Abstract: Methods, apparatuses, and systems of route planning for package pickup and delivery includes: receiving predetermined locations in a geographic region and data representing a predetermined route connecting the predetermined locations; determining unit areas in the geographic region based on sequential nearness of the predetermined locations along the predetermined route, the unit areas including a first unit area and a second unit area, and the unit areas being configured such that all locations in the first unit area are to be visited before visiting locations of the second unit area; generating delivery patterns for determining a route connecting at least one of the unit areas, each delivery pattern including at least one of the unit areas associated with a visiting sequence; when receiving task data including target locations to visit, determining a target route using the delivery patterns and the target data; and sending the target route to a mobile apparatus.

Abstract: Methods, apparatuses, and systems of route planning for package pickup and delivery includes: receiving predetermined locations in a geographic region and data representing a predetermined route connecting the predetermined locations; determining unit areas in the geographic region based on sequential nearness of the predetermined locations along the predetermined route, the unit areas including a first unit area and a second unit area, and the unit areas being configured such that all locations in the first unit area are to be visited before visiting locations of the second unit area; generating delivery patterns for determining a route connecting at least one of the unit areas, each delivery pattern including at least one of the unit areas associated with a visiting sequence; when receiving task data including target locations to visit, determining a target route using the delivery patterns and the target data; and sending the target route to a mobile apparatus.

Abstract: Methods, apparatuses, and systems of route planning for package pickup and delivery includes: receiving predetermined locations in a geographic region and data representing a predetermined route connecting the predetermined locations; determining unit areas in the geographic region based on sequential nearness of the predetermined locations along the predetermined route, the unit areas including a first unit area and a second unit area, and the unit areas being configured such that all locations in the first unit area are to be visited before visiting locations of the second unit area; generating delivery patterns for determining a route connecting at least one of the unit areas, each delivery pattern including at least one of the unit areas associated with a visiting sequence; when receiving task data including target locations to visit, determining a target route using the delivery patterns and the target data; and sending the target route to a mobile apparatus.

Abstract: 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.

Abstract: A framework for generating recommendations of an object based on classification is described herein. An object is classified into a class of an ordered classification arrangement based on a set of criteria. Recommendations are generated for the object to achieve an objective of a user based on user information of the user. The recommendations are personalized to the user based on the user information.

Abstract: A system for empty container repositioning includes a scenario module, a forecast engine, a network builder module and a multi-commodity flow solver. The scenario module is configured to define a plurality of scenarios related to a safe stock level of empty containers at each of multiple locations having one or more transport vessels. The forecast engine is configured to forecast values for each of the defined scenarios, where the forecasted values include a safe stock level of empty containers for each location. The network builder module is configured to use the forecasted values to build a multi-commodity flow network. The multi-commodity flow solver is configured to determine a number of empty containers to load on and load off of the transport vessels at each of the locations.

Abstract: 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.