Optimisation of sequential combinatorial processes

Abstract

A method of optimising a sequential combinatorial process comprising an interchangeable sequence of events comprises using a master model to model a selection of the possible sequences, and using information derived from the master model in a surrogate model that approximates the master model with a much shorter computation time. The surrogate model calculates all the possible sequences using an algorithm to select from the information calculated by the master model that which most closely matches the events of a present sequence, following a prioritised system so that the best match is used wherever possible. All results from the surrogate model are compared so that the modelled sequence that gives the result closest to a desired optimum result for the process can be identified, and potentially applied to the process. Accuracy can be enhanced by running the optimum sequence through the master model as a check, and further by adding the optimum sequence to the information used by the surrogate model in future calculations. Any sequential combinatorial process, in which the quality of the end result of the process depends on the order in which events in the process are performed, can be optimised in this way, including manufacturing process such as machining, cutting, shaping, forming and/or heat treating a workpiece, flow of material through a factory or oil or gas through a pipeline network, chemical and material science mixing processes, computational biology modelling, and fleet management.

Description

Background technique [0001] The present invention relates to the optimization of sequential combination processes. [0002] There are many examples of procedures comprising the performance of a sequence of steps, the end result of which can be optimized by performing the steps in an optimal manner. For example, a manufacturing process may include steps in which the magnitude, amount, duration, pressure, temperature, viscosity, etc. of certain variables affect the quality of the manufactured product. Industry is certainly concerned with the achievement of quality and therefore often wishes to optimize various steps to obtain the best possible end result. [0003] This optimization is often done through process modeling. In this way, different values ​​for a parameter or group of parameters can be compared to determine which value gives the best results. One modeling technique of particular interest due to its high precision and ability to handle complex situations is finite ...

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Problems solved by technology

In other words, no alternative model optimization techniques are available in combinatorial domains where variables have no physical meaning

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