Vanina G. Cafaro

MINLP model for the detailed scheduling of refined products pipelines with flow rate dependent pumping costs

Abstract Multiproduct pipelines transport fuels from refineries to distant distribution terminals in batches. The energy needed to move the fluids through the pipeline is mainly associated with elevation gradients and friction head loss. Commonly, friction loss is the major term requiring pump stations to keep the flow moving, and it is strongly dependent on the fluid flow rate. Some studies have been carried out for reducing the pumping costs in multiproduct pipelines, but none of them has been focused on thoroughly considering the head loss due to friction along the pipeline. This paper introduces a novel MINLP continuous-time formulation for the detailed scheduling of single-source pipelines, rigorously tracking power consumption at every pipeline segment through nonlinear equations. Real-world case studies are successfully solved using GAMS–DICOPT algorithm, which proves to be a useful tool for solving large-scale, nonlinear scheduling problems. Important reductions in the operation costs are achieved by keeping a more stable flow rate profile over the planning horizon.

Oil-derivatives pipeline logistics using discrete-event simulation

The management of oil-product pipelines represents a critical task in the daily operation of petroleum supply chains. Efficient computational tools are needed to perform this activity in a reliable and costeffective manner. This work presents a novel discrete event simulation system developed on Arena® for the detailed scheduling of a multiproduct pipeline consisting of a sequence of pipes that connect a single input station to several receiving terminals. The pipeline is modeled as a non-traditional multi-server queuing system involving a number of servers at every pipe-end that perform their tasks in a synchronized manner. Based on priority rules, the model decides which server should dispatch the entity waiting for service to the associated depot. Each priority rule can lead to a different delivery schedule, which is evaluated by using several criteria. Combined with optimization tools, the proposed simulation technique permits to easily manage real-world pipelines operations with low computational effort.

Effective Coordination of Simultaneous Delivery Flows into Receipt Terminals of Multiproduct Pipelines

Abstract This work presents a new continuous-time mixed-integer linear programming (MILP) formulation for developing the detailed schedule of single-source pipelines by allowing the execution of simultaneous deliveries to multiple receipt terminals. The model takes into account strict operational constraints restricting the flow rates at different pipeline segments and the delivery rates of products into each terminal. The problem goal is to minimize the flow restart and stoppage costs through accomplishing the least number of pumping operations. The solution to a real-world case study using the proposed model presents significant reductions in the operational cost and the CPU time with regards to previous contributions, even accounting for more realistic operating conditions.

A Continuous-Time Approach for Scheduling Bidirectional Pipeline Operations

Abstract Scheduling pumping operations in multiproduct pipelines is a complex logistic task that requires efficient supporting tools. Several approaches have been proposed for solving the pipeline scheduling problem for different pipeline configurations, but up to now the problem of sizing and sequencing oil product batches moving through bidirectional pipelines could not be tackled by continuous-time representations. This work introduces a mixed integer linear programming formulation standing for the first rigorous approach effectively solving the short-term operational planning of bidirectional pipelines.

Simulation-based framework to automated wet-etch station scheduling problems in the semiconductor industry

This work presents the development and application of an advanced modelling, simulation and optimization-based framework to the efficient operation of the Automated Wet-etch Station (AWS), a critical stage in Semiconductor Manufacturing Systems (SMS). Principal components, templates and tools available in the Arena® simulation software are used to achieve the best representation of this complex and highly-constrained manufacturing system. The major aim of this work is to provide a novel computer-aided tool to systematically improve the dynamic operation of this critical manufacturing station by quickly generating efficient schedules for the shared processing and transportation devices. This model presents a flexible structure that can be easily adapted to emulate random scenarios with uncertain processing and transfer times. A user-friendly interface for dealing with real-world applications in industry is also introduced.

A Hybrid Slot-Based/General Precedence Approach for Planning Crude Oil Supplies

Abstract This work introduces a mixed-integer nonlinear programming (MINLP) formulation based on a hybrid approach, combining the potentials of slot-based and general precedence continuous-time representations. We use general precedence sequencing variables to coordinate incoming/outgoing flows to/from every tank, and simultaneously address the scheduling of a long-distance pipeline supplying crude oil batches from harbour to refinery tanks, following a slot-based scheme. The model is able to precisely monitor key component concentrations keeping oil properties within admissible ranges. One of the most important decisions is how to manage oil shipments so as to improve logistics efficiency. Results show that the use of slot-based frameworks combined with general precedence variables for sequencing tasks in linking resources yields orders-of-magnitude savings in the computational effort to reduce costs and find optimal solutions.

New Tools for the Detailed Scheduling of Refined Products Pipelines

Abstract New tools for efficiently generating the detailed schedule of a pipeline system conveying multiple oil products from a single origin to several destinations are presented. They permit to refine the aggregate pipeline schedule provided by continuous-time optimization approaches. We propose a continuous-time mixed-integer linear programming formulation to find the detailed pipeline schedule that minimizes the total cost of shutting-down and restarting flows in pipeline segments over the planning horizon. Besides, a novel heuristic algorithm running on a discrete-event simulation framework is also developed. The performance of the proposed rigorous and heuristic approaches are compared by applying them to a real world pipeline system.