Optimizing a Pipeline Operation by Constraint Logic Programming (CLP) and Mixed Integer Linear Programming (MILP)

Abstract

This paper addresses the problem of developing an optimization structure to aid the operational decision-making in a real-world pipeline scenario. The pipeline connects an inland refinery to a harbor, conveying different types of products (gasoline, diesel, kerosene, alcohol, liquefied petroleum gas, jet fuel, etc). The scheduling of activities has to be specified in advance by a specialist, who must provide low cost operational procedures. The specialist has to take into account issues concerning product availability, tankage constraints, pumping sequencing, flow rate determination, and a series of operational requirements. Thus, the decision-making process is hard and error-prone due to the diversity of aspects to be considered. Nevertheless, the developed optimization structure can aid the specialist in solving the pipeline scheduling task with improved efficiency. Such optimization structure has its core in a novel mathematical approach, which uses Constraint Logic Programming (CLP) and Mixed Integer Linear Programming (MILP) technologies in an integrated CLP-MILP model. In particular, the integration of CLP and MILP technologies has been recognized as an emerging discipline for achieving the best that CLP and MILP can contribute to solve scheduling problems [1]. The scheme used for integrating CLP and MILP is double modeling [1], and the combined CLP-MILP model is implemented and solved by using a commercial tool [2]. Illustrative instances demonstrate that the optimization structure is able to define new operational points to the pipeline system, providing significant cost saving.