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We present a normative model for simulating freight flows of multiple products on a multimodal network. The multimodal aspects of the transportation system considered are accounted for in the network representation chosen. The multiproduct aspects of the model are exploited in the solution procedure, which is a Gauss-Seidel-Linear Approximation Alg...
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... During early years, national freight transport models were typically established as network design models. Specific examples include STAN (Guelat et al., 1990;Crainic et al., 1990a), NODUS (Jourquin and Beuthe, 1996) and TLSS (Arnold et al., 2004). In this approach, the freight transport system is modeled ''bottom-up'' as a graph. ...
... Operational costs are computed by assigning transport demand between node pairs to specific modes and routes within the graph. Examples of these models have been used in different countries, including Brazil, Sweden and Norway (Guelat et al., 1990;De Jong and Johnson, 2009). ...
... Other directions for future work include improving model accuracy-for example, by adopting a multi-stage approach with more than two stages or incorporating new elements, such as vessel retrofitting. Additionally, enhancing computational performance by developing tailored solution methods, such as decomposition (e.g., by product groups (Guelat et al., 1990), mode-fuel combinations, or uncertainty scenarios) or heuristic techniques, is a promising avenue. ...
... With the diversified development of transportation modes, multimodal transport in logistics has become one of the hot research spots. In [22], a multimodal hub network was represented by a graph where the nodes represent the demand and supply points and the arcs represent the transportation links between the nodes. As stated in [23], the growing importance of multimodal transport necessitated modeling and solving load planning problems by taking into account various complex decisions simultaneously. ...
... After generating the offspring, merge the parents and offspring as P merge , compute the Rank id and crowding distance CD id of P merge , select individuals for the next generation based on Rank id and CD id , and update the Rank ui and CD ui of the newest P c . if 0 ≤ r < θ 1 then 8 Choose candidate parents P candi by K-tournament selection based on Pareto rank Rank id and crowding distance CD id 9 Apply DE generate operators on P candi to generate offspring population as P o 10 else if θ 1 ≤ r < θ 2 then 11 Choose candidate parents P candi1 by K-tournament selection based on Pareto rank Rank ui and crowding distance CD ui 12 Choose candidate parents P candi2 by K-tournament selection based on Pareto rank Rank id and crowding distance CD id 13 Apply GA generate operators on P candi1 to generate offspring population, denoted as P o1 14 Apply DE generate operators on P candi1 and P candi2 to generate offspring population, denoted as P o2 15 Merge P o1 and P o2 as P o 16 else 17 Choose candidate parents P candi by K-tournament selection based on Pareto rank Rank ui and crowding distance CD ui 18 Apply DE generate operators on P candi to generate offspring population as P o 19 end 20 Evaluate the values f ui and f cons of P o 21 Merge P c and P o as P merge 22 Update the Pareto rank Rank id and crowding distance CD id on P merge 23 Environmental selection according to Rank id and CD id as the next generation P c 24 Update Rank ui and CD ui of the newest P c 25 end ...
Clustering islands located close to each other and sharing some common characteristics offer diverse and unique opportunities for tourism, trade, and research, and especially take a crucial part in the military. Remote from inland, islands have relatively limited resources, which makes them dependent on imported energy sources such as oil and gas or renewable energy. However, there are few studies about the energy security of clustering islands. To this end, this study proposes a novel energy optimization framework that aims to optimize the use of their different types of energy among clustering islands and improve the stability of the whole energy internet via a multilayer transportation network. The transportation network also enables islands to serve as emergency power sources for each other in some emergency situations. Specifically, we construct an assignment model that considers multimodal transport, multiobjective, and multiple constraints. To address this issue, we develop an unconstrained-individuals guiding constrained multiobjective optimization algorithm, named uiCMOA. Experimental results demonstrate the effectiveness of the transportation network and the efficiency of the proposed algorithm.
... As an example of these advances, specialized models are presented in the literature proposingamong others -the use of nonlinear transport cost functions sensitive to economies of scale, a multimodal transportation network with capacity constraints, delay functions reproducing the effect of congestions, and the traffic of empty or specialized vehicles, allowing a more detailed and realistic representation of transportation systems (Branco et al., 2020;Branco et al., 2019;Caixeta-Filho & Macaulay, 1989;Crainic et al., 1990;Crainic & Laporte, 1997;De La Cruz et al., 2010;Gédéon et al., 1993;Guélat et al., 1990;Labys & Yang, 1991). ...
Climate change is expected to affect the most diverse regions of the world in diverse ways, posing additional challenges to managers and populations in the countryside and in the cities. In this chapter, we adopt climate anomaly scenarios considering the variables such as maximum temperature, consecutive days of rain, and number of dry days, to select municipalities in the Brazilian Amazon that are likely to face great climate changes in the region. We then analyzed socioeconomic data, producing clusters for groups of municipalities based on the neural network self-organizing maps. Our findings reveal that an analysis of the cities from a nexus perspective shows the impact of climate change in urban development and, at the same time, urban development impacts on the natural resources. The results depict Brazilian Amazon municipalities’ vulnerability – they have the lowest level of basic sanitation, waste management, adequate storm drainage, and human development index that makes their population particularly vulnerable to face the climate crisis. Furthermore, impacts can be particularly disastrous for 30 Amazonian municipalities by their critical condition due to climate change and their socioeconomic and water demand index. Our results can be useful for managers of municipalities that may reach critical states due to climate change and serve as an alert to the urgency of adaptation and management strategies.
... (Shimizu et al., 2011) designed a stochastic rule-based resource optimisation model for global freight network optimisation scenarios under uncertainty. (Guelat et al., 1990) constructed a study based on the basic model of multi-commodity flow operation in intermodal network and designed Gauss-Seidel linear approximation algorithm to solve the shortest path with intermodal switching cost. (Friesz et al., 1986) constructed a network optimisation model for shippers and transport companies based on the decision-making process of shippers selecting transport companies and transport companies formulating transport plans, taking into account the maximisation of the respective interests of shippers and transport companies, aiming at predicting the state of the entire freight transport network in a more comprehensive way. ...
The new western land and sea corridor makes use of a variety of transport modes to reach major ASEAN countries such as Singapore to the south, connects Southeast Asia and North America to the east, and connects Chongqing, Lanzhou and Xinjiang to the north with the China-European Union (CEU) liner train, which is a composite opening-up corridor in the western region for realising the regional linkage and international co-operation with ASEAN and other countries, and organically connecting with the "One Belt, One Road". In order to explore the regional freight network of Gan-Qing-Ning region of the new western land and sea corridor, we establish the model of node socio-economic attractiveness, topological charisma and comprehensive utility maximization, and analyze the index factors by using entropy weighting, Delphi and comprehensive evaluation methods through the Matlab Genetic Algorithm Toolbox to study the influence of node socio-economic attractiveness, topological charisma, node freight volume, logistics and transportation costs and construction costs on the utility of nodes of the new western land and sea corridor. The influence of the regional node utility of Gansu, Qinghai and Ningxia in the corridor. Analyze the problems and factors affecting the selection of nodes in the existing freight transport corridors in the three provinces. Completing the site selection of Gan-Qing-Ning regional node of the new western land and sea corridor and combining the import and export cargo volume of the three provinces to propose the construction of the South-Middle East three-lane freight corridor, and completing the optimization plan of the freight network. The results show that: scientific optimization and improvement of Gan-Qing-Ning regional freight network can promote the construction of freight network in the northwest region of the new western land and sea corridor, to meet the demand for freight transportation in the corridor, and to promote the development of the corridor industry and economic growth. Thus, it provides reference for the development of Gansu, Qinghai and Ningxia region and freight network optimization.
... For this to work, a so-called "virtual network" with ties to other multimodal chain operations must be established. The concept of virtual networks in multimodal freight transportation models was first introduced in the early 1990s [10,23]. A process for generating virtual connections in a Geographic Information System, or GIS, automatically is presented by Joaquin et al. [26].The graphical representation of a global network for multidisciplinary and international freight transit is the subject of the researchers South worth and Peterson [35]. ...
This paper examines an integrated networking shipping issue with solutions for cargo aggregation. A goods forwarder can combine planned and flexible-time delivery services. One major feature of the issue is time constraints. They are used, for example, to simulate shipping and picking-up times. The several aspects of the issue can be characterized as digraph components, and combining them results in a comprehensive graph description. This allows for a quantitative multi-commodity flow formulation with origin-destination and time structures, side constraints, and non-convex piece-wise quadratic costs. Techniques for creating columns are intended to calculate lower bounds. These algorithms for creating columns are also integrated with heuristics that seek to identify feasible integer answers. When computational outcomes are presented using actual data, the effectiveness of the suggested method is demonstrated. This work is motivated by the work of L. Moccia, et al [35]. INTRODUCTION This article examines an international transport issue involving flexible scheduling and regular services that arose during the running of an Italian shipping organization. Some of the problem's properties are case-specific, but others are generalizable and applicable in a wide range of situations. We first go over the real-world application, then the general issue. Serving a large client that needs supplies from facilities regarding the carrier, getting from distributor systems is a genuine problem from those in northwestern France to those who live in the nation's center and southern regions. A commodities forwarder, who provides logistical services, coordinates the movement of merchandise from their manufacturing location to their final location point by purchasing carrier capacities and allocating deliveries to these companies. Transportation demands are always fulfilled, and an evolving two-week timeframe is part of the operational strategy. Numerous characteristics, such as the origin, Size, weight, kind of items, transportation and collection periods, are what define a transportation request. These opening times can be varied; for example, an automobile application could be picked up on the first day of the schedule, approximately 7 to 10 AM, or on day two, in the interval of 7 to 10 AM. In a similar vein, the delivery has several periods. The railway company owns the goods forwarder. The goods the carrier is cognizant of the yearly quantities and variances of client demands since they terminate with locomotives for the long-term arrangements of specified complete trains at a given regular date. The definition of a full train includes the day and time of departing, the itinerary, the number of stops, the maximum length, the weight capability, the permitted cargo, and the cost. The goods carrier pays the deposit to the railway business irrespective of whether or not the train is used, and the remaining amount is used to activate the train. This is the entire amount paid for a complete railway to the equipment company. The cost of reactivation is not affected by volume and is determined by the entire train's attributes, including its capacity, permitted products, number of stops, timetable, and distance traveled. We do not discuss the long-term arrangement involving the goods the carrier and the
... The majority of researchers used mathematical programs that seek to minimize the cost of freight flows under normal conditions. These studies focused on freight flows in intermodal/multimodal networks [3,8,9], freight routing in rail [10] and intermodal networks [11], and optimally locating intermodal terminals [12] or hub locations in a capacitated network [13]. Besides costminimization models, a few models seek to minimize the travel time of freight flow. ...
... The majority of mathematical programs with or without consideration of disruption used either exact or heuristic methods as their solution approach. Apart from these two methods, Evans algorithm [8], Gauss-Seidal Linear Approximation [9], Diagonalization [19], modified convex combination [2], and modified gradient projection algorithms [3] are used in solving assignment models under normal conditions. On the other hand, models under disruption consideration used improved Depth First Search [29], and Sample Average Approximation [23] for routing; Benders Decomposition, column generation, Monte Carlo simulation [27], and Integer Lshaped [30] algorithm for resilience models. ...
This paper presents a methodology for freight traffic assignment in a large-scale road-rail intermodal network under uncertainty. Network uncertainties caused by natural disasters have dramatically increased in recent years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River Flooding, and Hurricane Harvey) severely disrupted the U.S. freight transportation network, and consequently, the supply chain. To account for these network uncertainties, a stochastic freight traffic assignment model is formulated. An algorithmic framework, involving the sample average approximation and gradient projection algorithm, is proposed to solve this challenging problem. The developed methodology is tested on the U.S. intermodal network with freight flow data from the Freight Analysis Framework. The experiments consider three types of natural disasters that have different risks and impacts on transportation networks: earthquakes, hurricanes, and floods. It is found that for all disaster scenarios, freight ton-miles are higher compared to the base case without uncertainty. The increase in freight ton-miles is the highest under the flooding scenario; this is because there are more states in the flood-risk areas, and they are scattered throughout the U.S.
... With STraM, we aim to help fill this gap by developing a strategic national freight transport model that explicitly accounts for some of the difficulties arising in strategic planning. To do so, we take inspiration from the multimodal transport network design modeling framework STAN of Guélat et al. (1990); Crainic et al. (1990a). The main strategic decision variables in this framework relate to investments in the infrastructure from a system perspective, while the operation of the network is modeled on an aggregate scale to assess the feasibility and performance of the infrastructure investments (Crainic et al., 2021). ...
... The demand for transport is assumed to be exogenously given, while the model determines what modes (and fuels) are used to transport each demand from its origin to its destination. Specifically, STraM is a multimodal freight transport model (Guélat et al., 1990). ...
To achieve carbon emission targets worldwide, decarbonization of the freight transport sector will be an important factor. To this end, national governments must make plans that facilitate this transition. National freight transport models are a useful tool to assess what the effects of various policies and investments may be. The state of the art consists of very detailed, static models. While useful for short-term policy assessment, these models are less suitable for the long-term planning necessary to facilitate the transition to low-carbon transportation in the upcoming decades. In this paper, we fill this gap by developing a framework for strategic national freight transport modeling, which we call STraM, and which can be characterized as a multi-period stochastic network design model, based on a multimodal freight transport formulation. In STraM, we explicitly include several aspects that are lacking in state-of-the art national freight transport models: the dynamic nature of long-term planning, as well as new, low-carbon fuel technologies and long-term uncertainties in the development of these technologies. We illustrate our model using a case study of Norway and discuss the resulting insights. In particular, we demonstrate the relevance of modeling multiple time periods, the importance of including long-term uncertainty in technology development, and the efficacy of carbon pricing.
... The multidimensional travel choice situation is transformed into the one-dimensional choice situation of alternative routes within the network-transshipment link proposed by Tavasszy [30], with value of cost and delay as the attribute. Guelat et al. [31] represent a more certain transfer link by adding more links in the multimodal terminal. Detailed representation of transshipment link is also proposed by Southworth and Peterson [32]. ...
... As an example of these advances, specialized models are presented in the literature proposingamong others -the use of nonlinear transport cost functions sensitive to economies of scale, a multimodal transportation network with capacity constraints, delay functions reproducing the effect of congestions, and the traffic of empty or specialized vehicles, allowing a more detailed and realistic representation of transportation systems (Branco et al., 2020;Branco et al., 2019;Caixeta-Filho & Macaulay, 1989;Crainic et al., 1990;Crainic & Laporte, 1997;De La Cruz et al., 2010;Gédéon et al., 1993;Guélat et al., 1990;Labys & Yang, 1991). ...
This book aims to contribute to the transdisciplinary study of the water-energy-food (WEF) nexus in cities and to help policy makers adopt a more integrated approach to natural resources management in urban environments to face the challenges and threats of climate change. This approach is based on a multidimensional scientific framework that seeks to understand the complex and non-linear interrelationships and interdependencies between water-energy-food under climate change and to generate solutions to reduce trade-offs among development goals and generate co-benefits that help encourage sustainable development and contribute to the achievement of SDGs, mainly SDG 11 (make cities and human settlements inclusive, safe, resilient and sustainable) and SDG 13 (take urgent action to combat climate change and its impacts).Governing the WEF nexus in cities is one of the greatest resource challenges of our time, as cities consume large amounts of WEF, but one that can also generate relevant alternatives with which to tackle climate change. To help fostering these alternatives, this book analyzes the governance, institutional and political economy factors that determine the effectiveness of the nexus approach and reviews the potential, the benefits and the policy implications of the adoption of the WEF nexus approach at the urban level. Through a series of hands-on cases, chapters in this book present the opportunities of the WEF nexus approach to achieve innovation and transformative change and discuss concrete areas of synergy and policy initiative to raise urban resilience.
Water-Energy-Food Nexus and Climate Change in Cities will serve both as a guide for policy makers as well as a useful resource for students and researchers in fields such as urban studies, public health, environmental sciences, energy studies and public policy interested in learning how cities can represent possibilities to navigate and manage sustainability from local to global.
... As an example of these advances, specialized models are presented in the literature proposingamong others -the use of nonlinear transport cost functions sensitive to economies of scale, a multimodal transportation network with capacity constraints, delay functions reproducing the effect of congestions, and the traffic of empty or specialized vehicles, allowing a more detailed and realistic representation of transportation systems (Branco et al., 2020;Branco et al., 2019;Caixeta-Filho & Macaulay, 1989;Crainic et al., 1990;Crainic & Laporte, 1997;De La Cruz et al., 2010;Gédéon et al., 1993;Guélat et al., 1990;Labys & Yang, 1991). ...
Cities are dependent on hinterlands – whether local or global – for water, energy, and food (WEF) to sustain urban activities. With the projected growth of urban population and consumption, the demand for natural resources tends to increase. Moreover, climate change will potentially increase the insecurity of the availability of WEF in cities. Decision-makers in cities are often faced with the very challenging issue of resource management due to scarcity of resources that generates conflicts among stakeholders. Therefore, the risks associated with rapid urbanization and climate change have highlighted the need to reconfigure the development of cities to optimize and reduce the use of resources in order to achieve the Sustainable Development Goals (SDGs). Nevertheless, various approaches have been developed in the last decades to improve the WEFN. Thus, this chapter presents challenges and opportunities for improving the governance of cities over WEF systems and the nexus among them. Using the WEF nexus framework, cities would benefit from a transition toward a circular economy that uses renewable resources and designs cyclical and efficient systems. This would encourage innovative responses and effective partnerships toward smarter cities able to tackle climate change.
