Transportation Mode Selection Research Articles

Optimization of High-Speed Rail Express Transportation Plan Considering Products’ Variable Time Requirements

The global express market has expanded significantly in recent years. High-speed rail express freight transportation has been implemented between multiple cities in China, significantly improving transportation efficiency and effectively meeting demand. In this paper, an optimization model for HSR (high-speed railway) express transport plan is constructed using a space-time-state network, and an improved adaptive genetic algorithm is designed to solve the model. Reasonable transport schemes could be arranged for each cargo flow with the objective of maximizing operational revenue. The numerical experiment based on the Harbin–Dalian high-speed railway in China verifies the effectiveness and applicability of the proposed model. The results show that for HSR operators, appropriate transport strategies can be planned in terms of delivery priorities and transportation mode selection to make full use of the remaining capacity of HSR trains and increase operational revenue.

An optimization model for the downstream supply chain network, considering consolidated warehouses and the selection of transportation mode

PurposeThe supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.Design/methodology/approachThe model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.FindingsTotal cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.Research limitations/implicationsThe study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.Originality/valueThe issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

Analysis of the selection of land transportation modes in Serang City

The role of urban transportation is vast in supporting the mobility of city residents because, after all, urban people need city transportation, and most community groups depend on city transportation to meet their mobility needs. Along with the development of the era of online application-based transit, they are becoming newcomers as a service that makes it easier for people to carry out travel mobility, such as online motorcycle taxis. This study aims to determine the mode selection model between city transportation and online motorcycle in Serang City. The research approach is quantitative, with primary data sources obtained from distributing closed questionnaires to 43 city transportation travelers and 57 respondents to online motorcycle drivers. The data analysis used is the difference binomial logit model to determine the probability model of the traveler in choosing the mode. The results of modeling the choice of the way with the difference in binomial logit results in city transportation having the opportunity/probability of being chosen by travelers in Serang City at 45.64% while the probability/chance of selecting an online motorcycle is 54.36%.

Studi Pemilihan Moda Angkutan Barang Kapal Kayu dan Kapal Fery Rute Kota Tarakan-Kabupaten Tana Tidung

Tana Tidung Regency as a new autonomous region in North Kalimantan in 2007 resulted in increased interactions between regions in support of the supply of food, and other social needs. Tarakan City is an essential region supporting the Tana Tidung Regency. Currently, public the primary transportation from the Tarakan-Tana Tidung is served by two modes of transportation; Ferry Boats and Wooden Boats. Researchers conducted a study to identify the trends and variables that affect the choice of Tarakan-Tana Tidung freight routes to enhance the performance of primary public transportation. Study used variable freight costs per coli, travel time, the timetable for departure, service convenience, and travel time to the Port of Tengkayu 1 or Juata Ferry Port adapted from the study of transportation mode selection by travelling persons. The survey, in the form of a questionnaire to 170 respondents, used the stated preference method and was conducted with multiple regression analysis to obtain the quantitative relationship between a collection of research variables and respondents to obtain utility and probability by modelling the binomial logit. The research results showed that the discrepancy between the Ferry Boats and Wooden Boats was in balance, with 65% of the patrons choosing the Wooden Boat to support the freight activities. All research variables have been influential in the choice of goods transport mode in the most dominant order, namely goods transportation cost per coli variables, travel time, the timetable for departure, service convenience, and travel time to port.

A mathematical modeling approach for supply chain management under disruption and operational uncertainty

AbstractIn this work, we proposed a two‐stage stochastic programming model for a four‐echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two‐stage stochastic model with a multi‐period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.

Analysis of Modal Choice Preference Between Bus and Train in Post COVID-19 Pandemics: A Case of Tasikmalaya-Jakarta Route

The COVID-19 pandemic has had an impact on transportation. The impact is still felt when COVID-19 subsidies and community activities have begun to return to normal. Public transportation modes Trains had not operated optimally before the pandemic, resulting in most people choosing bus transportation modes. The purpose of this study is to determine the community's behavior in choosing the mode of transportation between trains and buses on the Tasikmalaya - Jakarta travel route. The method used in this study used the state preference method with attributes of travel time, travel costs, and waiting time. Data analysis was carried out from questionnaire data shared with 100 respondents. The survey results were collected, and linear regression analysis and modeling were conducted using a binomial logit model. Linear regression analysis using the SPSS program obtained the results of the transportation mode selection model, namely (KA-BUS) = -0.293 – 0.389 (X1) – 1.944x10-5(X2) – 0.060 (X3) where X1 Travel time variable, X2 travel cost variable and X3 waiting time variable. It can be concluded that the bus mode of transportation, with a proportion of mode selection of 57.12 %, has a greater chance of being chosen by travelers than the mode of rail transportation, which has a proportion of mode selection of 42,875%.

Transportation Mode Selection for Organ Transplant Networks by a New Multi-Criteria Group Decision Model Under Interval-Valued Intuitionistic Fuzzy Uncertainty

This study introduces a new multi-criteria group decision-making model in organ transplant transportation networks under uncertain situations. A new combined weighting approach is presented to obtain expert weights with various kinds of opinions by integrating similarity measure and subjective judgments of experts. Also, the CRITIC approach is given to obtain transportation criteria weights. Finally, a novel integrated ranking approach is proposed to calculate the rank of each alternative based on ideal point solution and relative preference relation (RPR) methods. This study regards an interval-valued intuitionistic fuzzy set to cope with the vagueness of uncertain conditions in a real case study.

Multimodal transportation network with cargo containerization technology: Advantages and challenges

Traditional multimodal transportation dispatching of roads, railways, and maritime has been studied for years. This study investigates a bulk cargo distribution problem in a multimodal transportation network considering both the transportation modes of inland waterways and containerization technology. First, we qualitatively discuss the advantages and challenges of transportation using cargo containerization technology from the aspects of transportation cost, time-saving, pollutant emission reduction, and customer preference. Second, a mixed-integer linear programming model is developed, wherein the influence of bridge heights on transportation is considered. The model is validated based on near-practice numerical experiments, and we can obtain the optimal transportation scenario by solving the model, including transportation mode selection, vehicle routing, depot selection, and cargo quantity for containerization. Third, we conduct numerical experiments based on the multimodal transportation network of the Yangtze River in China to discuss the performance of multimodal transportation with containerization technology considerations. The results show that containerization transportation can decrease the total transportation costs for logistics companies under certain parameter configurations. The model and experiments are then extended by considering pollutant emission reductions and time savings, respectively. We uncover the performance of containerization technology by analyzing the Pareto fronts of this technology and traditional bulk cargo transportation. This study elucidates the decision making of logistics companies and government policymaking.

Agent-Based Modelling for Evaluation of Transportation Mode Selection in the State of Guanajuato, Mexico

One of the negative consequences of the industrialization of Mexico favoured by the North American Free Trade Agreement (NAFTA), is the emergence of huge industrial corridors associated with the demand for mobility by commuters who move to their workplace. The demand produces mobility patterns that have a serious impact on air pollution in five cities in the state of Guanajuato that, despite being medium in size, outnumber Mexico City in pollution. The objective of this work is to model a data-driven agent based on the beliefs-desires-intentions model, to predict the selection of transport modes using a J48 decision tree algorithm that was designed from data from the 2015 national census (INEGI). The agent with system change function was programmed in Net logo.The results show that: it is possible to predict the demand of transport considering the: gender, level of education, transfer times and age in the five cities of Guanajuato, in a horizon of three years. With changes in public policies related to mobility and changes in transportation patterns, air pollution would be reduced. The proposed model could be used to support public policies that improve mobility and positively impact air quality in five cities in the state of Guanajuato.

Optimizing inland container logistics through dry ports: A two-stage stochastic mixed-integer programming approach considering volume discounts and consolidation in rail transport

It is essential for container shipping companies to plan for the optimum number and locations of dry ports as well as efficient inland container logistics operations to reduce costs and environmental impacts while improving customer service. The plan for optimum inland container transportation network design must account for demand uncertainty to eliminate financial difficulty due to redundant investment. In this regard, this study proposed a two-stage stochastic mixed-integer programming model for optimizing inland container logistics through dry ports. The model contributes to the state of the art in current research by including a piecewise-linear cost function for railway transportation to account for volume discounts, integrating full container scheduling with transport mode selection and empty container relocation for consolidation, and reflecting the fact that the amount of import and export full containers transported between customers (consignees and consignors) and seaports are exogenously decided. The solution results demonstrated the definite performance superiority of the progressive hedging algorithm over the extensive form solution. Additionally, the value of stochastic solution calculation showed that the application of stochastic solution might result in significant cost savings compared to the application of mean value deterministic solution. The proposed model can be applied for practical necessities to design robust optimum inland container logistics operations using intermodal rail transport with the progressive hedging algorithm as an efficient solution approach.

Intelligent Real-Time Modelling of Rider Personal Attributes for Safe Last-Mile Delivery to Provide Mobility as a Service

This paper develops an intelligent real-time learning framework for the last-mile delivery of mobility as a service in city planning, based upon safe infrastructure use. Through a hybrid approach integrating statistics and supervised machine learning techniques, knowledge-driven solutions based on the specific user rather than generalized safe mobility practices are suggested. One of the most important aspects influencing transport mode and route selection, and safe infrastructure usage, i.e., the age of the user, is simulated. This is because this variable has been described in the literature as a significant variable. Nonetheless, few works deal with such modelling or the learning system. The learning system was applied in the Northumbria region of England’s northeast as a case study. It comprised four building toolkits: (a) Input toolkit, (b) Safety Predictive toolkit, (c) Variable causation toolkit, and (d) Route choice toolkit. An accurate dynamic road safety model and understanding of the critical parameters influencing bicycle rider safety is created. The developed deep learning model’s average distinguishing power to reliably predict the riskiest age group was 95%, with a standard deviation of 0.02, suggesting a good prediction accuracy across all age groups. According to the study’s findings, different infrastructural networks represent varying risks to bicycle riders of different ages. The rider’s age impacts how other road users engage with them. The regional diversity in trip intent and traffic flow conditions were significant elements influencing the safe use of infrastructure for a specific age group. The study’s findings have the potential to considerably influence infrastructure route selection, modelling, and planning. The constructed model, which integrates the rider’s fragility, sensitivity to externalities, and the varied safety impact dependent on its features, may even be used for the infrastructure still in the planning/design phase. It is envisaged that this research would aid in adopting sustainable (green) transportation options and the last-mile delivery of mobility as a service. Future work should aim to uncover the sensitivities of a rider from different countries and make a baseline comparison scenario.

Implementation Of Decision Selection In The Selection Of Transportation Modes Among Workers And Students In Medan City Using Ahp And Electric Methods

<p>The mode of transportation is a means of connecting used by workers and students to be able to help with all daily activities. The development of transportation modes is increasingly significant with the addition of wider technological sophistication, so that there are more and more diverse types of transportation modes. The purpose of this research is to determine the factors that have the most influence on the choice of mode and provide more effective decisions among workers and students in the selection of public transportation modes to be used. In this research, the decision support system (DSS) method is used, namely the analytical hierarchy process (AHP) and elimination et choix traduisant la reality (ELECTRE) by performing multi-criteria calculations that compare the criteria and alternatives. Where the results obtained in this research is that the most influential factor in the selection of transportation modes among workers is time efficiency with a weight of 25.9 %, while the influential factor for students is cost which produces a weight of 30.7 %. The most effective and efficient alternative mode of transportation among workers is Go-Jek Bike, while for students it is Transmetro Deli.</p>

Stated Preference Analysis With Latent Variables for Higher Speed Rail

Recently, the necessity to redefine the mode selection model (modal split model) has emerged owing to the diversification of transportation systems. In addition to socio-economic factors, preference factors and user characteristics significantly affect the selection of transportation mode. Therefore, user preferences are reflected in the modal split model. The latent class model is highly descriptive, and its suitability can be improved. With the advent of high-speed rail (Great Train eXpress [GTX]), Seoul has established a competitive and complementary relationship for the demand of passengers between general and great express rail. Therefore, a modal split model reflecting the characteristics of the GTX, which is redefined by applying a latent variable to the stated preference analysis, is necessary. Latent class analysis uses the Bayesian information criterion and log-likelihood to distinguish the travel property (Cluster1), station property (Cluster2), and transfer property (Cluster 3). After comparing the time values for each cluster, the GTX preference is analyzed based on the inner and outer circle situations. A model that accurately reflects the characteristics and preferences of passengers is proposed in this paper. In the future, strategies can be established for the inner and outer circle situations, and higher operational efficiency can be achieved by combining the GTX and subway.

Analisis Pemilihan Moda Transportasi Untuk Perjalanan Ke Kampus (Studi Kasus: Civitas Akademik Politeknik Negeri Bali)

In carrying out the daily movement of man from one place to another requires a mode of transportation. In this context, the ownership and use of private and public vehicles can be said to be one of the alternatives for society in meeting the demands of the movement. Specifically in the travel pattern there is information about the characteristics of the traveler and the type of activity of the individual or person per person. Jimbaran area as a tourist area and synonymous with educational institutions and campuses, of course there are many tourist attractions and campuses, one of which is the Bali State Polytechnic. Along with this, research on the preference behavior of the use of student transportation vehicles, aims to analyze the choice of modes on daily trips to PNB, using the Analytic Hierarchy Process method with expert choice software version 11. to find out the most influential factors. The instrument includes a questionnaire form as well as conducting observations and direct interviews with the PNB academic community to identify the characteristics of the PNB academic community in the selection of transportation modes, analyzing the factors that most influence the use of transportation modes, both private vehicles and public vehicles. The results showed that the first priority for undergraduate students at the criteria level was the criteria for getting the highest weight of 36.4% followed by the cost criteria of 34.1%, and finally the safe criteria of 2 9.5%, with an inconsistency value of 0.00096 . From the results of the expert assessment through the questionnaire given, the alternative mode of transportation chosen was motorcycles by 46.4% as the first alternative in the selection of transportation modes, followed by private cars by 35.7% and finally public transportation by 17.9%.

Analyzing anchoring bias in attribute weight elicitation of SMART, Swing, and best‐worst method

AbstractIn this study, the existence of anchoring bias—people's tendency to rely on, evaluate, and decide based on the first piece of information they receive—is examined in two multi‐attribute decision‐making (MADM) methods, simple multi‐attribute rating technique (SMART), and Swing. Data were collected from university students for a transportation mode selection. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Statistical analyses of the weights obtained from the two methods show that, compared to Swing (with a high anchor), SMART (with a low anchor) produces lower weights for the least important attributes, while for the most important attributes, the opposite is true. Despite their differences in anchoring bias, analytical approaches supported by empirical studies suggest that both methods (SMART and Swing) overweigh the less important attributes and underweigh the more important attributes. As such, we examined whether the best‐worst method (BWM), which has two opposite anchors in its procedure (a possible promising anchoring debiasing strategy), could produce results that are less prone to anchoring bias. Our findings show that the BWM is indeed able to produce lower weights (compared to SMART and Swing) for the less important attributes and higher weights for the more important attributes. This study shows the vulnerability of MADM methods with a single anchor and supports the idea that MADM methods with multiple (opposite) anchors, like BWM, are less prone to anchoring bias.

A hesitant fuzzy multiplicative Base-criterion multi-criteria group decision making method

Hesitant fuzzy sets have a unique characteristic that its basic element could manifest the assessment values of different decision makers on the same option under a certain criterion. Base-criterion method is a very significant tool for calculating the weights of the criteria in multiple criteria decision-making. In this paper, we developed a novel approach hesitant fuzzy BCM based on hesitant fuzzy multiplicative preference relation for multiple criteria group decision making. The base-comparison of the preferential criterion relative to other criteria is expressed as linguistic terms, which might be converted into hesitant multiplicative elements (HMEs). HMEs are extended along the same length according to the attitude of the decision makers. Then normalized optimal hesitant fuzzy weights are calculated. The normalized optimal hesitant fuzzy weights of criteria may be transferred to crisp values by employing score function. To illustrate the applicability and suitability of hesitant fuzzy BCM, we analyse the optimal transportation mode selection problem and car selection problem under hesitant fuzzy environment. The outcomes of the proposed model indicate that the hesitant fuzzy BCM is highly consistent and can yield appreciable preference ranking of criteria and alternatives.

PENGARUH SUPPLY CHAIN MANAGEMENT TERHADAP OPERASIONAL PERUSAHAAN DAN KENDALA PROCUREMENT SISTEM ERP PADA PT UNILEVER INDONESIA TBK

This research aims to determine the effect of Supply Chain Management and constraints of existing ERP systems in PT Unilever Indonesia Tbk is a company engaged in the household production industry by offering food products, beverages, kitchen needs, to toiletries. In an effort to develop its business operations, it is known that PT Unilever Indonesia Tbk implements its management strategy through ERP and Supply Chain Management systems. The ERP system at PT Unilever Indonesia Tbk functions as an information integration tool for all company stakeholders, while Supply Chain Management functions as a tool for monitoring the flow of goods to consumers and the amount of inventory. Thus, the two strategies are related to the certainty of the performance of Unilever employees. This paper aims to analyze the performance and the relationship between supply chain management, and the effect of the ERP system implemented by Unilever companies on operational performance through interaction and communication of information in order to be efficient in implementing supply chain management strategies and ERP systems. The research method uses a document study approach, as well as primary and secondary data collection techniques. The results of the study reveals that the Supply Chain Management strategy of PT Unilever Indonesia Tbk has four components, namely supply chain uncertainty, management of economies of scale, selection of transportation modes, and product inventory control; as well as the ERP and Supply Chain Management systems of PT Unilever Indonesia Tbk have a positive and significant impact on the company's performance so as to obtain profits and achieve company goals.

Transportation Modes During Pandemic Covid-19 in Indonesia

The purpose of this study is the selection of transportation modes during the pandemic in Indonesia. This study uses an analysis of the SEM (Structural Equation Model) analysis technique, with a Partial Least Square (PLS) approach. The results of the analysis show that the cost and safety variables do not significantly affect the choice of transportation mode, this is due to the Covid-19 pandemic which resulted in changes in variables community priorities in the selection of transportation modes. While the variables of comfort and compliance with health protocols have a significant effect on the choice of transportation modes which have an impact on reducing the risk of being exposed to COVID-19. Therefore, it is necessary to study the improvement of minimum service standards in order to increase the convenience of users of transportation services and carry out supervision to ensure the implementation of minimum service standards in order to improve comfort and compliance with health protocols.

Emphasis on Occupancy Rates in Carbon Emission Comparison for Maritime and Road Passenger Transportation Modes

Carbon emissions generated by the transportation sector represent a large part of total greenhouse gas emissions and are thus subject to various policies and initiatives for emission reduction and the development of sustainable transportation networks. Furthermore, passenger transportation generates a significant amount of emissions within this sector, especially in those countries with large and developed tourist sectors. Examples of such countries are Italy and Croatia, located in the Adriatic region, with a large portion of passengers between them being transported utilizing mainly maritime and/or road transportation modes. A proper analysis of the impact of these transportation mode choices on carbon emissions is essential to enable the selection of the optimal transportation mode for the particular transportation route with respect to the generated emissions. Therefore, this study determines the carbon emissions of the maritime and/or road transportation modes on the existing cross-border passenger transportation routes between Italy and Croatia. For the analysis, the Adriatic region was divided into three sections—the Northern, Middle, and Southern regions—each characterized by specific transportation routes defined by geographical features and distances. The results obtained from this research are presented as total carbon emissions for each transportation mode separately, based on each of three chosen routes in different regions. In addition, a carbon emission comparison between each transportation mode in regard to occupancy rate is performed and presented separately for each chosen route based on its specific distances, transportation means, and features. Finally, by providing an analysis of the existing state, this study can serve as a basis for Italy–Croatia cross-border passenger mobility network modernization and the introduction of new, sustainable, and multimodal transportation routes.

Cross-Border E-Commerce Logistics Transportation Alternative Selection: A Multiattribute Decision-Making Approach

Cross-border e-commerce logistics and transportation system is important link in the cross-border e-commerce supply chain. How to choose a fast, safe, reliable, and low-cost logistics transportation mode is an urgent problem to be solved in cross-border e-commerce logistics. In view of this, this paper first analyzes the system characteristics of cross-border e-commerce logistics transportation systems and analyzes the factors affecting the choice of transportation mode from the perspective of reliability. Then, based on the ELECTRE method system, a multiattribute decision-making method for cross-border e-commerce logistics transportation mode selection is proposed. Finally, through a data example, it is verified that the model constructed in this paper and the proposed multiattribute decision-making method can effectively help cross-border e-commerce choose logistics transportation mode according to goods.