Abstract: 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.

Abstract: 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.

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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system includes an option selection engine selects an initial subset of pre-selected products from multiple products for display to a user, where the products include multiple filtering options and multiple selection criteria. An elicitation engine prompts the user to provide input including input for the filtering options and input for the selection criteria and receives the filtering options input and the selection criteria input. A ranking and scoring engine receives the filtering options input and the selection criteria input and selects one method of multiple methods to calculate a score for the products and to rank the products using the score based on the filtering options input and the selection criteria input from the user. An option selection engine selects an updated subset of products from the plurality of products for display to the user based on the rank of the of the products using the score.

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 computer-implemented method for a booking based demand forecast problem includes converting time series data into a multivariate time series, training a multivariate time series model using the converted multivariate time series, forecasting results using the multivariate time series model and aggregating the results.

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 computer-implemented method includes receiving, at a rental company, a customer's order to hire a transport equipment unit of a first type beginning on a start date. The rental company has an inventory of transport equipment units for hire and a network of diverse storage sites. The computer-implemented method further includes locating one or more transport equipment units of the first type in the rental company's inventory of transport equipment available for hire by the start date, computing the rental company's costs associated with picking up each of the located transport equipment units of the first type for delivery to the customer, and selecting, based on consideration of the rental company's picking up costs, one of the located transport equipment units for delivery to customer.

Abstract: To cluster objects associated with a dataset, a selection of criteria is received. For the received criteria, preference information is received to perform a preference-based clustering of the objects. Based on the preference information, a uni-criterion preference degree corresponding to each of the selected criterion is computed. The uni-criterion preference degrees of all the selected criteria are aggregated to compute a universal preference degree. Based on a preference-type and the computed preference degree, a relationship matrix is generated. The matrix representing similarity measure between the objects is generated. The objects are clustered according to the relationship matrix. A visualization of the clustered objects is rendered on an associated user interface.

Abstract: 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.