Resource management framework for manufacturing grid

Based on open grid service architecture (OGSA) and Globus Toolkit 3. 0 (GT3), a manufacturing grid (MG) is proposed to realize resource sharing and collaborative working among manufacturing enterprises. Nevertheless, resource management in MG is much more complicated than that in other grid applications due to the geographically distributed manufacturing resources, which range from CAD, CAPP and CAE to various kinds of machine tools. With the interaction of manufacturing grid information service (MGIS, developed by ourselves) and globus resource allocation manager (GRAM, provided by GT3), a resource management framework is presented to perform the functions of resource encapsulation, registry, discovery and monitoring. Furthermore, the application architecture and an example are depicted to illustrate the utilization of the resource management system.

Joint slot allocation and dynamic pricing of container sea-rail multimodal transportation

The container sea-rail multimodal transport system faces complex challenges with de- mand uncertainties for joint slot allocation and dynamic pricing. The challenge is formulated as a two-stage optimal model based on revenue management （RM） as actual slots sale of multi-node container sea-rail multimodal transport usually includes contract sale to large shippers and free sale to scattered shippers. First stage in the model utilizes an origin-destination control approach, formulated as a stochastic integer programming equation, to settle long-term slot allocation in the contract market and empty container allocation. Second stage in the model is formulated as a stochastic nonlinear programming equation to solve a multiproduct joint dynamic pricing and inventory control problem for price settling and slot allocation in each period of free market. Considering the random nature of demand, the methods of chance constrained programming and robust optimi- zation are utilized to transform stochastic models into deterministic models. A numerical experiment is presented to verify the availability of models and solving methods. Results of considering uncertain/certain demand are compared, which show that the two-stage optimal strategy integrating slot allocation with dynamic pricing considering random de- mand is revealed to increase the revenue for multimodal transport operators （MTO） while concurrently satisfying shippers＇ demand. Research resulting from this paper will contribute to the theory and practice of container sea-rail multimodal transport revenue management and provide a scientific decision-making tool for MTO.