Empty Container Allocation Research Articles

Optimization of merchandise delivery logistics: case studies at Bejaia port

The object of the study is the logistics of goods delivery by ports. This study presents a methodology designed to improve the efficiency of goods delivery logistics at the Bejaia port (Algeria). It prioritizes the optimization of empty container allocation to the ZEP zone, taking into account the geographical accessibility of the Tixter area, aiming to reduce the high costs linked with goods transportation. At the core of this strategy lies the use of simulation techniques to optimize truck fleets, ensuring maximum utilization rates and effective management of delivery operations by the Bejaia Mediterranean Terminal (BMT) for its clients. Addressing this challenge, the study offers an exhaustive analysis, integrating truck assignment models and accessibility assessments of logistic zones. The results highlight the paramount significance of optimal resource allocation and synchronized client coordination for achieving streamlined goods delivery. It becomes apparent that employing these methodologies can yield substantial productivity improvements, emphasizing their pivotal role in strengthening the port's logistical infrastructure. Via rigorous analysis and insights derived from data, this study elucidates avenues towards achieving operational excellence within the logistical infrastructure of the port. By harnessing innovative strategies to confront persistent challenges, such as optimizing truck fleets and strategically allocating resources, the research anticipates a profound transformation in the efficiency and cost-effectiveness of goods delivery operations. Ultimately, the integration of these methodologies holds the potential to propel the port of Bejaia towards enduring success and a competitive edge in the ever-evolving landscape of global trade. Through extensive efforts, this strategy can be extended to other national and international ports operating under similar conditions, as it provides valuable information and methodologies to optimize logistics and transportation operations.

Container drayage problem integrated with truck appointment system and separation mode

Growing demand for seaborne trade has exacerbated the challenges faced by drayage operators in inland container transportation. In some cases, drayage operators must make truck scheduling decisions and book periods for trucks to visit terminals daily. This paper considers the appointment system and separation mode in container drayage problem, which consists of delivery and pickup transportation tasks for empty and full containers. A mixed-integer linear programming model is developed to minimize the total truck operating time while solving the truck scheduling problem, the empty container allocation problem, and the appointment problem. Another key feature of the model is considering the temporal constraints between tasks from a more realistic perspective. Due to the complexity of the problem, it is not practical to obtain exact solutions for large instances. Therefore, we complete the implementation of a new genetic algorithm (GA) that introduces a topological sort tailored to this problem. Several experiments and analyses are used to demonstrate the correctness of the proposed model as well as the efficiency of the algorithm. The findings indicate that, when compared to traditional mode, separation mode can reduce total truck operating time by an average of 29.4%. The total truck operating time difference between the two modes shows an overall positive trend if the task size increases. In addition, a rational setting of the appointment system will have a beneficial effect on truck scheduling. The decision on the selection of periods and the increment of each quota is crucial in optimizing the setting of the appointment system to reduce the total truck operating time for the mutual benefit of both terminals and drayage operators.

Container Scheduling considering Customer Satisfaction in the Context of Internal Circulation

As China’s foreign trade continues to progress, a trade surplus in inland container transportation has emerged. At the same time, businesses need to not only focus on transportation tasks but also prioritize customer service requirements. To address the issues of container supply shortages and inefficient empty container allocation, this article establishes a container truck scheduling model that includes multiple terminals and yards. With the objective of minimizing total costs and maximizing customer satisfaction during the allocation process, the model incorporates coupling constraints between vehicle flow and container flow. Instance verification using Cplex demonstrates that the proportions of different cost types and the range of expected time windows can impact the formulation of transportation plans and, consequently, total costs. Lastly, numerical experiments are conducted to analyze the cost variations and transportation plan preferences under different ranges of time windows and different weights assigned to transportation costs and fixed costs.

Optimization of empty container allocation for inland freight stations considering stochastic demand

In the post-COVID-19 epidemic era (PCEE), the supply of empty containers will face stronger uncertainty. Estimating the amount of self-owned and leased empty containers that need to be allocated to each inland freight station in a specific area becomes a critical issue for liner companies in PCEE. However, owing to the high degree of unpredictability of the demand and the limited flexibility of empty container relocation, the abovementioned issue has not been fully addressed. This paper provides a model for empty container allocation without knowing the probability distribution function of empty container demand in advance. The abovementioned model can jointly optimize the quantities of self-owned empty containers and leased containers allocated to each inland freight station. To solve the model, a largest-debt-first policy is adopted to simplify the complicated model, and a differential evolutionary (DE) algorithm is developed to solve the simplified model. Compared with some commonly used algorithms, DE has advantages considering the ability to explore the optimal solution. In addition, the utility of the largest-debt-first policy proposed in this paper is compared with that of the traditional method. Experimental results show that in the case of high demand fluctuations, the proposed policy is better in controlling the operational and management costs. Overall, the theory and method proposed in this paper can effectively help the carrier set a reasonable regional empty container stock level and determine the number of self-owned and leased empty containers.

An analysis of port congestion alleviation strategy based on system dynamics

Port congestion has become a key factor restricting the international trade and economic development, especially during the COVID-19 epidemic. It is essential for the port to implement the effective alleviation strategies for handling the uncertain congestion. This paper aims to investigate the performance of the epidemic prevention alliance strategy (EPAS), shared berths strategy (SBS) around adjacent ports and their hybrid strategy in alleviating the port congestion. To simulate the effect of these three strategies, a system dynamics model of dual-port operation is developed considering the factors of the integrated service level of liner routes, empty container allocation, port congestion and regional economics, and so forth. The results indicate that the key issue of port congestion stems from the implementation of epidemic preventive measures. Among these three strategies, the hybrid strategy performs the best in alleviating the port congestion, improving integrated service levels, and curbing the fluctuation of container price. Moreover, the measures of investing more human resources and fixed assets are always taken in many current ports to alleviate the issue of port congestion. Therefore, the impacts of various investment in human resources and fixed assets on alleviating the port congestion are discussed. Finally, some suggestions are provided for the government to strengthen the cooperation between ports and promote the construction of port facility resources.

An Optimization Model of Railway Empty Container Allocation Based on Customer Satisfaction

With the rapid development of modern corporate management, customer satisfaction evaluation has been applied to the management of many companies. Railway empty container allocation not only involves the cost of the railway transportation, but also considers the customer's requirements for time and convenience, etc. Therefore customer satisfaction evaluation also becomes very important for railway container allocation decision. However, it is not easy to properly quantify customer satisfaction, because there are many indicators that cannot be directly quantified. This paper proposes a customer satisfaction evaluation index system based on the RATER index, and adopts a comprehensive evaluation method to achieve quantitative evaluation of customer satisfaction. An empty container allocation optimization model based on customer satisfaction is established. The model can comprehensively consider the interests of both the railway and customers, and provide an effective reference for the railway transportation department to improve the transportation service and optimize empty container allocation plan.

Empty container exchange among liner carriers

In an attempt to reduce the empty container repositioning costs, this paper studies an empty container allocation problem considering the coordination among liner carriers. We further measure the perceived values of empty container at different ports. The perceived values of empty container at the surplus (deficit) ports are described by the profits (empty container exchange costs paid) for delivering empty containers. To solve our problems, we propose a two-stage optimization method. In stage I, liner carriers are guided to pursue a centralized optimization solution of empty container allocation for all related liner carriers. In stage II, the inverse optimization technique is used to determine the empty container exchange costs, which are paid to liner carriers for exchanging empty containers and following the centralized optimization solution. The profits at the surplus ports are calculated with respect to the empty container exchange costs at the deficit ports. Finally, numerical experiments on an Asia–Europe–Oceania shipping service network are discussed.

Applying a time–space network to reposition reefer containers among major Asian ports

Empty container repositioning is a complicated daily operation that liners have to deal with due to the imbalanced cargo flows. This operation is comprised of two main problems: repositioning both dry containers and special containers. Due to the considerable amount of containers and high similarity, dry containers are usually moved in batches. In addition, leasing and pooling are also complementary strategies commonly used to adjust the allocation of empty dry containers. On the contrary, reefer containers have to be repositioned more precisely than dry containers because they can yield high profits and cannot be substituted easily. This study formulates a time–space model suitable for dealing with this large scale mathematical problem. A comparison between the model output and real operation is made to demonstrate the performance of the proposed model.

Joint slot allocation and dynamic pricing of container sea–rail multimodal transportation

The container sea–rail multimodal transport system faces complex challenges with demand uncertainties for joint slot allocation and dynamic pricing. The challenge is formulated as a two-stage optimal model based on revenue management (RM) as actual slots sale of multi-node container sea–rail multimodal transport usually includes contract sale to large shippers and free sale to scattered shippers. First stage in the model utilizes an origin-destination control approach, formulated as a stochastic integer programming equation, to settle long-term slot allocation in the contract market and empty container allocation. Second stage in the model is formulated as a stochastic nonlinear programming equation to solve a multiproduct joint dynamic pricing and inventory control problem for price settling and slot allocation in each period of free market. Considering the random nature of demand, the methods of chance constrained programming and robust optimization are utilized to transform stochastic models into deterministic models. A numerical experiment is presented to verify the availability of models and solving methods. Results of considering uncertain/certain demand are compared, which show that the two-stage optimal strategy integrating slot allocation with dynamic pricing considering random demand is revealed to increase the revenue for multimodal transport operators (MTO) while concurrently satisfying shippers' demand. Research resulting from this paper will contribute to the theory and practice of container sea–rail multimodal transport revenue management and provide a scientific decision-making tool for MTO.

ON-LINE SCHEDULING OF EMPTY CONTAINERS

This paper considers the empty container repositioning problem in an on-line scheduling setting. This optimization problem arising from container transportation management aims to balance empty containers distribution among transportation sites so as to minimize the total operating cost of loaded container shipping, empty container allocation and leasing. In this highly uncertain on-line environment we introduce a heuristic that does not attempt to balance the empty container distribution, and evaluate its competitive performance mathematically and empirically.

Study on a Liner Shipping Network Design Considering Empty Container Reposition

Empty container allocation problems arise due to imbalance on trades. Imbalanced trade is a common fact in the liner shipping,creating the necessity of repositioning empty containers from import-dominant ports to export-dominant ports in an economic and efficient way. The present work configures a liner shipping network, by performing the routes assignment and their integration to maximize the profit for a liner shipping company. The empty container repositioning problem is expressly taken into account in whole process. By considering the empty container repositioning problem in the network design, the choice of routes will be also influenced by the empty container flow, resulting in an optimum network, both for loaded and empty cargo. The Liner Shipping Network Design Program (LS-NET program) will define the best set of routes among a set of candidate routes, the best composition of the fleet for the network and configure the empty container repositioning network. Further, a network of Asian ports was studied and the results obtained show that considering the empty container allocation problem in the designing process can influence the final configuration of the network.

Application of a mixed fuzzy decision making and optimization programming model to the empty container allocation

Containerization transportation has been growing fast in the past few decades. International trades have been growing fast since the globalization of world economies intensified in the early 1990s. However, these international trades are typically imbalanced in terms of the numbers of import and export containers. As a result, the relocation of empty containers has become one of the important problems faced by liner shipping companies. In this paper, we consider the empty container allocation problem where we need to determine the optimal volume of empty containers at a port and to reposition empty containers between ports to meet exporters’ demand over time. We formulate this empty container allocation problem as a two-stage model: in stage one, we propose a fuzzy backorder quantity inventory decision making model for determining the optimal quantity of empty container at a port; whereas in stage two, an optimization mathematical programming network model is proposed for determining the optimal number of empty containers to be allocated between ports. The parameters such as the cost of loading container, cost of unloading container, leasing cost of empty container, cost of storing container, supplies, demands and ship capacities for empty containers are considered in this model. By taking advantages of the fuzzy decision making and the network structure, we show how a mixed fuzzy decision making and optimization programming model can be applied to solve the empty container allocation problem. The utilization of the proposed model is demonstrated with a case of trans-Pacific liner route in the real world. Six major container ports on the trans-Pacific route are considered in the case study, including the Port of Kaohsiung, the Port of Hong Kong, the Port of Keelung, the Port of Kobe, the Port of Yokohama and the Port of Los Angles. The results show that the proposed mixed fuzzy decision making and optimization programming model can be used to solve the empty container allocation problem well.

Estimating optimum container and vessel fleet sizes in a cyclic liner service using a holistic approach

The management of container fleets, regardless of type and size, is a rather costly operation. A number of strategies are present in the literature intending to efficiently and effectively tackle container utilisation emphasising on the position and allocation of empty containers. These strategies can be roughly categorised in two main groups of studies: container fleet management and fleet size planning. In this paper, a more holistic approach takes place by merging the problems of optimum vessel and container fleet size. The paper develops a model which intends to identify the factors that affect container utilisation in order to enable the effective and efficient operation and control minimising container idle time. Results indicate that the development of such a model can assist container shipping companies in improving their operations since decision-making processes relevant to container fleet size, vessel capacity utilisation, scheduling and frequency of service provided by the operators can be improved.

Implementing the street-turn strategy by an optimization model

The street-turn option represents a major strategy for the profitability of shipping companies supplying container-based transportation. This option consists in the distribution of trucks delivering loaded containers to import customers, the subsequent allocation of empty containers to export customers and the final dispatch of loaded containers to departure ports. However, the determination of truck routes is a time-consuming activity for shipping companies, because available information can suddenly change while they are making their decisions. In this paper we aim to propose a decision support tool to quickly determine truck routes and implement the street-turn strategy. This tool is based on an optimization model determining the allocation of empty containers between customers and defining truck routes in a post-optimization phase. We compare routes resulting from the proposed model to the decisions of a real shipping company. Early results indicate that this approach represents a promising support for shipping companies in dealing with street-turns. It can significantly reduce distances travelled by trucks and times requested to determine routes.

Allocation of empty containers between multi-ports

Owing to imbalances in international trade activities, shipping companies accumulate a large number of unnecessary empty containers in the import-dominant ports, whilst request a large number of empty containers in export-dominant ports. The logistics challenge to shipping companies is to better manage and control their containers, which consist of company-owned containers and leased containers. The multi-port empty container allocation problem is concerned with the allocation of empty containers from supply ports to demand ports. In this paper, optimal pairs of critical policies, ( U, D) for one port, which are importing empty containers up to U when the number of empty containers in the port is less than U, or exporting empty containers down to D when the number of empty containers is larger than D, doing nothing otherwise, are adapted to multi-port case so that decision-makers can make decisions about allocating the right amounts of empty containers to the right ports at the right time. This allocation problem has been formulated and the heuristic methods are designed according to that the average cost using ( u, d) policy at one port is convex in u and d. Furthermore, the examples show that, using the heuristic algorithm, the result in the inland line case is quite close to the lower bound, even the distance is not so close in the global line case.

An approximate dynamic programming approach for the empty container allocation problem

The objective of this study is to demonstrate the successful application of an approximate dynamic programming approach in deriving effective operational strategies for the relocation of empty containers in the containerized sea-cargo industry. A dynamic stochastic model for a simple two-ports two-voyages (TPTV) system is proposed first to demonstrate the effectiveness of the approximate optimal solution obtained through a simulation based approach known as the temporal difference (TD) learning for average cost minimization. An exact optimal solution can be obtained for this simple TPTV model. Approximate optimal results from the TPTV model utilizing a linear approximation architecture under the TD framework can then be compared to this exact solution. The results were found comparable and showed promising improvements over an existing commonly used heuristics. The modeling and solution approach can be extended to a realistic multiple-ports multiple-voyages (MPMV) system. Some results for the MPMV case are shown.

Empty container management in a port with long-run average criterion

The empty container allocation problem in a port is related to one of the major logistics issues faced by distribution and transportation companies: the management of importing empty containers in anticipation of future shortage of empty containers or exporting empty containers in response to reduce the redundance of empty containers in this port. We considered the problem to be a nonstandard inventory problem with positive and negative demands at the same time under a general holding-penalty cost function and one-time period delay availability for full containers just arriving at the port. The main result is that there exists an optimal pair of critical policies for the discounted infinite-horizon problem via a finite-horizon problem, say ( U, D). That is, importing empty containers up to U when the number of empty containers in the port is less than U, or exporting the empty containers down to D when the number of empty containers is more than D, doing nothing otherwise. Moreover, we obtain the similar result over the average infinite horizon.

A Two-Stage Stochastic Network Model and Solution Methods for the Dynamic Empty Container Allocation Problem

Containerized liner trades have been growing steadily since the globalization of world economies intensified in the early 1990s. However, these trades are typically imbalanced in terms of the numbers of inbound and outbound containers. As a result, the relocation of empty containers has become one of the major problems faced by liner operators. In this paper, we consider the dynamic empty container allocation problem where we need to reposition empty containers and to determine the number of leased containers needed to meet customers' demand over time. We formulate this problem as a two-stage stochastic network: in stage one, the parameters such as supplies, demands, and ship capacities for empty containers are deterministic; whereas in stage two, these parameters are random variables. We need to make decisions in stage one such that the total of the stage one cost and the expected stage two cost is minimized. By taking advantage of the network structure, we show how a stochastic quasi-gradient method and a stochastic hybrid approximation procedure can be applied to solve the problem. In addition, we propose some new variations of these methods that seem to work faster in practice. We conduct numerical tests to evaluate the value of the two-stage stochastic model over a rolling horizon environment and to investigate the behavior of the solution methods with different implementations.

Dynamic and Stochastic Models for the Allocation of Empty Containers

The empty container allocation problem occurs in the context of the management of the land distribution and transportation operations of international maritime shipping companies. It involves dispatching empty containers of various types in response to requests by export customers and repositioning other containers to storage depots or ports in anticipation of future demands. We describe the problem and identify its basic structure and main characteristics. We then introduce two dynamic deterministic formulations for the single and multicommodity cases, which offer a general modeling framework for this class of problems, and which account for its specific characteristics: the space and time dependency of events, substitutions among container types, relationships with partner companies, imports and exports, massive equilibration flows, etc. Finally, we provide a mathematical formulation for handling, in the single commodity case, the uncertainty of demand and supply data that is characteristic of container allocation and distribution problems. Various modeling choices, data requirements, and algorithmic considerations related to the implementation of the models are also discussed.

コンテナの短期回送計画に関する研究

Not only between two or more countries but between some regions in a big country like the U.S., there are a lot of problems for allocation and distribution of empty sea-borne containers because of international imbalance of trade situation. Authors presented an effective planning method for distribution of empty containers between two or more countries. For the distribution problem in a country, we propose another planning method since this problem contains different characteristics, i.e., short period movements of empty containers, and no consideration of state of container (i.e., full container or empty container). The distribution problem defined as the transshipment problem is formulated as transportation problem which is relatively easy to solve optimally. A case study for the distribution in the U.S. for 6 weeks shows that the total distribution cost by our method is 20 per cent less than that of the real one.