Abstract: A request is received to book a flight on a private aircraft. A disruption cost associated with adding the flight to a previously optimized schedule of previously booked flights is determined. A price to be quoted to book the requested flight is computed based at least in part on the disruption cost.

Abstract: A reliability data is stored for each of at least a first subset of a plurality of aircraft comprising a fleet of aircraft. A plurality of feasible solutions to fulfill transportation requirements of a plurality of groups of one or more passengers using aircraft comprising the fleet is determined, each group having a requirement to travel starting location of that group to a destination location of that group. Based at least in part on the plurality of feasible solutions and the reliability data, a plurality of scenarios are generated, each of which would result in a delay or cancellation affecting at least a second subset of the plurality of feasible solutions. With respect to each feasible solution and for each scenario an additional cost component is computed for that feasible solution associated with occurrence of that scenario. A solution is selected from among the plurality of feasible solutions based at least in part on the additional cost component associated with occurrence of a respective scenario.

Abstract: A request is received to book a flight on a private aircraft. A disruption cost associated with adding the flight to a previously optimized schedule of previously booked flights is determined. A price to be quoted to book the requested flight is computed based at least in part on the disruption cost.

Abstract: Techniques are disclosed for increasing the robustness of optimization solutions for bulk gas distribution, relative to uncertain events such as unexpected plant outages. The techniques actively consider different assumptions regarding unexpected plant outages while searching for a solution (i.e., when generating a delivery schedule). A robust solution (delivery schedule) is one on which a plant outage only has a limited impact. A computer-implemented delivery scheduling application may identify a robust solution that performs well with respect to a range of uncertain future conditions (e.g., a variety of plant outages), at the lowest additional cost to the producer/distributor relative to other solutions.

Abstract: Techniques are disclosed for designing a supply chain distribution network. A network design tool may be configured to facilitates strategic decision making for a producer/distributor of a commodity products (e.g., molecular gas mixtures stored in cylinders) by incorporating transportation costs (i.e., operational decisions) into the strategic decision making process. Further, the network design tool may also integrate investment decision costs, which are usually considered ‘tactical decisions’, into the strategic decision process (e.g., which filling tools are assigned to which plant/hub location). The network design tool implements different algorithm approaches allowing a user to obtain optimal, near-optimal, and approximate solutions.

Abstract: Techniques are disclosed for designing a supply chain distribution network. A network design tool may be configured to facilitates strategic decision making for a producer/distributor of a commodity products (e.g., molecular gas mixtures stored in cylinders) by incorporating transportation costs (i.e., operational decisions) into the strategic decision making process. Further, the network design tool may also integrate investment decision costs, which are usually considered ‘tactical decisions’, into the strategic decision process (e.g., which filling tools are assigned to which plant/hub location). The network design tool implements different algorithm approaches allowing a user to obtain optimal, near-optimal, and approximate solutions.

Abstract: Techniques are disclosed for increasing the robustness of optimization solutions for bulk gas distribution, relative to uncertain events such as unexpected plant outages. The techniques actively consider different assumptions regarding unexpected plant outages while searching for a solution (i.e., when generating a delivery schedule). A robust solution (delivery schedule) is one on which a plant outage only has a limited impact. A computer-implemented delivery scheduling application may identify a robust solution that performs well with respect to a range of uncertain future conditions (e.g., a variety of plant outages), at the lowest additional cost to the producer/distributor relative to other solutions.