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Liner companies have responded to escalating trade conflicts and the impact of the COVID-19 pandemic by forming alliances and implementing streamlined approaches to manage empty containers, which has strengthened the resilience of their supply chains. Meanwhile, shippers have grown more sensitive during these turbulent times. Motivated by the market situation, we investigate a liner alliance shipping network design problem considering the choice inertia of shippers and empty container relocation. To address this problem, we propose a bilevel programming model. The upper model aims to maximize the alliance's profit by optimizing the alliance's shipping network and fleet design scheme. The lower model focuses on optimizing the slot allocation scheme and the empty container relocation scheme. To ensure the sustainable operation of the alliance, we develop an inverse optimization model to allocate profits among alliance members. Furthermore, we design a differential evolution metaheuristic algorithm to solve the model. To validate the effectiveness of the proposed model and algorithm, numerical experiments are conducted using actual shipping data from the Asia-Western Europe shipping route. The results confirm the validity of the proposed model and algorithm, which can serve as a crucial decision-making reference for the daily operations of a liner shipping alliance.

In this study, an Economic Return Quantity (ERQ) model for the Empty Container Reposition (ECR) problem using the reverse logistics (RL) approach is developed. Some of the model’s primary considerations are the return rate that depends on the quantity and quality of the empty container, and the capacity constraints to hold the empty container in the port. The model of ERQ is optimized using an analytical approach. Based on the result of the hypothetical case, the authors examined that the acceptable quality level of reusable containers should be set at 67%, 55%, and 50% for the three types of containers to be able to obtain minimum inventory costs. Two cases of binding and nonbinding constraints are investigated, and it is found that the binding constraint gives 3.4% higher cost than the latter. The results of this study help the container depots to plan, manage, and handle empty containers so that the container utility can be increased, and inventory costs can be minimized.

Due to the trade imbalance and poor management, a large number of empty containers are piled up after unpacking in importing regions, while the export-oriented areas are in urgent need of empty containers for replenishment. This paper aims to determine the volume of empty containers transferred from the surplus area to the shortage area and the inventory of each node from the operational level. We subdivide container freight stations into rail stations and road stations and consider using sea, road, and railway as transportation modes to transfer empty containers. A multi-period linear programing model of multimodal empty container repositioning is established aiming at minimizing the cost of empty container repositioning. A real case study of the transportation network in Northeast China is conducted to demonstrate that using multiple modes of transportation can significantly reduce the cost of empty container repositioning compared to using a single mode of transportation.

The optimization of empty container repositioning nets has become an essential problem in low-carbon port cooperation. This paper proposed three optimization models of multi-port low-carbon empty container repositioning considering threshold under input and output of empty containers as random variables. Non repositioning strategy means the highest threshold, and complete-repositioning strategy means the lowest threshold; threshold-repositioning strategy is in the middle. The probability of empty-container inventory in each port and the storage cost, repositioning cost, lease cost, and carbon emission cost of empty containers are calculated. This paper mainly compares each cost of three models. The results have shown that: (1) Compared with the non repositioning strategy, the threshold-repositioning strategy and complete-repositioning strategy can reduce the ports storage costs and lease costs of empty containers and also reduce carbon emissions. The lower the repositioning threshold of empty containers between ports is, the more obvious the advantages of the threshold-repositioning strategy become. (2) When the cost of storage per empty container increases, under three strategies, the total cost, storage cost, lease cost, and carbon emission cost of the port will all increase. The ports proportion of dependence on its own empty-container storage will decrease, and the proportion of dependence on other ports and leasing companies will both increase.

Abstract Due to imbalances in the global transport of containerised goods, liner shipping companies go to great lengths to match the regional supply and demand for empty containers by transporting equipment from surplus to deficit regions. Making accurate forecasts of regional empty container availability could support liner companies and other involved actors by making better relocation decisions, thus avoiding unnecessary transport costs of empty equipment. Previously proposed container availability prediction models are limited to the application in individual regions and typically characterized by a high degree of temporal aggregation. Against this background, this paper introduces two novel approaches based on machine learning and probabilistic techniques to predict the future weekly availability of empty containers for more than 280 locations worldwide. The machine learning and probabilistic prediction models are built by analysing a unique data set of more than 100 million events from past container journeys. These events represent different stages during the transport process of a container. Both models use a two-step forecast logic. First, the expected future location of a container is predicted. Second, the expected timestamp for arriving at that location is estimated. The machine learning model uses artificial neural networks and mixture density networks to forecast the movements of containers. The models are quantitatively assessed and compared to the actual availability of containers and two more conventional forecasting approaches. The results indicate that the probabilistic prediction approach can keep up with conventional approaches while the neural network approach significantly outperforms the other approaches concerning every evaluation metric.

Due to the trade imbalance and poor management, a large number of empty containers are piled up after unpacking in importing regions, while the export-oriented areas are in urgent need of empty containers for replenishment. This paper aims to determine the volume of empty containers transferred from the surplus area to the shortage area and the inventory of each node from the operational level. We subdivide container freight stations into rail stations and road stations and consider using sea, road, and railway as transportation modes to transfer empty containers. A multi-period linear programing model of multimodal empty container repositioning is established aiming at minimizing the cost of empty container repositioning. A real case study of the transportation network in Northeast China is conducted to demonstrate that using multiple modes of transportation can significantly reduce the cost of empty container repositioning compared to using a single mode of transportation. [ABSTRACT FROM AUTHOR]

Approximately 20% of global container transportation activities are used for empty container repositioning, which does not generate profit margins. For container supply ports, in addition to periodically transporting excess empty containers to container shortage ports to reduce their inventory costs and meet empty container demand of container shortage ports, they also need to maintain a certain amount of empty container inventory to meet their uncertain needs in the future. This paper considers the container supply port and container shortage port as a whole, so that the container supply port maintains a certain amount of empty container inventory to meet its own uncertain needs, and empty container repositioning is carried out based on this. Based on queuing theory, an empty container repositioning model was established with the objective function of minimizing the total cost of the empty container inventory system and the empty container repositioning system and designing genetic algorithms to solve the model. The results indicate that considering empty container inventory and repositioning as a whole resulted in a total cost reduction of approximately 7.2%. Finally, a sensitivity analysis was conducted by changing the parameters in the model, and the results of the sensitivity analysis provided support for the decision-making of shipping lines in different situations.

Abstract Background With the steady growth of trade between China and Europe, the number of trains in CHINA RAILWAY Express has steadily increased under the Belt and Road Initiative. By the end of 2019, more than 21,000 trains have been operated. However, in the development of CHINA RAILWAY Express, the cost of empty container transportation and leasing remains high. So, it is necessary to put forward the strategy of container sharing. Aim Through the optimization of empty container transfer and rent under container sharing, this study aim to reduce the operation cost and put forward the strategy of container sharing. Method Considering the dynamic characteristics of empty container transportation in reality, this paper constructs a multi-period empty container transportation optimization model with the lowest total cost and the stability of the network, combining with the container sharing strategy. Lyapunov optimization method is used to transform the complex problem into a single period optimization problem, and then genetic algorithm is used to solve the model. Result Based on the analysis of 13 existing regular train companies, the results show that the model can effectively improve the utilization rate of containers and reduce the cost in the decision-making period. Based on the results and parameters analysis, some suggestions are put forward to optimize the empty container sharing and effectively realize of container sharing.

International trade continues to grow, increasing container traffic at seaports worldwide. The Port of Los Angeles saw a 20% increase in throughput from 2010-2018. Typically, loaded containers arriving at the ports are transported by truck to inland importers, emptied, and then returned to the port. The empty containers are then driven to inland exporters to be loaded and returned to the port for shipping. This lack of direct container exchange between importers and exporters, known as the empty container reuse problem, adds many truck trips to Southern California highways and adds significant expenses for shipping companies. Trucks transporting empty containers contribute to traffic congestion, greenhouse gas emissions, and local air pollution.Moving empty containers more efficiently could significantly reduce truck trips and the associated congestion and pollution. Researchers at the University of Southern California developed an optimization-based vehicle scheduling model that allows for a “street exchange” in which empty containers can go directly from importers to exporters without returning to the port. The model satisfies the current day’s known, or deterministic, demand while also accounting for the next day’s unpredictable, or stochastic, demand. The model is solved iteratively each day using this two-day time horizon to provide a routing plan for the current day’s demand. The model was tested using container demand data from the Ports of Los Angeles and Long Beach as well as randomly generated data sets. This research brief summarizes findings from that project.View the NCST Project Webpage

In today’s world, there is a significant amount of investigation regarding how to efficiently distribute loaded containers from the ports to the consignees. However, to fully maximize the process and become more environmentally friendly, one should also study how to allocate the empty containers created by these consignees. This is an essential part in the study of container movement since it balances out the load flow at each location. The problem of coordinating the container movement to reuse empty containers and lower truck miles is called the “Empty Container Problem”. In this work, the authors develop a scheduling assignment for loaded and empty containers that builds on earlier models but incorporates stochastic (random) future demand. Since this problem is meant to be solved daily and the solution implemented today affects tomorrow’s starting state, incorporating future demand is an important aspect. This report shows that the truck miles needed to satisfy the demand at all locations is reduced by about 4-7% when considering future stochastic demand as opposed to only considering today’s demand. Thus, leading to a cleaner and greener solution, creating less congestion and lowering the impact of freight movement on the environment.View the NCST Project Webpage

In this study, the empty container problem is evaluated by quantification of factors related to empty container accumulation as well as alternative ways that can be followed for solutions. The study is mainly constructed as two parts; the first part is on identifying involving factors by using DEMATEL and the second part deals with alternative solutions by applying TOPSIS method. The main causes affecting empty containers were found as trading imbalance, irregular distribution, delivery time, unbalanced freight charges and inadequate port management. Finally, based on applied Multi Criteria Decision Making approach, this study suggests that empty container problem can be solved by sharing infrastructures and equipment among logistic companies, allocating storage areas for empty containers outside the ports and following robust fast custom regulations.

Liner companies have responded to escalating trade conflicts and the impact of the COVID-19 pandemic by forming alliances and implementing streamlined approaches to manage empty containers, which has strengthened the resilience of their supply chains. Meanwhile, shippers have grown more sensitive during these turbulent times. Motivated by the market situation, we investigate a liner alliance shipping network design problem considering the choice inertia of shippers and empty container relocation. To address this problem, we propose a bilevel programming model. The upper model aims to maximize the alliance's profit by optimizing the alliance's shipping network and fleet design scheme. The lower model focuses on optimizing the slot allocation scheme and the empty container relocation scheme. To ensure the sustainable operation of the alliance, we develop an inverse optimization model to allocate profits among alliance members. Furthermore, we design a differential evolution metaheuristic algorithm to solve the model. To validate the effectiveness of the proposed model and algorithm, numerical experiments are conducted using actual shipping data from the Asia-Western Europe shipping route. The results confirm the validity of the proposed model and algorithm, which can serve as a crucial decision-making reference for the daily operations of a liner shipping alliance. [ABSTRACT FROM AUTHOR]

Abstract A shortage of empty containers has become a global crisis with more devastating effects than during previous periods when combined with various problems arising from the COVID-19, such as an increase in an imbalance of global trade between supply and demand, a decrease in the workforce, and restrictions by countries or regional quarantine practices. The absence of empty containers in regions where they are needed slows down industrial activities and locks the global supply networks, necessitating the use of alternative methods that are inefficient. Although this shortage causes many disruptions in global trade, solutions to the issue have not been studied in detail. Therefore, the aim of this study was to determine the issues caused by the shortage of empty containers and to rank the appropriate solutions. Four main criteria and sixteen subcategories used to define the issues, as well as a multi criteria decision model comprising five criteria for the solutions, were proposed based on information from the literature, sectorial publications, and expert opinions. The issues’ weighted order of importance in our proposed model was calculated using the SWARA (Step-wise Weight Assessment Ratio Analysis) method; solutions were ranked using the ARAS (Additive Ratio Assessment) method. The results of the study revealed that the issues were ranked in importance as cost increases, uncertainty in the supply chain, volume loss, and increases in blank sailing announcements. Appropriate solutions were ranked as booking guarantee applications and information communication technologies, using shipper-owned containers, inducement calls, and E2E (end to end) delivery services.

In 2015, the Ports of Los Angeles and Long Beach moved 15.3 million twenty-foot Equivalent Units (TEU). There is a significant body of work on moving loaded containers efficiently, however there has been little research on the movement of empty containers. Out of the 15.3 million TEUs, about 30% or 4.3 million TEUs were empty containers. Empty container movement is increasing greatly because of the enormous inconvenience for companies to coordinate with each other to exchange empty containers. This problem is known as the Empty Container Problem. This study proposes a mathematical model that solves the empty container problem using double and single container trucks. The model discretizes time and ensures demand is met. By solving the empty container problem, congestion can be reduced since fewer truck trips would be needed to satisfy demand. Furthermore, since double container trucks can deliver two containers per truck trip, the quantity of trucks needed to satisfy the demand is decreased even more, further reducing congestion. The model was tested using data from the Ports of Los Angeles and Long Beach. The results are promising and show that the number of miles and trucks can be significantly reduced by increasing the number of street exchanges, and further reduced by using double container trucks. This report shows that using a single container policy instead of the current policy would reduce truck miles by about 12%, and would reduce significant truck trips to and from the port. The double container policy reduces truck miles by about 55% compared to the current policy, which is a noteworthy reduction. This could potentially reduce congestion substantially, lessening the impact of container freight movement on the environment.View the NCST Project Webpage

Container drayage involves the transportation of containers by trucks. Although the distance is relatively short compared to maritime and rail transport, container drayage accounts for 25% to 40% of the total container transportation costs and significantly contributes to increased fuel consumption and carbon emissions. Thus, the modeling of the container drayage problem (CDP) has received a lot of attention in the last two decades. However, the three fundamental modeling factors, including the combination of trucking operation modes and empty container relocation strategies, as well as empty container constraints and multiple inland depots, have not been simultaneously investigated. Hence, this study addressed a comprehensive CDP that simultaneously incorporates the three modeling factors. The problem was formulated as a novel mixed integer linear programming (MILP) model based on the DAOV graph. Given the complexity of this problem, it was not realistic to find an exact solution for large instances. Therefore, an improved genetic algorithm (GA) was designed by integrating the "sequential insertion" method and "solution re-optimization" operation. The performance of Gurobi and GA was validated and evaluated through randomly generated instances. The results indicate that (1) the proposed algorithm can provide near-optimal solutions for large-scale instances within a reasonable running time, (2) the greatest cost savings from combining trucking operation modes and empty container relocation strategies range from 10.45% to 31.86%, and (3) the three modeling factors significantly influence the fuel consumption and carbon emissions, which can provide managerial insights for sustainable container drayage practices. [ABSTRACT FROM AUTHOR]