A metaheuristic for the containership feeder routing problem with port choice process

In this paper, we focus on understanding the joint problem of container ship route generation and consolidation center selection, two important sub-problems influencing the effectiveness of the liners shipping industry, which addresses the ship-routing problem. Two different metaheuristics procedures are presented that both consist of two stages: a solution construction phase (either nearest neighborhood with greedy randomize and Clark and Wright with greedy randomize selection) and a solution improvement phase, based on local search. Both metaheuristics are compared in terms of quality of solution, robustness analysis and computing time under variety of instances, ranging from small to large. A thorough comparison evaluation uncovers that both metaheuristics are close-to-each other. An argument in favor of the nearest neighborhood with greedy randomize approach is that it produces better performance than Clark and Wright configuration. Additionally, through sensitivity analysis, we investigate and test two hypotheses in this paper

A comparison of methods for denoising of well test pressure data

Abstract Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS–ARMA to real field data shows promising results