Method of generating an optimized ship schedule to deliver liquefied natural gas

Patent metadata

Field	Value
Publication number	US-10867261-B2
Application number	US-201514688772-A
Country	US
Kind code	B2
Filing date	Apr 16, 2015
Priority date	May 7, 2014
Publication date	Dec 15, 2020
Grant date	Dec 15, 2020

Abstract

A system and method is provided for generating an optimized ship schedule to deliver liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships. The method involves modeling the ship schedule via an LNG ship scheduling model and a LNG ship rescheduling model to provide optimized decisions for the LNG supply chain. The LNG supply chain includes the one or more LNG liquefaction terminals, the one or more LNG regasification terminals, and the fleet of ships.

First claim

What is claimed is:

1. A method for delivering liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships, comprising:
(A) obtaining a baseline ship schedule for LNG shipping operations, wherein the baseline schedule comprises a list of planned cargos that comprises a customer for each cargo, a destination for each cargo, a delivery ship for each cargo, a delivery time for each cargo, and a delivery quantity for each cargo;
(B) obtaining input data associated with the LNG shipping operations, wherein the input data comprises one or more of production data from one or more LNG liquefaction terminals, facility management data from one or more LNG regasification terminals, customer terminal data, contract data, and shipping data;
(C) obtaining one or more preferences associated with delivery of LNG, wherein the preferences comprise one or more of an amount of time by which a delivery time for a cargo can be delayed or advanced, an amount by which the delivery quantity for a cargo may be changed, an indication of whether a planned cargo needs to be delivered to a planned customer or a planned destination, and in indication of whether additional ships could be in-chartered or out-chartered;
(D) determining whether the obtained input data indicates a disruption to the baseline ship schedule and determining a disruption type associated with the disruption;
(E) developing an updated ship schedule when the input data indicates that there is a disruption to the baseline ship schedule, wherein the updated ship schedule provides a list of planned cargoes that comprise an updated customer for each cargo, an updated destination for each cargo, an updated delivery ship for each cargo, an updated delivery time for each cargo, and an updated delivery quantity for each cargo, and wherein developing the updated ship schedule comprises:
(i) providing an optimization model associated with LNG shipping operations;
(ii) translating the obtained input data, one or more preferences, and disruption type into objectives and constraints to recover scheduling based on the disruption type; and
(iii) developing an updated ship schedule using the objectives and constraints with one or more algorithms and the optimization model, wherein deviations between the updated ship schedule and the baseline shin schedule are minimized, and wherein the developing comprises:
(a) constructing a solution based on the cargo specified in the baseline schedule and cargo affected by the disruption; and
(b) updating the solution by applying a series of local neighborhood searches;

(F) displaying the updated ship schedule to a user;
(G) displaying one or more notifications to the user, where the notifications provide visual indications of changes between the baseline ship schedule and the updated ship schedule; and
(H) shipping LNG from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships based on the updated ship schedule.

2. The method of claim 1 , wherein the series of local neighborhood searches comprises one or more of a k-day flexibility search, rolling time window search, one-ship search, and two-ship search.

3. The method of claim 2 , wherein the rolling time window search comprises:
detecting changes one by one starting from the beginning of a 90-day time period;
creating a time window around a change, wherein within the time window variables are re-evaluated and wherein variables outside of the time window are fixed to their current value.

Assignees

• Shao Yufen
• Goel Vikas
• Lowry Joshua R
• Tarhan Bora
• Furman Kevin C
• Exxonmobil Upstream Res Co

Inventors

• Shao Yufen
• Goel Vikas
• Lowry Joshua R
• Tarhan Bora
• Furman Kevin C

Classifications

• G06Q10/0631 Primary

  Resource planning, allocation, distributing or scheduling for enterprises or organisations · CPC title

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06Q50/06

  Energy or water supply · CPC title

• G06Q10/0633

  Workflow analysis · CPC title

• G06Q10/0832

  Special goods or special handling procedures, e.g. handling of hazardous or fragile goods · CPC title