The optimization investment policy decision of SCM-supply chain management-implementation has been analysed under symmetric and asymmetric information conditions.For both conditions,SCM implementation optional decisio...The optimization investment policy decision of SCM-supply chain management-implementation has been analysed under symmetric and asymmetric information conditions.For both conditions,SCM implementation optional decision optimizing models have been developed.In these models,both clients and vendors try to pursue their own benefits.Based upon the principal-agent theory,the models show to what extent a principal(a client)needs to pay more to an agent(a vendor)in a context of asymmetric information.For the client it is important to understand the extra costs to be able to adopt effective strategies to stimulate a vendor to perform an optimal implementation of a SCM system.The results of a simulation experiment regarding SCM implementation options illustrate and verify the theoretical findings and confirm the general notion that the less informed party is obliged to pay information rent to the better-informed party.展开更多
Multi-commodity flow problems（MCFs） can be found in many areas, such as transportation, communication, and logistics. Therefore, such problems have been studied by a multitude of researchers, and a variety of method...Multi-commodity flow problems（MCFs） can be found in many areas, such as transportation, communication, and logistics. Therefore, such problems have been studied by a multitude of researchers, and a variety of methods have been proposed for solving it. However, most researchers only discuss the properties of different models and algorithms without taking into account the impacts of actual implementation. In fact, the true performance of a method may differ greatly across various implementations. In this paper, several popular optimization solvers for implementations of column generation and Lagrangian relaxation are discussed. In order to test scalability and optimality, three groups of networks with different structures are used as case studies. Results show that column generation outperforms Lagrangian relaxation in most instances, but the latter is better suited to networks with a large number of commodities.展开更多
基金Supported by the Social Science Foundation of China(18BJL017)the Natural Science Foundation of Liaoning Science and Technology Bureau(20170540439)
文摘The optimization investment policy decision of SCM-supply chain management-implementation has been analysed under symmetric and asymmetric information conditions.For both conditions,SCM implementation optional decision optimizing models have been developed.In these models,both clients and vendors try to pursue their own benefits.Based upon the principal-agent theory,the models show to what extent a principal(a client)needs to pay more to an agent(a vendor)in a context of asymmetric information.For the client it is important to understand the extra costs to be able to adopt effective strategies to stimulate a vendor to perform an optimal implementation of a SCM system.The results of a simulation experiment regarding SCM implementation options illustrate and verify the theoretical findings and confirm the general notion that the less informed party is obliged to pay information rent to the better-informed party.
基金supported by research funds from the National Natural Science Foundation of China (Nos. 61521091, 61650110516, 61601013)
文摘Multi-commodity flow problems（MCFs） can be found in many areas, such as transportation, communication, and logistics. Therefore, such problems have been studied by a multitude of researchers, and a variety of methods have been proposed for solving it. However, most researchers only discuss the properties of different models and algorithms without taking into account the impacts of actual implementation. In fact, the true performance of a method may differ greatly across various implementations. In this paper, several popular optimization solvers for implementations of column generation and Lagrangian relaxation are discussed. In order to test scalability and optimality, three groups of networks with different structures are used as case studies. Results show that column generation outperforms Lagrangian relaxation in most instances, but the latter is better suited to networks with a large number of commodities.
