The Problem of Sustainable Intermodal Transportation: A Case Study of an International Logistics Company, Turkey

A Sustainable Intermodal Location-Routing Optimization Approach: A Case Study of the Bohai Rim Region

The optimal intermodal nodes and routes are two of the most challenging issues for intermodal participants. We present a two-phase approach that includes the fuzzy c-means clustering method (FCM) and a multi-objective optimization model to solve intermodal location-routing issues. A weighted sum technique and a genetic algorithm (GA) are designed to address this model. The two-phase approach is beneficial in meeting different market demand preferences of intermodal participants. It also has applications in solving the sustainable intermodal location-routing problems, further solving the network optimization problem in large-scale scenarios. A typical intermodal transport network in the Bohai Rim region is used to verify the effectiveness of this approach. The results provide references for the participants in the Bohai Rim region to choose the optimal intermodal nodes and routes. The findings also offer theoretical insights for optimizing intermodal networks in other regions of China, with goals of improving sustainable transport efficiencies.

Sustainable Transportation in Practice: A Systematic Quantitative Review of Case Studies

After the COVID-19 pandemic and the spectrum of new climate change disruptions in the supply chain, a holistic approach towards sustainable transportation is needed. Sustainable transportation could benefit sustainable development from different angles; reduced traffic deaths, increased share of renewable energy, higher quality of transport-related infrastructure, increased satisfaction with public transportation, increased responsible consumption and production, and reduced fossil fuel consumption. This study is an attempt to show whereon the scholars were focused previously and where the focus needs to be more on. This study has reviewed 358 case studies and categorized them into twenty groups based on the transportation mode and eleven groups based on the authors’ primary areas of concern. Keyword analysis followed by topics modeling showed three non-overlapping trends in the cohort. The results, with a corroboratory investigation on the benefits of the United States’ infrastructure bill, were discussed in four categories: in-vehicle improvements, built-environment elements, human factors, and planning and regulations.

Identifying Factors for Selecting Land over Maritime in Inter-Regional Cross-Border Transport

Several cross-border land corridor projects have been implemented worldwide, because land transport is a vital alternative to international maritime transport in inter-regional transport. Maritime transport generally costs less than land transport, but it is much slower. Nonetheless, land transport can be more appropriate than maritime under certain situations. This study aims to identify factors that can help select between these two modes in long-distance inter-regional cross-border transport; to this end, a Tobit model is employed to estimate the dependent variable, i.e., the land ratio of origin–destination pairs between countries and/or areas. Eight variables are identified as significant: distance, export of manufacturing commodity, landlocked country/area, neighboring country/area, country risk, infrastructure level, port-access time, and maritime transport frequency. We also find that geographical conditions, country relationship, and regulations are barriers for selecting land transport. However, cross-border land corridors contribute to the increase of land ratio.

Towards Deriving Freight Traffic Measures from Truck Movement Data for State Road Planning: A Proposed System Framework

To make the right decisions on investments, operations, and policies in the public road sector, decision makers need knowledge about traffic measures of trucks, such as average travel time and the frequency of trips among geographical zones. Private logistics companies daily collect a large amount of freight global positioning system (GPS) and shipment data. Processing such data can provide public decision makers with detailed freight traffic measures, which are necessary for making different planning decisions. The present paper proposes a system framework to be used in the research project “A new system for sharing data between logistics companies and public infrastructure authorities: improving infrastructure while maintaining competitive advantage”. Previous studies ignored the fact that the primary step for delivering valuable and usable data processing systems is to consider the final user’s needs when developing the system framework. Unlike existing studies, this paper develops the system framework through applying a user-centred design approach combining three main steps. The first step is to identify the specific traffic measures that satisfy the public decision makers’ planning needs. The second step aims to identify the different types of freight data required as inputs to the data processing system, while the third step illustrates the procedures needed to process the shared freight data. To do so, the current work employs methods of literature review and users’ need identification in applying a user-centralized approach. In addition, we develop a systematic assessment of the coverage and sufficiency of the currently acquired data. Finally, we illustrate the detailed functionality of the data processing system and provide an application case to illustrate its procedures.

Planning an Intermodal Terminal for the Sustainable Transport Networks

Growing competition in the global market imposes the need for adequate planning of transportation processes and development of intermodal transport networks, whereby intermodal terminals play a key role. This paper proposes a methodology for prioritization of the intermodal terminal’s development features, as the procedure in its planning process, leading to the design of the intermodal terminal in accordance with the needs of various stakeholders and the principles of the sustainable development. As the stakeholders often have conflicting interests and objectives, it is necessary to consider a broad set of requirements and developmental features that enable the fulfillment of the defined requirements. In order to solve the problem this paper proposes a new hybrid multi-criteria decision-making model that combines Delphi, Analytical Network Process (ANP) and Quality Function Deployment (QFD) methods in the fuzzy environment. The applicability of the proposed model is demonstrated by solving an example of planning an intermodal terminal in Belgrade.

Grocery Delivery or Customer Pickup—Influences on Energy Consumption and CO2 Emissions in Munich

The number of supermarkets offering grocery delivery has increased in recent years. Many studies conclude that CO2 emission savings result from this concept. Since the delivery of groceries also consumes energy and produces emissions, break-even points can be calculated, where the delivery is environmentally beneficial compared to customer pickup. In this paper, influences of differing vehicle use on break-even points for savings of energy and CO2 emissions are analyzed for the case of Haidhausen Süd, a district in Munich, Germany. Internal combustion engine and electric vehicles are investigated to depict current as well as future trends. After an introduction to the methodology used, the potential to save energy and CO2 emissions related to the delivery of groceries in the chosen district of Munich are evaluated. Subsequently, influences on the break-even points are presented and discussed. As the results show, a delivery of groceries leads to energy and carbon dioxide savings in a wide range of private vehicle use for grocery shopping trips. Nevertheless, if the complete customer vehicle fleet is electrified, the use of delivery vehicles with an internal combustion engine could cause an additional environmental impact at the current modal split for shopping trips in Germany.

A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows

Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands. We set multiple transportation orders as the optimization object and employ soft time windows to reflect the customer requirement on on-time transportation. Under the above situation, we establish a fuzzy mixed integer nonlinear programming (FMINLP) model to formulate the capacitated road–rail multimodal routing problem with demand uncertainty and time windows. We first use the fuzzy expected value model and credibility measure based fuzzy chance-constrained programming to realize the defuzziness of the model and then adopt linearization technique to reformulate the crisp model to finally generate an equivalent mixed integer linear programming (MILP) model that can be solved by standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. Sensitivity analysis and fuzzy simulation are combined to quantify the effect of demand uncertainty on the routing problem and also reveal some helpful insights and managerial implications.