Total Network Cost Research Articles

Resilient and sustainable global COVID-19 vaccine supply chain design considering reverse logistics

This study proposes a multi-mode, multi-product, and multi-objective optimization model to design a resilient and sustainable global COVID-19 vaccine supply chain network under operational risks. The network includes a forward chain for vaccine supply, production, and distribution and a reverse chain for vaccine waste management. The objective functions comprise minimizing the total network costs and environmental pollution caused by the waste of vaccination centers and the transportation between facilities, as well as considering social impacts (maximizing regional development and the effect of domestic vaccine production on self-sufficiency, and minimizing the impact of vaccine side-effects). Resilience strategies are employed to deal with the disruption risks in different facilities and transportation links of the chain, including multiple sources of raw material supply, vaccine production and distribution, and waste management facilities; import; multiple transportation modes and different vehicles in each mode; inventory holding; capacity expansion; transshipment; and fortification of facilities. Financial parameters, including exchange rate and international transport insurance for importing raw materials and vaccines, are also considered in the presented model. An actual-world case study in Iran is conducted to indicate the model's applicability. Finally, by analyzing the computational results, important research implications are presented. The analyses investigate the conflict of objectives, examine the impact of vaccine side-effects on production and import, assess the efficiency of resilience strategies in the face of disruptions, demonstrate the effect of disruptions on network components, and determine suppliers' share for the supply of raw materials.

Collaborative optimization design for district distributed energy system based on energy station and pipeline network interactions

District distributed energy systems (DDESs) are widely used worldwide due to their environmentally-friendly and energy-saving characteristics. The strong correlation and coupling of energy stations and pipeline networks lead to difficulties in the collaborative optimization design of the DDES. To minimize the total annual cost of the system, this research proposed a collaborative optimization model to realize the integrated design of the DDES. The energy distance method is combined with the K-means cluster method to solve the problem of locating and sizing energy stations. The pipelines planning algorithm based on “Dijkstra algorithm (DA) + genetic algorithm (GA)” is used to optimize the pipeline layout and diameter simultaneously. The improved DA method continuously updates the cost full adjacency matrix and pipe diameter matrix of each pipe segment by optimizing the access sequence of user nodes, and finally obtains the optimal layout and pipe diameter of the pipe network at the same time. Moreover, this paper reveals the influence factors that should be considered in the planning of DDES, such as the number of energy station and flow velocity. The results indicate that compared to traditional optimization processes, the collaborative method proposed in this paper reduced the total annual cost of the pipeline network by 20.5 %. The improved DA method solves the problem of pipeline sharing while preventing the system from falling into local optima. Moreover, optimizing the number of energy stations and flow velocity can reduce annual cost of pipelines by 0–14 % and 0–20 %, respectively. This study provides theoretical guidance and technical support for researchers in the planning and designing of DDES.

Providing an integrated multi-depot vehicle routing problem model with simultaneous pickup and delivery and package layout under uncertainty with fuzzy-robust box optimization method

This paper modeled and solved an integrated multi-depot vehicle routing problem (MDVRP) with simultaneous pickup and delivery (SPD) with package layout under unpredictable pickup, delivery, and transfer costs. The model described in this paper is divided into two stages. In the first stage, the SCA algorithm is used to optimize the package dimensions (a collection of commodities consumers need). The NSGA II and MOALO algorithms are used in the second stage to optimize the three objective functions of 1 simultaneously) minimizing total costs, 2) minimizing co2 emissions, and 3) minimizing the maximum working hours of drivers based on the optimal dimensions (length, width, and height) obtained from solving the first stage model. Determining the quantity and ideal location of possible warehouses, the best route for trucks to take to deliver and collect customer items, and the distribution of customers to warehouses are the key goals of the second stage. Since the model is unclear, the problem's uncertainty parameters are controlled using a novel fuzzy-robust box optimization (FRBO) technique. This technique, which combines the advantages of fuzzy programming with robust box-based optimization, produces excellent results when used to optimize objective functions. The numerical calculations in the numerical example show that the total network costs and CO2 emissions increased in the second stage in the presented model with an increasing uncertainty rate. At the same time, the maximum working hours of drivers decreased due to the shortened communication route and the number of vehicles increasing. Finally, the MOALO algorithm was used to resolve a case study at Safir Broadcasting Company because of its excellent efficiency in resolving the created model, the findings of which revealed the presence of 13 potential effective solutions. The quantity of greenhouse gas emissions rose by 1.11%, the overall expenditures climbed by 1.72%, and the number of hours that drivers worked fell by 11.98% when the uncertainty rate was raised from 0.5 to 0.7, according to research on the FRBO.

Research on Distributed Energy Storage Planning-Scheduling Strategy of Regional Power Grid Considering Demand Response

Distributed energy storage and demand response technology are considered important means to promote new energy consumption, which has the advantages of peak regulation, balance, and flexibility. Firstly, this paper introduces the carbon trading market and the new energy abandonment penalty mechanism. Taking the energy storage cost, distribution network operation cost, network loss cost, carbon transaction cost, and new energy abandonment cost as the objective functions, the distributed energy storage optimization allocation model is established. Considering the scheduling problems of various units in the system, the operation strategy of distributed energy storage and demand response linkage is proposed. The improved bat algorithm is used to solve the problem. The example analysis shows that under the combined action of energy storage and demand response, the annual total cost of the distribution network is effectively reduced, the consumption rate of new energy is improved, and the voltage quality and network loss of the power grid are improved to ensure the reliability and stability of the distribution network.

Optimal AC/DC Distribution Systems Expansion Planning From DSO's Perspective Considering Topological Constraints

As the integration of DC distributed resources increases in recent years, hybrid AC/DC topology emerges as a promising alternative in the planning of distribution systems. This paper presents an AC/DC optimal expansion planning of distribution networks from Distribution System Operator's perspective with a special focus on grid architecture through Integer Programming-based topological constraints. The proposed formulation aims to determine the optimal location and sizing of converters and to select lines type (AC or DC) and state (closed or open). The problem is formulated as a mixed integer second-order conic programming model that guarantees global optimality and verifiable exactness by using off-the-shelf solvers. A multi-scenario approach with a Wasserstein distance-based scenario reduction is applied to deal with resource uncertainty. Case studies based on a 13 bus and IEEE 33 bus distribution networks were used to verify the effectiveness of the model. The results show that a hybrid architecture can improve the efficiency of the grid through optimal active power flow and reduce the total network costs.

Robust multimodal hub network design for hazmat considering a detour strategy

Most traditional hub networks focus on ordinary cargo transportation and usually limit the number of transshipment occurrences to minimise costs. However, this limitation is not conducive to the transportation of hazmat, which often requires detours to avoid high risks. Therefore, this paper proposes a new multimodal network model considering a detour strategy and discusses the uncertainty in the network from the demand point of view. The method of robust optimisation is used to address the uncertainty in the proposed model, and the Lagrange relaxation algorithm and augmented ɛ-constraint method are used to transform and simplify the model. In addition, simulated data of hazmat transportation in the Yangtze River Delta urban agglomeration are used for a case study. The results show that under the consideration of uncertain demand, changes in the demand uncertainty degree and transit discount factor cause changes in the total cost and risk of the multimodal hub network, thus affecting the carrier’s location and route decisions; the new model can better control and mitigate risk, which is more suitable for hazmat transportation. This study provides a reference for multimodal transport carriers in hazmat transportation decision making.

Designing a resilient and green coal supply chain network under facility disruption and demand volatility

Few studies have addressed strategic-level disruptions and operational-level risks for the coal industry in the literature on mining supply chain management. In many developing countries, the coal industry plays a leading role in electric power generation and primarily contributes to carbon emissions. This study aims to design a resilient and green coal supply chain network (RG-CSCN) considering strategic-level disruptions, operational-level risks, and carbon emissions. A practical stochastic mixed-integer programming model is developed to optimise both economic and environmental objectives (i.e., the total network cost and the total carbon emissions cost). Our proposed model involves five resilience strategies: facility fortification, reserving emergency inventory, direct-to-port delivery, establishing reliable distribution centres, and multiple transportation routes. Based on real-world data from the coal industry, we conduct extensive computational experiments to evaluate the impacts of strategic-level disruptions and operational-level risks and to validate the applicability of the proposed five resilience strategies and their synergistic performance in coal supply chain management. Moreover, sensitivity analyses are carried out to examine the influences of different customer service levels and carbon tax policies in various scenarios.

A multi-objective solution framework for the assembly inventory routing problem considering supply risk and carbon offset policies

This study aims to provide novel insights into the multi-objective optimisation of an inbound assembly inventory routing problem (IRP) by considering customer demand uncertainty, stochastic supply failure risks, and a carbon offset policy. An inbound IRP network generally comprises several geographically dispersed suppliers, vehicle depots, and assembly plants. In this study, a new mixed-integer nonlinear optimisation model is formulated considering two objective functions: (i) minimising the total supply network cost and (ii) maximising a low-risk supplier priority function with practical constraints. A hybrid non-dominated sorting genetic algorithm-II was developed to obtain near-optimal Pareto solutions for various small, medium, and large IRP instances. The Pareto solutions were compared with the efficient solution obtained using the ε-constraint method. Finally, the best Pareto solution was obtained using the multi-criteria decision-making technique. The results indicate that implementing supply risk-mitigation strategies leads to higher overall supply network costs and carbon emissions. However, a sensitivity analysis based on factorial simulation experiments indicates that a supply risk-mitigation strategy is advantageous if component prices are low and supply capacity, fuel price, and demand variations are high. In addition, the lower the demand variation, the lower the expected overall network cost and carbon emissions.

Two-stage stochastic optimal scheduling for multi-microgrid networks with natural gas blending with hydrogen and low carbon incentive under uncertain envinronments

Multi-energy multi-microgrid (MMG) networks have the advantage of integrating various energy resources and promoting energy utilization efficiency. To further improve the economy and low-carbon operation of networks, this paper decarbonizes the multi-energy MMG network, introduces the hydrogen-natural gas blending technology, and establishes a stochastic planning model of a multi-energy MMG network considering natural gas blending with hydrogen and low-carbon incentive. Firstly, the hydrogen energy storage (HES) model is developed by blending hydrogen into natural gas and supplying it to the gas unit. Considering the incentive mechanism in the case of surplus carbon allowances, a stepped carbon trading mechanism with a variable incentive coefficient is designed. Secondly, to effectively and simply measure the sources and load uncertainties in the MMG network, Conditional Value-at-Risk (CVaR) is calculated by the interaction cost between the MMG and the distribution network. A two-stage stochastic planning model for the MMG network is established based on the Nash negotiation theory to minimize the total cost of the MMG network in the first stage, allocate the benefits, and maximize the benefits of each microgrid in the second stage. Finally, by dynamically correcting the penalty factor through the quantitative relationship between the original residual and the pairwise residual, an improved alternating direction multiplier method (ADMM) based on the dynamic adaptive penalty factor is constructed to realize the distributed solution of the scheduling model. Through the comparative analysis of different cases of the arithmetic system test, it is verified that the proposed model can maximize each microgrid's benefits while ensuring the MMG network's lowest cost, promoting renewable energy consumption, and reducing carbon emissions.

Dynamic deployment of energy symbiosis networks integrated with organic Rankine cycle systems

The amount of greenhouse gas (GHG) emissions from the energy sector is significant. Energy symbiosis (ES) is an innovative solution that helps reduce those emissions and lessens their role in worsening climate change. ES can enable the uptake of renewable energy sources at the industrial level, reduce reliance on fossil fuel, and help conserve materials. Organic Rankine Cycle (ORC) systems are ideal ES solutions for recovering wasted heat as they are compatible with different heat sources and offer high flexibility and compatibility. This study uses parametric optimization and performance analysis to examine the dynamic development of symbiotic networks with ORC facilities. The multi-objective mixed integer linear programming (MILP) model developed here maximizes energy recovery amounts in the network and minimizes the costs of establishing the required infrastructure. The MILP model can determine, for each period, the number of financial resources, the active connections between nodes (facilities), the amount of energy flowing through symbiosis, the opening/closing times of ORC facilities, and the ORC locations and capacities. The model has been tested in a case study using two scenarios: dynamic development of ES excluding and involving the ORC. The results show that integrating the ORC increases energy recovery by 18% while reducing the total network cost by about 7%.

Cost-Efficient Sharing Algorithms for DNN Model Serving in Mobile Edge Networks

With the fast growth of mobile edge computing (MEC), the deep neural network (DNN) has gained more opportunities in application to various mobile services. Given the tremendous number of learning parameters and large model size, the DNN model is often trained in cloud center and then dispatched to end devices for inference via edge network. Therefore, maximizing the cost-efficiency of learned model dispatch in the edge network would be a critical problem for the model serving in various application contexts. To reach this goal, in this paper we focus mainly on reducing the total model dispatch cost in the edge network while maintaining the efficiency of the model inference. We first study this problem in its off-line form as a baseline where a sequence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> requests can be pre-defined in advance and exploit dynamic programming techniques to obtain a fast optimal algorithm in time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(m^{2}n)$</tex-math></inline-formula> under a semi-homogeneous cost model in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -sized network. Then, we design and implement a 2.5-competitive algorithm for its online case with a provable lower bound of 2 for any deterministic online algorithm. We verify our results through careful algorithmic analysis and validate their actual performance via a trace-based study based on a public open international mobile network dataset.

The p-hub centre routing problem with emissions budget: Formulation and solution procedure

In recent years, the use of fossil fuels has led to a drastic increase in the emissions of CO2 and other greenhouse polluting gases. The transportation sector stands out as one of the main contributors to this pollution. Thus, several network design problems are being revisited to uncover cost and energy-efficient solutions. In this paper, we formulate and solve a p-hub centre routing problem under a CO2 emissions budget. The aim is to locate hub nodes, allocate client nodes in local tours of capacitated vehicles, and decide on vehicle speeds during transportation. The objective is to minimise the maximum time of service subject to a budget constraint on the total CO2 emissions cost of the hub network. To solve the introduced problem, we implement an efficient simulated annealing algorithm with a temperature-dependent penalty cost function, a fixed size prohibited solutions list, and a speed-based reparation heuristic. Additionally, we present a novel clustering-based construction heuristic to generate initial starting solutions for our algorithm, while hub location–allocation, vehicle routing, and speed optimisation operators are considered during the local search step. Extensive computational experiments on adapted AP data set instances show that the proposed solution approach outperforms a state-of-art solver in terms of CPU time and solution quality. Finally, we study the trade-off between the quality of service and the CO2 emission costs and discuss the effect of the CO2 budget on the design of hub networks.

Designing a new recycling network for post-demolition autoclaved aerated concrete (AAC) in Europe

Autoclaved aerated concrete (AAC) is a widely used building material for masonry blocks. Its porous structure and mineral composition lead to low thermal conductivity and fire resistance. European AAC production and usage strongly increased in the 1960s and 1970s. Therefore, assuming limited buildings’ lifetimes, significant post-demolition AAC volumes can be expected in the following decades. However, post-demolition AAC recycling in high-value environmentally friendly applications is still to be established as most post-demolition AAC is currently landfilled. Different recycling options for post-demolition AAC are presently being researched. However, a recycling network to implement these options is neither designed nor established. This contribution focuses on creating a European recycling network, including mathematical modelling, data acquisition, and solving the model. i.e. minimising the total costs. The mathematical modelling uses a capacitated warehouse location problem with multi-sourcing and direct delivery. Results show that recycling plants of smaller capacity (100,000 t input/a) are placed in the recycling networks in 2020 and 2025. With higher waste quantities being expected from 2030 onwards, plants with a larger capacity (200,000 t input/a) are added, especially in Poland, where the highest pd-AAC amount in Europe is expected. The recycling network shows a decentralised structure with numerous recycling plants to keep transport costs low. Most network costs result from variable processing costs, showing the highest cost increases from 2020 to 2050. Fixed costs increase with the higher number of recycling plants and account for the second-largest share of total network costs. Transport costs are comparatively low thanks to the decentralised structure of the network. Overall, waste generation is expected to increase by 226% from 2020 to 2050, while the total costs of the recycling network are expected to rise by 151% only. The results support decision-makers in fostering recycling and implementing a circular economy for post-demolition AAC.

Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty

In this paper, robust planning of hybrid photovoltaic/wind/battery storage (PV/WT/Battery) system is performed in the distribution network to minimize power losses cost, purchasing power cost from the hybrid system as well as purchasing power cost from the upstream network considering uncertainties of network demand and renewable generation using information gap decision theory (IGDT) based on risk aversion strategy (RAS). The decision variables are considered as the optimal installation location and capacity of the hybrid system components in the network as well as the uncertainty radius of the uncertain parameters, which is determined using the flow direction algorithm (FDA), which is inspired by the movement of surface water flow to the pond outlet. The planning problem is implemented with deterministic and robust-based IGDT approaches. The proposed methodology is performed on the IEEE 33- and 69-bus distribution networks. The deterministic results indicate that the optimal hybrid PV/WT/Battery system planning in the networks significantly reduces system costs by injecting the optimal renewable power into the network and the battery power reserve management. The better capability of the FDA in deterministic planning is confirmed compared to the particle swarm optimization (PSO), crow search algorithm (CSA), and moth-flame optimizer (MFO) in achieving lower system cost. Moreover, in the IGDT robust approach, the maximum uncertainty radius of the renewable generation and network load is determined for the different uncertainties budget. The IGDT effectiveness is proved compared with deterministic and Monte Carlo simulation (MCS) methods. The total cost of the 33-bus network in deterministic, IGDT, and MCS is obtained at $ 17978.18, $ 20996.19, and $ 26178.02, respectively, and for the 69-bus network are achieved at $ 16846.26, $ 20215.39, and $ 20597.84, respectively. A comparison of the results showed that the MCS strongly depends on the defined scenarios, is very time-consuming, and is also not able to provide a level of cost assurance, the deterministic method is not able to determine the effect of uncertainties on system planning, but the IGDT can easily determine the cost of the system based on changes in uncertainty parameters. Also, planning based on the IGDT has resulted in the achievement of a robust hybrid system against forecasting errors caused by uncertainties.

Methodology for integrating flexibility into realistic large-scale distribution network planning using Tabu search

A successful energy transition will be firmly based on the effective integration of distributed energy resources and on the integration of new flexibility providers into the energy system. To make this possible, a deep transformation in the design, operation and planning of power distribution systems is required. Currently, a lack of comprehensive planning tools capable of supporting operators in their investment plan options exists. As a result, reinforcements of conventional grid assets are common solutions put in place. This paper proposes a methodology to obtain cost-optimal distribution network expansion plans, by modelling a single-stage distribution network planning tool, using conventional assets as well as flexibility contracting from demand response. A Tabu Search metaheuristic has been implemented in order to solve the optimization problem. A case study based on a realistic large-scale city network model is presented, for a planning horizon of ten years with significant load growth due to electromobility penetration. Results show that, in the case study analysed, the use of load flexibility in combination with conventional reinforcements can reduce the total expansion network cost by about 7.5 %. Furthermore, a sensitivity analysis on the cost of flexibility contracting is undertaken. Remarkably, the methodology presented generalises to further alternative solutions by providing a straightforward financial benchmark between the latter and conventional grid expansion.

Determining an optimal sensor system for smart buildings with uncertain energy supply and demand

The increased smartness of buildings and new technology are expected to mitigate the impact of climate change through renewable energy usage, efficient energy management, and greenhouse gas emission reduction. Using internet of things, sensors, and data analysis, energy management can substantially optimize the use of energy and balance energy supply and demand. However, a distributed sensor system is complex and needs to be located well to make the system efficient. The present study proposes a continuous approximation approach to determine the number of sensors required and the location to set them up in a building as well as the smartness level of each sensor while minimizing the total network cost. We also conducted a numerical analysis to illustrate the model and obtain management insights. Interestingly, in our example, we found that the basement floor of a building should have more sensors installed than other floors.

Key physical topology features for optical backbone networks via a multilayer correlation analysis

A communication network is a multilayer network comprising various layered technologies, and the underlying physical topology is an important aspect that determines the upper bound of overall system performance, including total communication capacity, cost, and robustness. We expect that understanding the impact of the physical topology on system performance will lead to better optical communication network design in the future, and we thus focus on clarifying the relationship between physical topology features and system performance. There have been various studies on the relationship between topology features and overall network performance. For example, the average number of hops and the cluster coefficient are well known to change network properties in complex networks. From the perspective of optical communication networks, it is known that the algebraic connectivity and average path length are related to total network capacity, and these findings have been applied in physical topology design models. On the other hand, there have been no quantitative comparisons among various topology features, and the comprehensiveness of the population from which these features are extracted is insufficient. Hence, we have developed a multilayer (physical topology and layer 1) correlation analysis framework that enables a quantitative comparison of topology features. We use this framework to numerically examine the relationships between physical topology features and the total communication capacity, cost, and robustness of optical communication networks, including graph features (especially graph spectra) that have not been investigated. The results show that the Laplacian spectral radius and geodesic distance Laplacian spectral radius are strongly related to system performance, in addition to the conventional average number of hops, cluster coefficient, algebraic connectivity, and average path length. We confirm that these correlations hold for the different network sizes and spatial nonuniformity of real optical backbone networks in different countries and regions. The results show that the average path length and cluster coefficient, or the Laplacian spectral radius and geodesic distance Laplacian spectral radius, are important guidelines for physical topology design of real optical backbone networks.

Queueing-based formulation model in a public transit network considering energy storage technology and demand charges

Battery Electric Buses (BEBs) are quickly emerging as a viable option for dramatically lowering transportation-related emissions. Finding optimal locations of charging stations for BEBs can facilitate the development of electric transit networks that provide continuous services. Using fast charging along with Energy Storage (ES) charger technologies has also garnered attention to mitigate the peak power demand. A consensus regarding two types of chargers may result in more investment in the electric transit network, which resulted in considering installing fast chargers in designing an electric transit network. While this study aims to consider two types of fast- and ES-chargers, along with demand charges and waiting times at terminal stops simultaneously, to minimize the total costs of the electric transit network. The queuing model integrated with the Mixed-Integer Non-Linear Programming (MINLP) model is proposed to locate the optimal placements of charging stations in the electric transit network. To validate the model, a benchmark Mumford0 network is applied. Results indicated that the proposed charging location model could efficiently determine optimal locations of fast- and ES-chargers across the electric transit network. Also, the results confirmed that considering ES chargers could significantly save 16.6 % of the total costs of the Mumford0 transit network and reduce the total demand charge imposed on the network by the fast charger. More interestingly, by purchasing more chargers, transit agencies can decide on an acceptable waiting time that would be comfortable for BEBs arriving at terminal stops.

Integrated operation of gas and power system through the P2P market mechanism

AbstractPeer‐to‐peer (P2P) energy trading is a new technology for integrating distributed energy resources (DERs) into the power system. A P2P market allows direct energy trading between end‐users, enables local power and energy equilibrium and supports power grid operations. As a common DER, gas‐fired power plants are employed to deal with the intermittency of the power system due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas‐fired power plants, which makes the operation of these systems even more interdependent and cost‐effective. This paper proposes a market‐based two‐stage framework for the integrated operation of power and natural gas grids taking into account demand response and both network constraints. In the first stage (scheduling stage), the MINLP‐based optimisation approach is used for the optimal scheduling of two energy carriers considering AC power flow and gas hydraulic calculations for the next 24 h. Then, in the second stage, the continuous double auction (CDA)‐based P2P energy trading approach is used for enabling customers to trade energy with each other. To simulate human trader behaviour and maximises the benefits of customers, the authors considered the optimum bidding strategy through the zero intelligent plus trader model. The simulations executed on a 33‐bus power distribution grid and a 33‐node gas network indicate that the proposed framework can dramatically reduce the total operational cost and improve the performance of both networks. Using only the MINLP optimisation problem, first stage, the total operational cost of both networks is reduced by 15.58%, while the voltage profile at the end of the power grid is improved by about 7%. In the next stage, the total operating cost of both networks is further decreased by 29.31% via implementing the P2P energy trading mechanism.

A two-stage stochastic model for intermodal terminal location and freight distribution under facility disruptions

ABSTRACT A two-stage stochastic model is developed for intermodal facility location and freight distribution under random disruptions at shipper facilities and/or intermodal terminals (IMTs). The magnitude of the disruption and the impacted locations are uncertain parameters. A two-stage stochastic programming model is used to address supply uncertainty at shippers and throughput capacity uncertainty at IMTs. A level-method based decomposition approach and the L-shaped method are used to solve the model. The state of South Carolina in the U.S.A. is used as a case study with the goal of determining the set of IMT locations that minimise the total long-run network costs due to hurricane disruptions. A methodology is developed to generate realistic scenarios. The Freight Analysis Framework Version 4.5 data set is used to generate demands and supply, and k-means clustering is used with the Hurricane database (HURDAT2) to generate hurricane disruption scenarios. Sensitivity analyses are performed by varying the disruption probabilities, disruption duration, and direct shipping cost parameters. The results indicate that as disruptions increase, less disrupted intermodal facilities are opened. Also, as direct shipping costs increase, the long-term savings increase non-linearly for all magnitudes of disruptions.