Synchromodal Transport Research Articles

Synchromodal transport vs. conventional hinterland transport: a stakeholder theory analysis

ABSTRACT Synchromodality aims to reduce costs, emissions and delivery times by offering a flexible and adaptive mode choice and seamless integration of transport modes, both horizontally and vertically. The concept, which has hardly been applied in practice, poses significant requirements for the involved stakeholders and their interactions. However, there has been limited research on how stakeholder dynamics should be shaped and adapted compared to the conventional hinterland transport stakeholder dynamics to facilitate synchromodal transport. Our study fills this gap by presenting an overview of the stakeholders that should be involved in synchromodality, their decision-making responsibilities and relations (referred to as stakeholder dynamics), illustrated in a conceptual synchromodal stakeholder network. Then, this network is compared to the conventional port-hinterland transport stakeholder network, which is the transport chain in which synchromodality is likely to be applied. To this end, this paper analyses and integrates literature on three topics: synchromodality, stakeholder dynamics in the port-hinterland transport chain and stakeholder theory. Our analysis reveals that facilitating synchromodal transport necessitates adaptations such as the inclusion of additional stakeholder roles, the delegation of decision-making power, and the evolution towards collaborative relationships. However, examining these requirements through the lens of stakeholder theory exposes significant complexities, such as an increased risk of opportunism, thereby giving rise to the formulation of an extensive future research agenda. The findings were tested against industry experts’ views in a workshop and focus group. This analysis should enhance the understanding of synchromodal stakeholder dynamics and its challenges from a stakeholder theory perspective, contributing to further developing dynamic stakeholder management strategies.

Evaluating Logistics Companies’ Readiness towards Adopting Synchromodality in the Flanders Region

The transport sector accounts for approximately a quarter of the EU’s total greenhouse gas emissions, with freight transport alone accounting for about one-third of the overall transport-related emissions. Mitigating the sector’s environmental impact is crucial for tackling climate change and achieving sustainable development goals. Modal shift is one of the main solutions to address this challenge; however, many companies have yet to realize its full potential. This paper presents a survey conducted in the Flanders region of Belgium, aiming to identify the challenges and barriers faced by industry players in this key geographical area and to explore the reasons behind the limited implementation of synchromodal transport among them. The survey evaluates the current state of synchromodal transport adoption and offers valuable insights for policymakers and industry stakeholders aiming to enhance sustainability in the logistics sector. The findings emphasize that to overcome the identified challenges, both policy support and the companies’ commitment are required. Policy support includes establishing consistent regulations and promoting greener transport modes through providing incentives and technological advancements. This research contributes to the field by examining barriers to the adoption of synchromodality and exploring its application within the context of Flanders. By focusing on this strategic logistics hub, the study provides insights and recommendations tailored to the specific challenges of the region’s logistics sector. The challenges faced by industry players in Flanders offer a deeper understanding of modal shift dynamics, facilitating informed decision-making for policymakers and industry stakeholders. Implementing these strategies paves the way for more environmentally friendly, efficient, and integrated transport, benefiting both the industry and the planet.

A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.

Synchromodal transport re-planning: an agent-based simulation approach

In the rapidly evolving global marketplace, the logistics sector faces a multitude of challenges that demand implementation of more resilient solutions to respond to any future disturbance. Synchromodal transport, which is viewed as an extension of multimodal transport, is known as a key answer to this issue, as it provides more flexible and sustainable freight transport and also focuses on collaboration between different logistics players. We consider synchromodal transport as a collection of agents that not only have their own characteristics and behaviors, but also interact with each other, which impacts the entire system. In this paper, we study the system using an Agent-Based Modeling approach. The network represents the combination of long-haul and drayage transport, where pre-haulage and end-haulage are done only by truck, and the rest can be done by trucks, trains, or barges. A numerical experiment is conducted to evaluate cost savings and emissions reduction under different logistics service providers’ relation and re-routing scenarios. Our findings show that synchromodal scenarios are more economically and environmentally efficient, and that they lead to higher flexibility and reliability compared to business-as-usual scenarios. Additionally, our model verifies that the cost saving is considerable when logistics service providers collaborate with each other. The results of sensitivity analyses show consistent overall trends when comparing the different scenarios. Therefore, the conclusions drawn from the original experiment appear to be applicable, not only for that specific instance, but have broader relevance and applicability.

An advanced intermodal service network model for a practical transition to synchromodal transport in the US Freight System: A case study

• Provide managerial insights for synchromodal freight transportation network designs in long haul transportation. • Present routing models based on MIP for a free mode choice network with different intermodal terminal schedules and delays. • Develop a genetic algorithm to solve the model for an advanced intermodal service network design. Free mode choice, termed “synchromodality,” is an extension of intermodal service network design and is still in the early stages of modeling development. European countries have already started moving toward realizing this innovative transportation system. However, advancement in global transport with longer distances is rare and needs more infrastructural preparation and studies to clarify the steps for such a transition. In this paper, an advanced intermodal service network model (AI-SNM) is proposed to support the development of synchromodal transportation systems. This mixed-integer programming (MIP) model finds the optimal path between O/D pairs while considering horizontal integration of variant transport modes in a supply chain network along with resource constraints and time windows. It minimizes the total transportation cost, transshipment cost, and tardiness with a penalty for delays at intermodal terminals and overdue costs at the destination that accounts for the opening and closing times of the terminals. In order to solve the model for large problem instances, an efficient multiobjective genetic algorithm using a novel coding approach is developed. The algorithm is tested on two US-based case studies, showing the capability of the model to provide cost- and time-saving advantages in long-haul freight. The results of this study can be applied to long-distance global transportation with similar geography and scale.

Evaluating synchromodal transport with agent-based simulation

Synchromodal transport implies using dynamically several modes of transport. Two relevant aspects of synchromodality are collaboration between actors and flexibility, aiming to make as many decisions as possible in real-time, imposing several changes on the logistic system's performance. This study aims to model the interactions between actors and their reactions to disruptions within the transport network. Using an agent-based modelling approach, the system is modelled as a collection of heterogeneous agents with complex interactions. Different collaboration scenarios and reaction strategies are modeled. The result shows improvement in the average cost, as the collaboration between logistics service providers and the flexibility of their response to disruptions increases. This model provides a testbed for stakeholders to assess different scenarios and ideas inexpensively and risk-free.

Distributed model predictive control methods for intermodal transport cooperative planning based on ADMM

The cooperative planning in intermodal transport networks can obtain the global optimal decision under the premise of ensuring the data privacy of each role in the cooperation and avoiding massive data transmission. For the control of container flow in intermodal transport networks, the distributed model predictive control (DMPC) method can effectively realize cooperative planning, but the convergence speed of the existing DMPC methods is slow. Therefore, this study attempts to construct faster DMPC methods for cooperative planning. The Jacobi proximal distributed model predictive control (JP-DMPC) and dual consensus distributed model predictive control (DC-DMPC) methods are constructed for container flow control based on two variants of alternating direction method of multipliers (ADMM). The simulation experiments prove that the convergence speed of JP-DMPC and DC-DMPC methods is higher than that of the state-of-the-art method on the premise that the time cost and interaction data volume of each iteration do not change much, and the DC-DMPC method improves planning speed particularly significantly. This study provides new methods for intermodal transport cooperative planning and has significance for the development of synchromodal transport.

Synchromodal transport planning considering heterogeneous and vague preferences of shippers

In synchromodal transport, a freight forwarder usually serves multiple shippers with heterogeneous and vague preferences, such as low-cost, fast, or reliable transport. Ignoring shippers’ preferences will negatively impact the satisfaction of shippers and lead to the loss of them in the longer run. In order to incorporate these preferences, a Synchromodal Transport Planning Problem with Heterogeneous and Vague Preferences (STPP-HVP) is proposed and formulated as a mathematical model. Heterogeneous and Vague Preferences (HVP) are modeled through Multiple Attribute Decision Making approaches that integrate fuzzy set theory. The proposed model has two objectives, i.e., maximizing the number of served requests and minimizing the transportation cost. Preferences of shippers are set as constraints such that the freight forwarder needs to satisfy the preferred levels for each attribute. A heuristic algorithm (Adaptive Large Neighborhood Search) is proposed to find (near) optimal solutions. The case study in the European Rhine–Alpine corridor demonstrates that the proposed model can provide more attractive solutions to shippers compared with optimization which ignores preferences. Under various scenarios, the attributes, such as cost, time, emissions, reliability, and risk of damage, are analyzed and the (near) optimal modes and routes are suggested according to HVP. Moreover, the results show that the conflicts among attributes, conflicts among shippers, and conflicts between the freight forwarder and shippers are resolved by making one actor more satisfied without compromising any other actor’s preferences.

Synchromodal transport planning with flexible services: Mathematical model and heuristic algorithm

As a critical feature of synchromodal transport (ST), service flexibility plays an important role in improving the utilization of resources to reduce costs, emissions, congestions, and delays. However, none of the existing studies considered flexible services under the framework of synchromodality. This paper develops a Mixed Integer Linear Programming (MILP) model to formulate service flexibility in ST planning. In the MILP model, vehicles with flexible services as well as fixed services are both considered, and vehicle routes and request routes are planned simultaneously. Due to the computational complexity, an Adaptive Large Neighborhood Search heuristic is designed to solve the problem. Several customized operators are designed based on the characteristics of the studied problem. The proposed model is compared with the models developed in a highly-cited paper and a newly published paper that do not consider service flexibility. Case studies on small instances verified that the proposed model with flexibility performs better on all scenarios, including scenarios with different weights for the individual objectives, scenarios under congestion,and dynamic optimization scenarios. On large instances (up to 1600 shipment requests), the proposed model with flexibility reduces the cost by 14% on average compared with the existing models in the literature.

Anticipatory scheduling of synchromodal transport using approximate dynamic programming

AbstractWe study the problem of scheduling container transport in synchromodal networks considering stochastic demand. In synchromodal networks, the transportation modes can be selected dynamically given the actual circumstances and performance is measured over the entire network and over time. We model this problem as a Markov Decision Process and propose a heuristic solution based on Approximate Dynamic Programming (ADP). Due to the multi-period nature of the problem, the one-step look-ahead perspective of the traditional approximate value-iteration approach can make the heuristic flounder and end in a local-optimum. To tackle this, we study the inclusion of Bayesian exploration using the Value of Perfect Information (VPI). In a series of numerical experiments, we show how VPI significantly improves a traditional ADP algorithm. Furthermore, we show how our proposed ADP–VPI combination achieves significant gains over common practice heuristics.

Global synchromodal shipment matching problem with dynamic and stochastic travel times: a reinforcement learning approach

Global synchromodal transportation involves the movement of container shipments between inland terminals located in different continents using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of accepted shipments with services in an integrated global synchromodal transport network with dynamic and stochastic travel times. The travel times of services are unknown and revealed dynamically during the execution of transport plans, but the stochastic information of travel times are assumed available. Matching decisions can be updated before shipments arrive at their destination terminals. The objective of the problem is to maximize the total profits that are expressed in terms of a combination of revenues, travel costs, transfer costs, storage costs, delay costs, and carbon tax over a given planning horizon. We propose a sequential decision process model to describe the problem. In order to address the curse of dimensionality, we develop a reinforcement learning approach to learn the value of matching a shipment with a service through simulations. Specifically, we adopt the Q-learning algorithm to update value function estimations and use the $$\epsilon $$ -greedy strategy to balance exploitation and exploration. Online decisions are created based on the estimated value functions. The performance of the reinforcement learning approach is evaluated in comparison to a myopic approach that does not consider uncertainties and a stochastic approach that sets chance constraints on feasible transshipment under a rolling horizon framework.

Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Decision-makers in synchromodal transport (ST) have different preferences toward different objectives, such as cost, time, and emissions. To solve the conflicts among objectives and obtain preferred solutions, a preference-based multi-objective optimization model is developed. In ST, containers need to be transferred across modes, therefore the optimization problem is formulated as a pickup and delivery problem with transshipment. The preferences of decision-makers are usually expressed in linguistic terms, so weight intervals, that is, minimum and maximum weights, are assigned to objectives to represent such vague preferences. An adaptive large neighborhood search is developed and used to obtain non-dominated solutions to construct the Pareto frontier. Moreover, synchronization is an important feature of ST and it makes available resources fully utilized. Therefore, four synchronization cases are identified and studied to make outgoing vehicles cooperate with changes of incoming vehicles’ schedules at transshipment terminals. Case studies in the Rhine-Alpine corridor are designed and the results show that the proposed approach provides non-dominated solutions which are in line with preferences. Moreover, the mode share under different preferences is analyzed, which signals that different sustainability policies in transportation will influence the mode share.

Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Global synchromodal transport with dynamic and stochastic shipment matching

This paper investigates a dynamic and stochastic shipment matching problem, in which a platform aims to provide online decisions on accepting or rejecting newly received shipment requests and decisions on shipment-to-service matches in global synchromodal transportation. The problem is considered dynamic since the platform receives requests and travel times continuously in real time. The problem is considered stochastic since the information of requests and travel times is not known with certainty. To solve the problem, we develop a rolling horizon framework to handle dynamic events, a hybrid stochastic approach to address uncertainties, and a preprocessing-based heuristic algorithm to generate timely solutions at each decision epoch. The experimental results indicate that for instances with above 50% degrees of dynamism, the hybrid stochastic approach that considers shipment request and travel time uncertainties simultaneously outperforms the approaches that do not consider any uncertainty or just consider one type of uncertainties in terms of total profits, the number of infeasible transshipments, and delay in deliveries.

Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions

Long-haul continental freight flows still heavily rely on unimodal road transport. Intermodal transport, combining road transport with other transport modes, has the potential to have lower operating costs and to be more environmentally sustainable. However, road transport benefits from its better flexibility and adaptability to sudden disruptions and uncertainties. To facilitate a modal shift towards intermodal transport, it is crucial to improve its resilience (i.e., capability to resist and recover from sudden disruptions). Synchromodality is an extension of intermodality in which decisions on modal choice and routing are not predefined long in advance but are taken based on real-time information and may provide a step in that direction. The conducted literature review investigates how uncertainty can be handled in intermodal and synchromodal freight transport networks. The literature is classified based on the planning level, which is either strategic, tactical or operational. The main focus is on the studied types of uncertainty and the proposed solution approaches. This work contributes to the research field by reviewing the literature on intermodal and synchromodal transport with uncertainty, presenting measures to mitigate the effects of uncertainty and proposing future research directions.

Physical Internet: First results and next challenges

Physical Internet: First results and next challenges

Model predictive control for simultaneous planning of container and vehicle routes

Transport of containers on a-modal bookings enables transport suppliers to route the containers in accordance with the current state of the synchromodal transport network. At the same time, it enables the transport providers to route their vehicles in real time based on the current need for transportation. The interdependency of the routes of containers and of vehicles has not yet been discussed explicitly in the synchromodal literature. This paper presents a model predictive controller that determines which combination of trucks, trains, and ships to use for transporting the containers and what routes empty and full trucks should use as one integrated problem. The impacts of this integrated problem as opposed to only considering the routes of the containers are shown with experiments on a simulated synchromodal hinterland network performed with both the proposed method and with a method that solely routes the containers. The results indicate an improved vehicle utilization. Furthermore, the integrated problem approach allows for more realistic constraints and costs.

Investigation and evaluation of main indicators impacting synchromodality using ARTIW and AHP methods

The East-West Transport Corridor (EWTC) in the Southern part of the Baltic Sea Region (BSR) has been chosen as a practical area for implementation of a novel transportation concept called synchromodality. The main expectations in developing synchromodal transport system are related to the improvement of the transport service level, modal shift and capacities utilization of transport hubs along this corridor. However, in the absence of identification of major factors influencing synchromodal system it is difficult to evaluate a potential benefit of implementation of this new transport concept. Our main goal in this paper was, on the basis of a review and using a specific questionnaire, to determine the main indicators impacting effectiveness of the synchromodal system. Compatibility of experts’ opinions was verified by using Multiple-Criteria Decision-Making (MCDM) method. The paper will be useful for transport and logistics companies interested in moving towards synchromodal transport, as well as for future researchers. In the course of the research – using the Average Rank Transformation Into Weight (ARTIW) and Analytic Hierarchy Process (AHP) methods – it was determined that the normalized subjective weights of the five main criteria impacting synchromodality distributed in the following order: service quality, efficiency, infrastructure sufficiency, technical properties of terminals and interaction of technologies. In accordance with the Kendall’s concordance coefficient and consistency ratio values, expert opinions are consistent, which enabled the rankings of expert group averages and the eigenvector to take as a result of the task solution. The outcomes of the research presented in the paper have shown that service quality (transport time, service and waiting time, handling time, working hours, reliability and flexibility) is the most important indicator (criterion) impacting synchromodality. At the same time it is a big challenge for transport researchers because it requires to design and apply a qualitatively new mathematical models facilitating an establishment of an optimal synchromodal network and services along chosen transport corridor.

Synchromodality in the Physical Internet \u2013 dual sourcing and real-time switching between transport modes

Synchromodality, also referred to as “synchronized intermodality”, employs multiple transport modes in a flexible, dynamic way in order to induce a modal shift towards more environmentally friendly transport modes like rail or inland waterways, without compromising on responsiveness and quality of service. It is characterized by the synchronized parallel usage of different transport modes and/or the ability to switch freely between transport modes at particular times while a consignment is in transit. We present a decision rule that can integrate both the parallel usage, as well as real-time switching of transport modes, either in combination or separately. It takes into account real-time stock levels and service requirements of the shipper. The policy first determines at the source which volumes will be shipped using each mode of transport, and subsequently depicts whether it should switch modes at an intermediate terminal. Using a simulation study we demonstrate how our synchromodal transport policy can induce a modal shift towards low carbon transport modes.

Should I Stay or Should I Go? Assessing Intermodal and Synchromodal Resilience from a Decentralized Perspective

Synchromodal transport incorporates real-time events in a dynamic manner in order to facilitate the most suitable selection of modes, routes and handling points. Up until now, current assessments rely on analytical models. Most of these models average distances for barges and trains via route mapping platforms that provide realistic distances for road only. To reflect on real-world developments more accurately, new thinking and modelling approaches are necessary to bridge academic models with physical transport processes. This paper introduces a computational model which computes movements of agents in geographically referenced space. The model captures stochastic parallel processes for each mode, and simulates decentralized delivery performance of each order in terms of cost, time and emissions at an operational level. Furthermore, we study the routing of individual orders and their responsiveness to disruptions. Computational experiments are performed within a case study which concerns imports of retail goods by unimodal truck transport from France to Belgium. Our findings show that dynamic synchromodal solutions cope with disturbances better, but unnecessary deviations and pro-activeness can also lead to negative effects when compared to static intermodal solutions