Transportation problems for intermodal networks: Mathematical models, exact and heuristic algorithms, and machine learning

Göçmen, Elifcan; Erol, Rızvan

doi:10.1016/j.eswa.2019.06.023

Cited by 27 publications

(11 citation statements)

References 31 publications

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“…Elifcan and Rizvan presented a pick-up routing combinatorial problem with a three-dimensional loading constraint, clustered backhauls at the operational level, and trained loading at the tactical level for an intermodal transportation network. It was solved by the combination of mathematical models, genetic algorithm, and machine-learning algorithm [47]. Dai et al proposed a sub-bus based on the mobile data that contained the passenger flow forecasting and dynamic route planning of the stations [48].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

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Section: Literature Review and Related Workmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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“…(21), which determines their lower bound, compatibility requirements indicated by Eqs. (10), (12), and ( 13) can be all satisfied by Eqs. ( 58)- (60).…”

Section: Improved Linear Formulationsmentioning

confidence: 99%

“…In recent years, intermodal transportation planning problem has drawn remarkable attention in the transportation sector [12,13]. As sorted by Caris et al [11], there are six topics related to the intermodal transportation planning in the strategic, tactical, and operational levels, i.e., policy support, terminal network design, intermodal service network design, intermodal routing, drayage operations, and information and communication technologies' (ICT) innovations.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approaches and Simulation-Based Reliability Modeling to Solve a Road–Rail Intermodal Routing Problem with Soft Delivery Time Windows When Demand and Capacity are Uncertain

Sun

2020

Int. J. Fuzzy Syst.

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In this study, a freight routing problem considering both soft delivery time windows and demand and capacity uncertainty in a road-rail intermodal transportation system is investigated. According to fuzzy set theory, uncertain demands and capacities are formulated as trapezoidal fuzzy numbers. Soft delivery time windows under a fuzzy environment is established, in which fuzzy periods caused by early and late deliveries that lead to penalty are modeled based on maximum functions. To solve the routing problem yielding the above characteristics, this study designs a fuzzy mixed-integer nonlinear programming model whose objective is to minimize the total costs created in the road-rail intermodal transportation activities. After using the fuzzy expected value method to address the fuzzy objective, two fuzzy approaches, i.e., fuzzy chance-constrained programming method and fuzzy ranking method, are separately adopted to undertake the defuzzification of the fuzzy constraints. Improved linear formulations of the model are then produced to make it easier to solve. A simulation-based reliability modeling is developed to quantify the reliability of the optimization results given by different fuzzy approaches under different parameter settings in a simulation environment. Finally, an empirical case is presented to verify the feasibility of the proposed methods. The effects of demand and capacity fuzziness on the routing optimization are revealed, and an optimization procedure that helps decision-makers to select a more suitable fuzzy approach and determine the best parameter setting for a given case is demonstrated. Some insights that are helpful for organizing a reliable transportation are also drawn. Keywords Road-rail intermodal routing Á Demand uncertainty Á Capacity uncertainty Á Soft time windows Á Simulation-based reliability Á Fuzzy programming model Á Fuzzy expected value Á Fuzzy chance-constrained programming Á Fuzzy ranking Electronic supplementary material The online version of this article (

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“…ÞAn overview of this method (N dataset, initial k clusters, cluster centroid, and cluster label are represented by A, B i , B, C, respectively) is shown as (Göçmen and Erol 2019)…”

Section: Solution Algorithmmentioning

confidence: 99%

Linear programming with fuzzy parameters for inventory routing problem in effective management of personal protective equipment: a case study of corona virus disease 2019

Göçmen

2020

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Coronavirus disease 2019 (COVID-19) brings about severe disruptions for human living, economics, and environmental balance. This pandemic has boosted the demand for personal protective equipment (PPE) and thus, medical wastes. Thus, a research hypothesis is stated whether it would be possible to provide more efficient distribution and inventory planning to optimize the PPE availability in a fuzzy environment. To confirm the hypothesis, in addition to addressing a scientific novelty considering integration of linear programming with fuzzy parameters, named as the Jimenez's method and clustering heuristic methods, the practicality of these approaches is provided on a real case study and generated instances from Turkey. The computational results reveal that clustering the 32% of the hospitals ensures optimum total cost and solution time while feasibility degree changes between 0, 4, and 1 values of the Jimenez are not more effective on the results. Improved cost results reveal the economic importance of the healthcare supply chain management and optimal PPE distribution and inventory planning can break down the disease propagation and reduce the detrimental impacts on human life. The proposed approach, for the first time, ensures practical and theoretical insights of distribution and storage decisions across disciplines, namely, engineering and health-care organization authorities.

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Transportation problems for intermodal networks: Mathematical models, exact and heuristic algorithms, and machine learning

Cited by 27 publications

References 31 publications

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

Fuzzy Approaches and Simulation-Based Reliability Modeling to Solve a Road–Rail Intermodal Routing Problem with Soft Delivery Time Windows When Demand and Capacity are Uncertain

Linear programming with fuzzy parameters for inventory routing problem in effective management of personal protective equipment: a case study of corona virus disease 2019

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