This
study addresses the operation-scheduling problem of a transfer
hub with multiple tank farms (M-TH) connected to a multiproduct pipeline
network. As part of the pipeline network, the THs serve as links that
receive, store, and deliver oil products. Previous scheduling optimization
works on pipeline networks with oil products largely focused on the
batch plans of each pipeline, without considering the operation schedule
of the THs. In this study, the main task is to optimize the loading
and unloading scheduling operations of the tanks in the M-TH, which
are subjected to given schedules of upstream/downstream pipelines.
The schedules of pipelines that carry different types of products
can be decomposed into several independent single product scenarios.
A mixed-integer linear programming (MILP) formulation is presented.
The proposed formulation takes into account the capacities of the
tanks, operational rules of the tanks and tank farms, structural constraints,
and settling time after the loading operation, while minimizing the
switch operations between the tanks and switchovers between the tank
farms. Moreover, the MILP formulation can be solved for one separate
products at one time as there is no interference among the single-product
scheduling problems within the transfer hub. The formulation is based
on a new continuous time representation with static and dynamic time
slots. The scheduling horizon is partitioned into several static time
slots on the basis of the time points of batches arriving at and leaving
from the M-TH or the flow rate changed. Each static time slot is divided
into several dynamic time slots, whose duration and starting/ending
points are determined by the optimization process. The proposed formulation
is validated using the optimal results of a real-world case. A time
horizon partitioning strategy is employed to deal with the long-term
horizon scenario. The results help verify that we can substantially
save computational time and obtain a satisfactory solution; however,
the optimality is compromised.