Route/Modal choice analysis and tradeoffs evaluation of the intermodal transport network of Peninsular Malaysia

Gohari, Adel; Matori, Abdul Nasir; Yusof, Khamaruzaman Wan; Toloue, Iraj; Myint, Khin Cho; Sholagberu, Abdulkadir Taofeeq

doi:10.1080/23311916.2018.1436948

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…The others rows are considered as restrictive conditions of the linear model, (formulas from 2 to 11). The liner model for the first criterion is as follow: x + x + x + x + x (2) 0.00 ≥ 0.50 0.50 2 1 x + x (3) x + x + x + x + x (6) 0.00 ≥ 0.50 0.50 2 1 x + x (7) 0.20 ≤ 0.20 0.20 0.20 0.20 0.20 (8) 0.50 ≤ 0.50 0.50 2 1 x + x (9) 0.50 ≤ 0.50 0.50 2 1 x + x (10) 0.50 ≤ 0.50 0.50 2 1 x + x (11) where x j -score of each alternative; j = 1,…,m -number of variants.…”

Section: Resultsmentioning

confidence: 99%

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Application of SIMUS method for assessment alternative transport policies for container carriage

Stoilova

2019

Engineering for Rural Development

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The purpose of this paper is to study the container transportation by rail and by road as alternative policies for carriage and to choose the effective one. A methodology to evaluate both transportation and ranking them has been developed. The Sequential Interactive Modelling for Urban Systems (SIMUS) as a tool to aid decision-making problems with multiple objectives has been applied. The methodology of the research includes two scenarios taking into account the weights of criteria -the first scenario does not use the weights of criteria and for the second one it is necessary to apply the weights which are pre-defined. The methodology of the research includes four steps. The first step defines the quantitative and qualitative criteria for assessment of container transportation. The investigated strategies for transportation that have been studied are intermodal rail transport and road transport. The weighs of criteria have been determined in the second step. They are used only in the second scenario. The third step of the research formulates the alternatives of carriage by containers and includes two ways of transportation: by container block trains and by trucks. The SIMUS method has been applied in the fourth step for ranking the variants of transportation. The method solves successive scenarios formulated as linear programming. This step involves ranking of alternatives and the choice of optimal type of transportation. The methodology is approbated for the routes Sofia-Varna from the railway and road network of Bulgaria. It was found that the technologies that include rail transport are the best variants of transportation. The methodology can be applied for assessment different types of transportation and different transport policies.

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Section: Resultsmentioning

confidence: 99%

“…The containerized freight transport through the intermodal transport network of Peninsular Malaysia has been investigated in [6]. The authors elaborated a model to analyse the transport network based on ArcMap software and MatLab software.…”

Section: Introductionmentioning

confidence: 99%

Application of SIMUS method for assessment alternative transport policies for container carriage

Stoilova

2019

Engineering for Rural Development

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“…The sample average approximation method generates robust transportation plans. Gohari et al use ArcMap software to build transport network, use MATLAB software to design user interface and develop the shortest path algorithm for analysis of transport network, construct the path selection model of multimodal transport network, and determine the best transport route and mode from the origin to destination for different objective functions (such as distance, time, emission and cost) [12]. Sun et al pointed out that in large urban centers (such as Chicago or Los Angeles), road transport at both ends of roadrail intermodal transport may involve hundreds of drivers and up to 500 containers moving to or out of several different rails ramps every day [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Baykasoglu and Subulandivided the transport time of multimodal transport into road, sea and railway service time according to the mode of transport, including loading/unloading time, road, sea and railway transport time, combined operation time at the transfer point, customs clearance time and waiting time of the port and railway station [8]. Gohari et al divided the transport time of multimodal transport into transit time and change time, and transit time is directly proportional to distance and inversely proportional to running speed [12]. The existing studies consider the time of combined transport, which is mainly divided according to the mode and process of transport.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design

et al. 2019

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In this study, we aim to develop a system optimization model of Railway Freight Transportation Routing Design (RFTRD) and conduct solution analysis which is based on the improved multi-objective swarm intelligence algorithm. The proposed improved multi-objective swarm intelligence algorithm is applied to solve the combinatorial optimization problem of railway door-to-door freight transportation through design, and provide decision support for railway vehicle door-to-door freight transportation through design. The optimization results shows that, the random multi-neighborhood based multi-objective shuffled frog-leaping algorithm with path relinking (RMN-MOSFLA-PR) can be better applied to solve the combined multi-objective optimization problem, and this proposed improved algorithm can find Pareto frontier through the comparative analysis in the design example of railway door-to-door freight transportation. The frontier can provide support for railway transportation enterprises, arrange the decision-making of the starting and ending stations for multiple shippers, and optimize the use of existing transportation resources, so as to reduce the transportation cost and time of the system.INDEX TERMS Intelligent water drops algorithms, multi-objective cluster intelligent algorithms, random frog-leaping algorithm, random multi-neighborhood structure, routing design.

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“…Behdani et al (2016), , and Agbo and Zhang (2017) studied the transfer process between mode and intermodal synchronization schemes. While Sitek et al (2020), Shao and Dessouky (2020), Zeng et al (2020), and Gohari et al (2018) investigated routing selection. Sun et al (2015) discussed optimization models for freight routing planning problems (FRPP) on multimodal transport networks and present algorithms to complete the development of the FRPP model.…”

Section: Introductionmentioning

confidence: 99%

Freight route planning in intermodal transportation network to deal with combinational disruptions

2020

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Route/Modal choice analysis and tradeoffs evaluation of the intermodal transport network of Peninsular Malaysia

Cited by 8 publications

References 12 publications

Application of SIMUS method for assessment alternative transport policies for container carriage

Application of SIMUS method for assessment alternative transport policies for container carriage

Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design

Freight route planning in intermodal transportation network to deal with combinational disruptions

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