Ricardo Lüders

Simulating the operational scheduling of a realworld pipeline network

Abstract This paper addresses the problem of developing a simulation model to aid the operational decision-making of scheduling activities in a real-world pipeline network. Basically, the simulation model should represent three different behaviors: production, transport and demand of oil derivatives. Batches are pumped from (or pass through) many different areas and flow through pipes which are the shared resources at the network. It is considered that different products can flow through the same pipe and each oil derivative has its proper tankfarm at refineries, terminals or harbor. The simulator makes use of an optimal scheduling sequence of batches that balance demand requirements to the production planning, considering inventory management issues and pipeline pumping procedures. The simulation model represents a real-world pipeline network designed to aid typical activities of an operator such as inventory management at different and batch performance analysis by visualization tank levels and pipe utilization rate.

An integrated framework for operational scheduling of a real-world pipeline network

Abstract This paper addresses the problem of developing an optimisation structure to aid the operational decision-making of scheduling activities in a real-world pipeline network. During the scheduling horizon, many batches are pumped from (or passing through) different nodes (refineries, harbours or distribution terminals). Pipelines are shared resources operated and monitored 365 days a year, 24 hours per day. Scheduling details must be given, including pumping sequence in each node, volume of batches, tankage constraints, timing issues, while respecting a series of operational constraints. The balance between demand requirements and production campaigns, while satisfying inventory management issues and pipeline pumping procedures, is a difficult task. Based on key elements of scheduling, a decomposition approach is proposed using an implementation suitable for model increase. Operational insights have been derived from the obtained solutions, which are given in a reduced computational time for oil industrial-size scenarios.

PSO with path relinking for resource allocation using simulation optimization

This paper proposes a PSO-based optimization approach with a particular path relinking technique for moving particles. PSO is evaluated for two combinatorial problems. One under uncertainty, which represents a new application of PSO with path relinking in a stochastic scenario. PSO is considered first in a deterministic scenario for solving the Task Assignment Problem (TAP) and hereafter for a resource allocation problem in a petroleum terminal. This is considered for evaluating PSO in a problem subject to uncertainty whose performance can only be evaluated by simulation. In this case, a discrete event simulation is built for modeling a real-world facility whose typical operations of receiving and transferring oil from tankers to a refinery are made through intermediary storage tanks. The simulation incorporates uncertain data and operational details for optimization that are not considered in other mathematical optimization models. Experiments have been carried out considering issues that affect the choice of parameters for both optimization and simulation. The results show advantages of the proposed approach when compared with Genetic Algorithm and OptQuest (a commercial optimization package).

HSEDA: a heuristic selection approach based on estimation of distribution algorithm for the travelling thief problem

Hyper-heuristics are high-level search techniques which improve the performance of heuristics operating at a higher heuristic level. Usually, these techniques automatically generate or select new simpler components based on the feedback received during the search. Estimation of Distribution Algorithms (EDAs) have been applied as hyper-heuristics, using a probabilistic distribution model to extract and represent interactions between heuristics and its low-level components to provide high-valued problem solutions. In this paper, we consider an EDA-based hyper-heuristic framework which encompasses a Heuristic Selection approach aiming to find best combinations of different known heuristics. A surrogate assisted model evaluates the new heuristic combinations sampled by the EDA probabilistic model using an approximation function. We compare our proposed approach named Heuristic Selection based on Estimation of Distribution Algorithm (HSEDA) with three state-of-the-art algorithms for the Travelling Thief Problem (TTP). The experimental results show that the approach is competitive, outperforming the other algorithms on most of the medium-sized TTP instances considered in this paper.

Sequencing Batches in a Real-World Pipeline Network Using Constraint Programming

Abstract This paper presents a new approach for sequencing of batches in a complex pipeline network by using Constraint Programming (CP) techniques. These batches are composed by oil derivatives to be transferred by the pipeline network. Although many papers found in the literature usually deal with reduced pipeline topologies, the network considered in this work is quite complex. It involves nine areas, including three refineries, one harbor and five distribution centers interconnected by fifteen multiproduct pipelines. Although this paper only focus on sequencing of batches, the final task is to provide a complete transfer scheduling for the pipeline network. This scheduling is accomplished by means of three key elements: assignment, sequencing and timing. The proposed CP model considered for sequencing of batches introduces particular features of the problem that can be used by specialized solvers. This approach aims to consider time delays introduced by the pipeline network during product transfers. Preliminary results show that a suitable ordering of batches can be obtained for improving inventory levels. Besides, results show a better performance when compared to a heuristic approach which can fail for some particular cases.

A hyperheuristic approach based on low-level heuristics for the travelling thief problem

In this paper, we investigate the use of hyper-heuristics for the travelling thief problem (TTP). TTP is a multi-component problem, which means it has a composite structure. The problem is a combination between the travelling salesman problem and the knapsack problem. Many heuristics were proposed to deal with the two components of the problem separately. In this work, we investigate the use of automatic online heuristic selection in order to find the best combination of the different known heuristics. In order to achieve this, we propose a genetic programming based hyper-heuristic called GPHS*, and compare it to state-of-the-art algorithms. The experimental results show that the approach is competitive with those algorithms on small and mid-sized TTP instances.

Combining K-means method and complex network analysis to evaluate city mobility

Complex networks have been used to model public transportation systems (PTS) considering the relationship between bus lines and bus stops. Previous works focused on statistically characterize either the whole network or their individual bus stops and lines. The present work focused on statistically characterize different regions of a city (Curitiba, Brazil) assuming that a passenger could easily access different unconnected bus stops in a geographic area. K-means algorithm was used to partition the bus stops in (K =) 2 to 40 clusters with similar geographic area. Results showed strong inverse relationship (p < 2 × 10−16 and R2 = 0.74 for K = 40 in a log model) between the degree and the average path length of clustered bus stops. Regarding Curitiba, it revealed well and badly served regions (downtown area, and few suburbs in Southern and Western Curitiba, respectively). Some of these well served regions showed quantitative indication of potential bus congestion. By varying K, city planners could obtained zoomed view of the behavior of their PTS in terms of complex networks metrics.

HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm

Probabilistic modeling of selected solutions and incorporation of local search methods are approaches that can notably improve the results of multi-objective evolutionary algorithms (MOEAs). In the past, these approaches have been jointly applied to multi-objective problems (MOPs) with excellent results. In this paper, we introduce for the first time a joint probabilistic modeling of (1) local search methods with (2) decision variables and (3) the objectives in a framework named HMOBEDA. The proposed approach is compared with six evolutionary methods (including a modified version of NSGA-III, adapted to solve combinatorial optimization) on instances of the multi-objective knapsack problem with 3, 4, and 5 objectives. Results show that HMOBEDA is a competitive approach. It outperforms the other methods according to the hypervolume indicator.

Um modelo de otimização da operação de terminais petrolíferos usando a teoria das restrições como pré-processamento

In the scheduling of oil transfer operations for harbor plants containing ships, piers, tanks and pipelines, an optimization model is frequently used. However, due to the complexity involved, computational time is a real concern. In this paper, a mixed integer linear programming model (MILP) found in the literature is studied and a preprocessing procedure is proposed. This procedure is based on the Theory of Constraints (TOC), which is used to reduce the model compilation time. Since the problem bottleneck is identified, the corresponding constraints are not considered in the preprocessing step. An analysis based on LP dual price is also carried out to verify actions taken in the preprocessing step. Although optimality cannot be assured, the results obtained show a minor deviation from the optimal solution but with a significant computational time gain.

A fitness landscape analysis of the travelling thief problem

Local Optima Networks are models proposed to understand the structure and properties of combinatorial landscapes. The fitness landscape is explored as a graph whose nodes represent the local optima (or basins of attraction) and edges represent the connectivity between them. In this paper, we use this representation to study a combinatorial optimisation problem, with two interdepend components, named the Travelling Thief Problem (TTP). The objective is to understand the search space structure of the TTP using basic local search heuristics and to distinguish the most impactful problem features. We create a large set of enumerable TTP instances and generate a Local Optima Network for each instance using two hill climbing variants. Two problem features are investigated, namely the knapsack capacity and profit-weight correlation. Our insights can be useful not only to design landscape-aware local search heuristics, but also to better understand what makes the TTP challenging for specific heuristics.