Collaborative Approach Based on Heuristic Algorithm and MILP Model To Assignment and Sequencing of Oil Derivative Batches in Pipeline Networks

Abstract

This paper presents a collaborative approach to the assignment and sequencing of batches in pipeline networks. The approach is based on the integration of a heuristic algorithm with a mixed integer linear programming (MILP) model. The pipeline-scheduling problem is solved using a hierarchical decomposition [Ind. Eng. Chem. Res.2015545077], but a new collaborative approach is proposed for assignment/sequencing tasks. At a first step, the proposed heuristic algorithm (assignment module) determines priorities for sending batches in order to respect deadlines. The algorithm encompasses an analysis of production and demand plans, inventories, and input and output of products in terminals, trying to use resources, namely tanks and pipelines, in an optimized form. This algorithm is used in cooperation with a proposed MILP sequencing model, which allows overcoming computation difficulties previously indicated by a traditional scheduling approach that tried to aggregate into the same monolithic MILP model assignment and sequencing decisions [Ind. Eng. Chem. Res.2012514591]. The proposed assignment/sequencing collaborative approach can be used to define operational batches with their volumes and routes in pipeline networks. Thus, the lot-sizing problem of batches in pipeline networks is addressed within the proposed paper. Tests were made in pipeline networks of different topologies. First, a small, but representative pipeline network is proposed and a data set for this network is made available for reproducibility purposes. Second, tests are made in a real-world pipeline network and results have been attained in computational times from seconds to few minutes.

Scope for optimal Scheduling of Multiproduct Pipelines with Multi-batch Sequential transportation

“…Generally, the generated contaminated products cannot be sell off as qualified products and need to be processed by blending or refinery, which brings extra operational cost for petroleum companies [17]. Most of the previous work control the development of contaminated products from two aspects, one is to optimize batch sequencing [18][19][20][21], and the other is to add processing constraints in mathematical models. For example, aiming at batch sequencing and detailed scheduling of branch pipeline networks, Mostafaei, Castro [18] presented a unified optimization model with considering different processing cost depend on the contamination type and volume.…”

Computationally Efficient MILP Model for Scheduling a Branched Multiproduct Pipeline System

“…Most previous works avoided batch contamination by either optimizing batch sequence or adding constraints to the mathematical models. Research on the former has received a great deal of attention over the past decade. − Aiming at batch sequencing and detailed scheduling of branched pipeline networks, Mostafaei et al presented a unified optimization model considering processing costs dependent on the volume and type of contamination. Forbidden batch sequences were also taken into account to prevent products with large differences in physical properties to be conveyed adjacently.…”