Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Geng, Jing; Li, Mingwei; Dong, Zhihui; Liao, Yusheng

doi:10.1016/j.neucom.2014.06.070

Cited by 74 publications

(31 citation statements)

References 49 publications

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“…They introduced in the restarted simulated annealing PSO method, which was the combination of restarted SA and PSO. Geng et al [140] introduced robust v-support vector regression (RSVR) model to forecast port throughput. In order to search the more appropriate parameters combination for the RSVR model, they presented a chaotic simulated annealing PSO (CSAPSO) algorithm to determine the parameter combination.…”

Section: With Aco Chen and Chienmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: With Aco Chen and Chienmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

View full text Add to dashboard Cite

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“…There exist may studies that have developed forecasting methods and range from simple univariate techniques, for example auto-regressive integrated moving average Kim et al (2011) and exponential smoothing Abraham and Ledolter (2009) models, to more complex multivariate methods that model the interdependencies between a broader set of predictor variables such as socio-economic indicators, gross domestic products, commodity prices, etc. These methods include multivariable adaptive regression splines, dynamic factor models, vector autogreressive and auto-regressive integrated moving average with exogenous variables (Geng et al, 2015, Angelopoulos and Chlomoudis, 2015, Intihar et al, 2015. All of these methods are capable of extracting the models from data and capturing the uncertainty in how the demand will evolve in the future.…”

Section: Machine Learningmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

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When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operatewithin the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable.On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tacticaland operational planning.In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions.More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure.Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand thata facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge forinvestors and operators of rail inter-modal terminals.We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.

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“…According to Geng et al (2015), the most popular time series forecasting techniques for port throughput are the autoregressive integrated moving average (ARIMA) models. Xu (2011) predicted the Shanghai Port cargo throughput and Klein (1996) forecasted Antwerp maritime traffic flow with ARIMA.…”

Section: Techniques Used On Prediction Of Port Throughputmentioning

confidence: 99%

“…For port throughput prediction Geng et al (2015) used robust V-support vector regression model (RSVR) and employed multivariate, nonlinear, non-parametric regression approach (MARS) to determine the final input vectors for RSVR. Liu and Park (2011) used regression analysis to find out which independent variables have the strongest impact on port container throughputs.…”

Section: Techniques Used On Prediction Of Port Throughputmentioning

confidence: 99%

Can market indicators forecast the port throughput?

Çalışkan

Karaöz

2019

IJDMMM

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The main aim of this study is to forecast the likelihood of increasing or decreasing port throughput from month to month with determined market indicators as input variables. Additionally, the other aim is to determine whether artificial neural network (ANN) and support vector machines (SVM) algorithms are capable of accurately predicting the movement of port throughput. To this aim, Turkish ports were chosen as research environment. The monthly average exchange rates of US dollar, euro, and gold (compared to Turkish lira), and crude oil prices were used as market indicators in the prediction models. The experimental results reveal that, the model with specific market indicators, successfully forecasts the direction of movement on port throughput with accuracy rate of 90.9% in ANN and accuracy rate of 84.6% in SVM. The model developed in the research may help managers to develop short-term logistics plans in operational processes and may help researchers in terms of adapting the model to other research areas.

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Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Cited by 74 publications

References 49 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Can market indicators forecast the port throughput?

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