Solving Heated Oil Pipeline Problems Via Mixed Integer Nonlinear Programming Approach

TL;DR

This paper formulates the heated oil pipeline problem as a mixed integer nonlinear programming model and develops an efficient algorithm that reduces costs by optimizing heating strategies.

Contribution

It introduces a novel mixed integer nonlinear programming model for heated oil pipelines, with relaxations, preprocessing, and an improved solution algorithm for global optimality.

Findings

Algorithm reduces running costs by 6.83%

Proposed relaxations are equivalent under certain conditions

Efficient solution method outperforms practical schemes

Abstract

It is a crucial problem how to heat oil and save running cost for crude oil transport. This paper strictly formulates such a heated oil pipeline problem as a mixed integer nonlinear programming model. Nonconvex and convex continuous relaxations of the model are proposed, which are proved to be equivalent under some suitable conditions. Meanwhile, we provide a preprocessing procedure to guarantee these conditions. Therefore we are able to design a branch-and-bound algorithm for solving the mixed integer nonlinear programming model to global optimality. To make the branch-and-bound algorithm more efficient, an outer approximation method is proposed as well as the technique of warm start is used. The numerical experiments with a real heated oil pipeline problem show that our algorithm achieves a better scheme and can save 6.83% running cost compared with the practical scheme.