Sap Se

Philippe Nemery

Xin Li

Raymond Yao

Wen-Syan Li

Honggang Wang

Mengjiao Wang

Xingtian Shi

Yan Tan

Yuanyuan Cen

Qinhe Lin

Yingbin Zheng

Harald Hengelbrock

Hanmei Zhang

Irene Chen

Terry Wang

Mark Finch

An input corresponding to a time window is received in an integrated container management system. For containers of a specific container type, a network flow solver implementing a multi-commodity network flow problem is executed based on the input. A graph is generated with nodes representing depot locations, mode of transport locations and customer locations. Arcs connecting nodes, and representing operations and costs associated with the nodes are generated. Pick-up arcs are generated between the depot locations and customer locations. Return arcs are generated between the customer locations and the depot locations. Street turn arcs are generated among customer locations. Unload load arcs are generated between the mode of transport locations and the depot locations. Output tables are generated with values representing functionalities associated with container repositioning, container pick-up and return, and container street turn. Container repositioning, container pick-up and return, and container street turn are optimized based on the values in the output table.

Integrated container management system

Various embodiments of systems and methods for optimizing vehicle utilization are described herein. The method includes determining demand, supply, and availability of vehicles. A graph with nodes (representing source and destination stations of orders) and arcs connecting nodes is generated based on validity conditions, e.g., there is network connectivity from source to destination node, etc. The arc is assigned for repositioning, waiting, and loaded move based upon input such as tolerable delay for orders, waiting cost at stations, repositioning cost, long stand cost, revenue generated from orders, order priority, etc. A network flow solver is successively executed to solve the graph to maximize profit, optimized utilization of vehicles, fulfill order on time, and balance demand and supply. Output tables including key performance indicators (KPIs) and information of fulfilled and unfulfilled orders are generated. Vehicle allocation, repositioning, waiting, and loaded move may in turn are optimized based on the output tables.

Integrated system for optimizing vehicle utilization

A rule handler may receive a first routing rule and a second routing rule, each routing rule characterizing at least one potential physical transportation route. A rule converter may convert the first routing rule and the second routing rule into a first finite automaton (FA) and a second finite automaton, respectively, and may combine the first finite automaton and the second finite automaton into a finite automaton network graph. A graph comparator may then compare the finite automaton network graph to existing physical transportation routes, and a route selector may select, based on the comparing, at least one feasible physical transportation route from the existing physical transportation routes, the at least one feasible physical transportation route being an instance of the at least one potential physical transportation route.

Automaton-based framework for asset network routing

A data flow model format allows for improved data flow model creation and execution. The data flow models can provide a user with greater flexibility in selecting an appropriate data flow model, including providing information about the data flow model and selecting from multiple available data flow models. Tabular or graphical visualizations can aid the user in understanding a data flow model and retrieving desired information. Technical features such as designing a data flow with a single data output, and maintaining node identifiers in node metadata, can reduce coupling between software components and improve security and performance.

Data flow modeling and execution

The embodiments provide a system for decision-making criteria-based recommendations. The system may include a decision engine configured to receive a request for recommendations for an option problem associated with a product or service category, and determine options among a plurality of options for the product or service category based on preference information. The preference information may include activated decision-making criteria and corresponding weight values. The corresponding weight values may represent a relative importance of each activated decision-making criterion. The decision engine may be configured to determine options among the plurality of options including calculating scores for the plurality of options based on, in part, the activated decision-making criteria and the corresponding weight values and selecting the options among the plurality of options based on the calculated scores. The decision engine may be configured to provide a display of the determined options as the recommendations for the product or service category.

Decision making criteria-driven recommendations

A framework for clustering is described herein. In accordance with one aspect, a data set having x number of objects, and preference information of a user is provided to a clustering tool. The clustering tool may calculate preference degrees between objects in the data set. In addition, similarity measures of objects in the data set may be calculated. Clusters of objects may then be generated from the data set.

Growing community of inventors

Shanghai, China

Philippe Nemery