posted on 2015-05-13, 00:00authored byS. N.
B. Magatão, L. Magatão, F. Neves-Jr, L. V.
R. Arruda
This
work presents an approach for scheduling of operational activities
in a large real-world pipeline network, where oil derivatives and
ethanol are transported and distributed among refineries, terminals,
depots, and final clients. The hierarchical decomposition approaches
to solve the pipeline-scheduling problem presented by Boschetto et
al. [Ind. Eng. Chem. Res. 2010, 49, 5661] and Magatão et al. [Ind. Eng. Chem. Res. 2012, 51, 4591], which are based on the integration
of mixed integer linear programming (MILP) models and a set of heuristic
modules, are merged and compounding blocks are also improved. Thus,
a novel decomposition approach for scheduling product distribution
through a pipeline network is proposed. In addition, this work presents
a new MILP approach for the last hierarchical level: the timing block
(timing model). This paper expands and improves the former MILP model,
which was the timing block core. A series of operational constraints
were considered within a continuous time representation in order to
determine the exact time instants that products should be pumped into
the pipelines and received in the operational areas during a scheduling
horizon of, typically, 1 month. Within the new MILP timing model,
turn shift constraints, local constraints, and surge tank constraints
are improved; immediate pumping constraints are proposed. In addition,
a decomposition approach for the new MILP model is also proposed within
this article. This decomposition is based on a relax-and-fix heuristic
implemented by a sequential run of two MILP models: MLC (Model with
Local Constraints) and MST (Model with Seasonal costs and Turn shift
constraints). The MILP decomposition goal is to reduce the computational
load, if seasonal costs and turn shift constraints are active, without
quality solution losses. The proposed approach is applied to the solution
of real case studies of a pipeline network that includes 30 bidirectional
multiproduct pipelines associated with 14 nodes (four refineries,
two harbors, six depots, and two final clients). Computational results
have been attained in a reasonable computational time (from seconds
to a few minutes) for the addressed pipeline network.