the stability of the proposed matching results, and the comparative analysis is carried out to illustrate the reliability of the proposed matching algorithm and the merits of the developed model. It reveals that the proposed model can not only obtain stable matching results but also give more choices in matching methods.

Two-sided matching model for complex product manufacturing tasks based on dual hesitant fuzzy preference information

The vulnerability of multinational corporation (MNC) knowledge network is one of the major causes for the failure and even the death of MNCs in the fierce global market competition. Employee turnover and knowledge loss are the triggers for the MNC knowledge network vulnerability and a matter of serious concern in the evolution and development of MNC knowledge network. The purpose of this work is to propose a valid and quantitative measurement method to investigate the influence of employee loss and knowledge loss on the vulnerability of MNC knowledge network.,MNC knowledge network is inherently a heterogeneous network where there are mainly two types of units: employees and their knowledge. Therefore, this paper establishes a weighted super-network model for MNC knowledge network to depict its heterogeneous composition. On the basis of the weighted MNC knowledge super-network, the static and dynamic vulnerability measurement methods are further proposed to investigate and evaluate MNC knowledge network vulnerability.,A real case is given to illustrate the applicability of the proposed weighted MNC knowledge super-network model and the network vulnerability measurement methods. The results show the super-network model proposed in this paper can effectively embody the complex features of MNC knowledge network, and the vulnerability measurement methods can effectively investigate the influence of employee loss and knowledge loss on network vulnerability.,From the perspective of super-network, researchers and practitioners can get a more systematic and deeper understanding of the MNC knowledge network and its human and knowledge resource constitute which are vital for the evolution and development of MNC. Moreover, the MNC knowledge network vulnerability measurement methods can effectively measure and analyze the influence of resource loss on network vulnerability, which can provide a helpful decision support for monitoring and managing of MNC knowledge network vulnerability to reduce its adverse effects.

Vulnerability of multinational corporation knowledge network facing resource loss: A super-network perspective

Owing to the complexity of socio-economic environments and the fuzziness of human cognition, information of cognitive preference provided by decision-making organizations composed of many experts is often hesitant and fuzzy. In consequence, for the sake of addressing the hesitance and fuzziness of preference information for the configuration of tasks and resources in cloud manufacturing, a decision-making model of a two-sided matching considering a bidirectional projection under preference information of hesitant fuzzy is put forward. Primarily, this paper describes the problem of two-sided matching and introduces the hesitant fuzzy set. Afterwards, according to preference information given by matching agents using the hesitant fuzzy element, the evaluation matrix is constructed. Meanwhile, the bidirectional projection technology and TOPSIS method are combined to calculate the closeness degree matrix. Further, by introducing the constraint of the stable matching, a decision-making model of a two-sided matching for maximizing the closeness degree of two-sided matching agents is constructed, and the optimal configuration results are obtained by the solving of the model. Subsequently, the illustrative case is provided to validate the rationality and effectiveness of the presented model in solving the configuration for cloud manufacturing tasks and resources. Also, the stability in the proposed configuration results is illustrated by making a sensitivity analysis. Further, the reliability in the given solving process is demonstrated through performing a comparative analysis, as well as the advantages in the proposed model is also discussed. It shows that this developed model can give stable configuration results and also provide a matching approach for different agents under an uncertain environment.

Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources

With the development of specialization, coordination and intelligence in the manufacturing service process, the issue of how to quickly extract potential resources or capabilities for distributed manufacturing service requirements, and how to carry out resource matching for manufacturing service requirements with correlated mapping characteristics, have become the critical issues to be addressed in the cloud manufacturing environment. Through the combination of the characteristics of relevance, synergy and diversity of manufacturing service tasks on the intelligent cloud platform, a matching decision method for manufacturing service resources is proposed in this paper based on multidimensional information fusion. On the basis of integrating multidimensional information data in cloud manufacturing resource, the information entropy and rough set theory are applied to classify the importance of manufacturing service tasks, while the matching capability are analyzed by using a hybrid collaborative filtering (HCF) algorithm. Then, the information of function attribute, reliability and preference is employed to match and push manufacturing service resources or capabilities actively, so as to realize the matching decision of manufacturing service resources with precise quality, stable service and maximum efficiency. At last, a case study of resources matching decision for body & chassis manufacturing service in a new energy automobile enterprise is presented, in which the experimental results show that the proposed approach is more accuracy and effective compared with other different recommendation algorithms.

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A Multidimensional Information Fusion-Based Matching Decision Method for Manufacturing Service Resource

For enterprise, how to quickly realize the selection of green innovative design projects has become a key issue for improving innovation performance. Based on an analysis of enterprise product innovation and customer green preferences, an indicator set for innovation performance in enterprise was established. Considering the fuzziness of the correlation between indicators for innovation performance in enterprise and consumer’s green preferences, a fuzzy clustering method was used to identify the internal relations among the indicators for innovation performance with green preferences of customers. Then a wavelet neural network was used to select the innovation design project for various green preferences of customers. Finally, a case study was proposed to verify the feasibility and effectiveness of the method. This work can help the enterprise to develop green design, products, and serve uniformly, which can effectively shorten green product development cycles, reduce cost, and improve enterprise innovation performance greatly.

https://www.mdpi.com/2071-1050/11/5/1375/pdf

Selection of Manufacturing Enterprise Innovation Design Project Based on Consumer’s Green Preferences

Crowdsourcing design for complex products has open objectives and requires collaboration among different fields. It is difficult to use a single approach to describe the process in which the number and interaction of tasks and steps of tasks are changed dynamically depending on the execution state. Motivated by modeling the collaborative business process of crowdsourcing design in complex products, a crowdsourcing hierarchical process modeling approach based on an object-oriented Petri net is proposed. From the perspective of organization and task, the crowdsourcing object interaction and design process are modeled separately. First, an organization layer modeling approach based on expert review is proposed to describe the information transfer relationship among crowdsourcing objects from the organization perspective. Then, the task layer modeling approach based on the component decomposition idea is proposed. From the perspective of the task, the task is decomposed by using a design structure matrix (DSM) and a product structure tree to model the interaction of the design process. The object-oriented Petri net-is used to realize the representation of the model. Finally, the method is verified by taking the model of the crowdsourcing design of an air cooler as an example. The model subnet is analyzed by means of an overlay tree and incidence matrix, and the reliability and robustness of the model are proven. This method provides a reference for the process modeling of the crowdsourcing design of complex products.

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A Novel Modeling Method of the Crowdsourcing Design Process for Complex Products-Based an Object-Oriented Petri Net

Enterprise production is often interfered with by internal and external factors, resulting in the infeasible original production scheduling scheme. In terms of this issue, it is necessary to quickly decide the optimal production scheduling scheme after these disturbances so that the enterprise is produced efficiently. Therefore, this paper proposes a new rescheduling decision model based on the whale optimization algorithm and support vector machine (WOA-SVM). Firstly, the disturbance in the production process is simulated, and the dimensionality of the data from the simulation is reduced to train the machine learning model. Then, this trained model is combined with the rescheduling schedule to deal with the disturbance in the actual production. The experimental results show that the support vector machine (SVM) performs well in solving classification and decision problems. Moreover, the WOA-SVM can solve problems more quickly and accurately compared to the traditional SVM. The WOA-SVM can predict the flexible job shop rescheduling mode with an accuracy of 89.79%. It has higher stability compared to other machine learning methods. This method can respond to the disturbance in production in time and satisfy the needs of modern enterprises for intelligent production.

/pdf/a-new-decision-method-of-flexible-job-shop-rescheduling-204z5c9j.pdf

Lijun Song

Papers

A Multidimensional Information Fusion-Based Matching Decision Method for Manufacturing Service Resource

Selection of Manufacturing Enterprise Innovation Design Project Based on Consumer’s Green Preferences

A Novel Modeling Method of the Crowdsourcing Design Process for Complex Products-Based an Object-Oriented Petri Net

A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM