Empty Transportation Research Articles

Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines.

Liner shipping network design model with carbon tax, seasonal freight rate fluctuations and empty container relocation

In response to the challenges posed by the shipping carbon tax resulting from the low-carbon environmental policy, the seasonal fluctuation of freight rates in the container liner shipping market, and the reduced flexibility in fleet adjustment, we develop a bi-level programming model to maximize the average revenue per container ship and the total revenue of the fleet.The upper-level model selects the optimal shipping networks for both the off-season and peak season, while also designing the liner fleet for these two networks. The lower-level model optimizes the slot allocation scheme and the empty container storage and transportation scheme to evaluate the revenue of the network calculated by the upper-level model. Three meta-heuristic algorithms are proposed. We take the China-West Europe liner shipping route as the research object, and conduct numerical experiments on different optimization objectives and liner types. Results demonstrate that maximizing the average revenue per container ship involves reducing the carbon tax cost, simplifying the trunk route structure, and increasing the number of feeder routes. These changes lead to reduced satisfaction rates for transportation demands in both China and Europe. If the company seeks to maximize total revenue, they may achieve the opposite result. Therefore, liner companies should reasonably set optimization goals, adjust the network structure and liner operation status in a timely manner, and scientifically allocate container ship and empty containers to achieve the operational goals of reducing carbon emissions and maximizing revenue to cope with the volatile market and the low-carbon regulations.

Game-Theoretical Method Toward Dry Port Multilevel Location Considering Capacity Constraints and Shippers’ Choice Behavior

The high demand for containerized cargo and the low market share of low-carbon transportation modes for hinterland freight systems in China are causing severe traffic congestion and environmental pollution. Dry ports offer an opportunity to reduce carbon emissions, relieve congestion, and expand the hinterland of seaports. Therefore, this paper focuses on the dry port multilevel location problem considering capacity constraints, shippers’ choice behavior, and empty container transportation in a four-mode port-hinterland freight network. A Stackelberg game-theoretical model is built for the government and shippers. In the model, the government determines the number, location, and capacity level of dry ports, and shippers choose seaports and paths according to the constrained nested logit model because of the capacity constraints of nodes and links. To solve the problem, a complete enumeration algorithm and a hybrid genetic algorithm are designed. Numerical experiments in China are performed, and two analyses are conducted on the cost parameters and time parameters. The results indicate that constructing dry ports can decrease carbon emissions and generalized transportation costs, and promote the mode shift from road to railway. However, building a larger number and higher capacity level of dry ports sometimes increases total costs, contrary to expectations. There are also some interesting findings with respect to the effects of costs and time parameters on dry port location results, carbon emissions, modal split, and seaport market share.

Distribution-Free Approaches for an Integrated Cargo Routing and Empty Container Repositioning Problem with Repacking Operations in Liner Shipping Networks

Cargo (laden container) routing and empty container repositioning are crucial components in liner shipping, which are closely relevant. However, most existing works focus on cargo routing and empty container repositioning separately, which cannot optimize the global shipping cost. In addition, (1) appropriate repacking operations for laden containers can curtail the total handling costs, and (2) the perfect information for uncertain laden and empty container demands may be difficult to obtain due to insufficient historical data. This work investigates an integrated cargo routing and empty container repositioning problem with repacking operations in which only partial information about uncertain laden and empty container demands is known. The objective is to minimize the total costs including repacking operation cost and laden and empty container transportation costs. For the problem, a new chance-constrained programming model based on moment-based ambiguous sets is formulated. Then, four distribution-free solution approaches are adopted to solve the investigated problem. Numerical experiments are conducted to compare the proposed methods.

Research on empty container transportation based on (D, U) control strategy under random demand

In order to effectively reduce the cost of empty container dispatching and transportation, an integer programming model for empty container dispatching and transportation with multiple periods and multiple ports was established, and the empty container dispatching and transportation scheme was optimized under the (D, U) inventory control strategy considering the service level. The results show that compared with no inventory control strategy, the (D, U) strategy can effectively reduce the total operating cost of liner companies. Select the service level for sensitivity analysis, and verify that (D, U) control strategy can effectively reduce the dependence of liner companies on container chartering.

Research on Bi-objective Vehicle Routing Problem Considering Empty Loading Ratio

The vehicle routing plan in logistic distribution minimizing the transportation cost merely may lead to high empty loading ratio and a waste of logistic resources. This research studies the bi-objective vehicle routing problem considering empty loading ratio. The definitions about the empty loading ratio are proposed firstly. A bi-objective vehicle routing model is developed, minimizing the empty loading ratio and transportation cost simultaneously. Then, the solution method based on ɛ constraint method is provided to compute the Pareto optimal solutions. Finally, numerical experiments are reported, and the results show that the ɛ constraint method is better than the weighting method for the proposed problem.

Optimization of multi- period empty container repositioning and renting in CHINA RAILWAY Express based on container sharing strategy

BackgroundWith 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.AimThrough 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.MethodConsidering 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.ResultBased 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.

Detention decisions for empty containers in the hinterland transportation system

In this paper, we study a hinterland empty container transportation system which consists of a sea container terminal and an inland container terminal. There are a hinterland container operator who is in charge of the hinterland container transportation and an ocean carrier who has an empty container depot at the sea container terminal. We utilize a two-stage game model to describe the ocean carrier’s decision about the container’s free detention time and the hinterland container operator’s decision about the time when should an arrived empty container at the inland terminal be dispatched to the sea terminal. Optimal delivery policy of the empty container and the ocean carrier’s optimal free detention time are derived. It is shown that the decentralized system does not guarantee system coordination all the time. The ocean carrier has incentive to integrate the hinterland transportation operation only if the hinterland area is not very short of empty containers.

Empty Container Management at Ports Considering Pollution, Repair Options, and Street-Turns

International trade imbalances make the management of empty containers within shipping networks an important economic and ecological problem. While import-dominated ports accumulate large amounts of empty containers, export-dominated ports need them as transport resources, requiring a repositioning transportation of empty containers on the sea and land side. Acknowledging the importance of the problem, plenty of respective literature has appeared. Since periodic review inventory management systems allow to model the inherent stochasticity of empty container transportation, they have emerged as a major solution approach in the domain. Nevertheless, existing approaches often omit crucial economic and ecological real world conditions determining the success of empty container management. Pollution, repair options, and street-turns are important aspects in this context. In this work, we present new stochastic review policies incorporating a realistic allocation scheme for empty container emissions, realistic maintenance, and repair options as well as street-turns. We analyze the optimality of the proposed polices and evaluate them in a simulation model with metaheuristic parameter search based on extensive real-world data from a major global shipping company operating in Latin America. Results provide insights for academics and practitioners about the economic and ecological impact of the distinct empty container management polices within a shipping network.

Half-life time of ozone as a function of air movement and conditions in a sealed container

The effectiveness of ozone sterilization of empty storage and transportation containers used for food products and bulk grains depend on the ability to maintain high concentrations over a sufficiently long period of time. The half-life time (HLT) is generally the measure used to characterize the degradation of a fumigant in air. It is typically affected by the air vapor properties, temperature and relative humidity, gas diffusivity and air tightness. The focus of this study was to determine HLT of ozone in air as a function of airflow (0, 0.028, 0.051, 0.10 m3/s), temperature (4, 24, 40 °C) and relative humidity (0, 30, 80%) in a sealed plexiglass cylinder equipped with a combination temperature/humidity sensor and fan. HLT was as high as 1524 min (25.4 h) in still air at 24 °C and zero humidity, which was substantially longer than previously published data (i.e., 30–40 min). As airflow, temperature and humidity increased, HLT decreased to as low as 39 min. The results suggest that sterilization with ozone will be more effective in still air at low temperature and humidity (e.g., headspace ozonation of rail cars in the early spring) than at high temperature and humidity (e.g., grain storage silo in the middle of summer).

Container repositioning management in liner shipping industry

Article history: Received January 16, 2013 Received in revised format 23 April 2013 Accepted 29 April 2013 Available online May 1 2013 Managing empty containers in shipping industry plays an important role to increase the profitability on marine shipping industry. This paper presents an empirical investigation to detect influential factors in container transportation and estimates the value of empty containers transportation. The study also evaluates and compares different strategies employed to reduce the charge of empty container transportation. By selecting three major passages including Trans-pacific, trans-Atlantic and Europe-Asia and considering the balance in transportation network, regardless of the origin of shipping companies, an optimistic estimation of empty container transportation would be possible; therefore, empty container transportation could be explained, quantitatively. Furthermore, since shipping companies organize the movement of containers in various routes, four different strategies are also proposed.

Cargo routing and empty container repositioning in multiple shipping service routes

This paper considers the problem of joint cargo routing and empty container repositioning at the operational level for a shipping network with multiple service routes, multiple deployed vessels and multiple regular voyages. The objective is to minimize the total relevant costs in the planning horizon including: container lifting on/off costs at ports, customer demand backlog costs, the demurrage (or waiting) costs at the transhipment ports for temporarily storing laden containers, the empty container inventory costs at ports, and the empty container transportation costs. The laden container routing from the original port to the destination port is limited with at most three service routes. Two solution methods are proposed to solve the optimization problem. The first is a two-stage shortest-path based integer programming method, which combines a cargo routing algorithm with an integer programming of the dynamic system. The second is a two-stage heuristic-rules based integer programming method, which combines an integer programming of the static system with a heuristic implementation algorithm in dynamic system. The two solution methods are applied to two case studies with 30 different scenarios and compared with a practical policy. The results show that two solution methods perform substantially better than the practical policy. The shortest-path based method is preferable for relatively small-scale problems as it yields slightly better solution than the heuristic-rules based method. However, the heuristic-rules based method has advantages in its applicability to large-scale realistic systems while producing good performance, to which the shortest-path based method may be computationally inapplicable. Moreover, the heuristic-rules based method can also be applied to stochastic situations because its second stage is rule-based and dynamical.

Solving the Uncapacitated Network Design Problem by a Lagrangean Heuristic and Branch-and-Bound

The network design problem is a multicommodity minimal cost network flow problem with fixed costs on the arcs, i.e., a structured linear mixed-integer programming problem. It has various applications, such as construction of new links in transportation networks, topological design of computer communication networks, and planning of empty freight car transportation on railways. We present a Lagrangean heuristic within a branch-and-bound framework as a method for finding the exact optimal solution of the uncapacitated network design problem with single origins and destinations for each commodity (the simplest problem in this class, but still NP-hard). The Lagrangean heuristic uses a Lagrangean relaxation as subproblem, solving the Lagrange dual with subgradient optimization, combined with a primal heuristic (the Benders subproblem) yielding primal feasible solutions. Computational tests on problems of various sizes (up to 1000 arcs, 70 nodes and 138 commodities or 40 nodes and 600 commodities) and of several different structures lead to the conclusion that the method is quite powerful, outperforming for example a state-of-the-art mixed-integer code, both with respect to problem size and solution time.