Terminal Logistics Research Articles

Effects of E-commerce Terminal Logistics Service Quality on Rural Residents' Online Purchasing Behavior

Effects of E-commerce Terminal Logistics Service Quality on Rural Residents' Online Purchasing Behavior

A Influência das instituições na gestão ambiental

Essa pesquisa visou analisar a influência das instituições estaduais na gestão ambiental dos terminais logísticos. Realizou-se a análise de dados por meio da regressão de Poisson. Apurou-se que as instituições estaduais influenciam o desempenho ambiental nos terminais. Todavia, o impacto positivo foi encontrado apenas na instituição “incentivos fiscais”, porém foi apurado um impacto negativo causado pela instituição “abrangência da legislação ambiental”. Apesar de serem contraditórios, esses resultados estão em concordância com a literatura. O impacto negativo da “abrangência da legislação ambiental” indica que à legislação ambiental e seu enforcement não estão plenamente alinhados nos países em desenvolvimento, como o Brasil.

An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace

Traditional terminal logistics distribution in urban areas is mainly concentrated on the ground, which leads to increasingly serious air pollution and traffic congestion. With the popularization of unmanned aerial vehicle (UAV) techniques and the reform of low altitude airspace, terminal logistics distribution is expected to be carried out by drones. Therefore, it is of great significance to construct a reasonable air route network for logistics UAV to ensure the safety and efficiency of operations. In this paper, a single route planning model and an air route network planning model for UAV were constructed by fully considering the complex urban low altitude environment, the flight performance of UAV and the characteristics of logistics tasks to regulate the flights of drones. Then, taking Jiangjun Road Campus of Nanjing University of Aeronautics and Astronautics as an example, the improved cellular automata (CA) was adopted to search for the optimal route between different waypoints, and the optimal spanning tree algorithm was used to construct the route network. The experimental results demonstrated that the improved CA could greatly reduce search time and obtain the optimal route while enhancing safety. With the satisfaction of the voyage, the needs of logistics and distribution constraints, a network that had smaller intersection points and redundancy was generated. The models and core ideas proposed in this paper can not only regulate operation of drones but also provide a solid foundation for the distribution of logistics UAV in the future.

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

Simulation Model to Determine Ratios between Quay, Yard and Intra-Terminal Transfer Equipment in an Integrated Container Handling System

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

Computational Logistics for Container Terminal Handling Systems with Deep Learning.

Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques

The authors propose an innovative Internet of Things (IoT) based E-commerce business model Cloud Laundry for mass scale laundry services. The model utilises big data analytics, intelligent logistics management, and machine learning techniques. Using GPS and real-time update of big data, it calculates the best transportation path and update and re-route the logistic terminals quickly and simultaneously. Cloud laundry intelligently and dynamically provides the best laundry solutions based on the current state spaces of the laundry terminals through the user's specifications and thus offers local hotel customers with convenient, efficient, and transparent laundry services. Taking advantage of the rapid development of the big data industry, user interest modelling, and information security and privacy considerations, cloud laundry uses smartphone terminal control and big data models to maintain customers’ security needs. Different from the traditional laundry industry, cloud laundry companies have higher capital turnover, more liquidity, and stronger profitability. Therefore, this new generation of smart laundry business model could be of interest to not only academic researchers, but E-commerce entrepreneurs as well.

Computational Logistics for Container Terminal Logistics Hubs Based on Computational Lens and Computing Principles

The volatile global shipping market puts forward the higher requirements to the container terminal logistics systems (CTLS) than ever, especially in terms of programming, planning, scheduling and decision. The computational logistics provide a systematic methodology to overcome the issues that differs from traditional approaches distinctly. This paper discusses the theoretical framework, important components and core concept of computational logistics, and then presents the container terminal logistics generalized computation design, implement, execution, analysis and evaluation hierarchy (LGC-DIE-AEH) by the integration of the computational lens and computing principles to explore the computational logistics in the field of CTLS. Subsequently, the execution performance of a quay crane farm at a regional container hub terminal in China is analyzed and evaluated by LGC-DIE-AEH. The crane performance evaluation core indicator framework (CPE-CIF) is proposed by the fusion of the design philosophy and underlying principles of computing architecture, operating system, and virtual machine by computational lens. The CPE-CIF help us to find out the advantages, disadvantages and improvement directions of quay crane farm operation. That illustrates and verifies the feasibility and credibility of LGC-DIE-AEH from the perspective of the practice of container terminal decision-making support at the tactical level.

Disruption management strategy of terminal logistics under accidental travel time delay based on prospect theory

Disruption management strategy of terminal logistics under accidental travel time delay based on prospect theory

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

Queueing analysis for operations modeling in port logistics

Purpose The use of queueing network models was stimulated by the appearance (1975) of the exact product form solution of a class of open, closed and mixed queueing networks obeying the local balance principle and solved, a few years later, by the popular mean value analysis algorithm (1980). Since then, research efforts have been produced to approximate solutions for non-exponential services and non-pure random mechanisms in customer processing and routing. The purpose of this paper is to examine the suitability of modeling choices and solution approaches consolidated in other domains with respect to two key logistic processes in container terminals. Design/methodology/approach In particular, the analytical solution of queueing networks is assessed for the vessel arrival-departure process and the container internal transfer process with respect to a real terminal of pure transshipment. Findings Numerical experiments show the extent to which a decomposition-based approximation, under fixed or state-dependent arrival rates, may be suitable for the approximate analysis of the queueing network models. Research limitations/implications The limitation of adopting exponential service time distributions and Poisson flows is highlighted. Practical implications Comparisons with a simulation-based solution deliver numerical evidence on the companion use of simulation in the daily practice of managing operations in a finite-time horizon under complex policies. Originality/value Discussion of some open modeling issues and encouraging results provide some guidelines on future research efforts and/or suitable adaption to container terminal logistics of the large body of techniques and algorithms available nowadays for supporting long-run decisions.

Research on Joint Distribution of Rural Logistics

AbsrtactWith the progress of science and technology and the development of society, the popularity of online shopping has been greatly improved, and it provides a broad space for the circulation and sale of agricultural products, and promotes the development of new countryside in China. Despite the vigorous development of rural e-commerce, it is still in its infancy, and there are many problems such as imperfect logistics distribution system. Among them, the joint distribution mode of terminal logistics is an important factor restricting the development of rural e-commerce. Therefore, we should develop the logistics mode of common warehousing and distribution among logistics enterprises, and promote the integration of rural logistics warehousing and distribution resources through computer simulation of logistics distribution. This paper investigates the situation of rural express delivery, evaluates the performance of rural express terminal distribution by using the method of fuzzy comprehensive evaluation, and constructs a new mode of rural express delivery operation in view of related problems.

Next generation integrated smart manufacturing based on big data analytics, reinforced learning, and optimal routes planning methods

ABSTRACTIn this study, Big Data Analytics has been applied to implement smart manufacturing services performed by local commercial laundry Small and Medium Sized Enterprises (SMEs), which, to be specific, is called Smart Laundry Services (SLSs). This laundry service first uses traffic big data inputs from the City Traffic monitoring Server and abstract patterns to develop dynamic optimal scheduling routes for logistic terminals after taking pick-up orders from hotel supply managers. On the other hand, the laundry floor, which isfor fully automatic operation through integrated smart manufacturing with reinforced learning algorithms, will automatically classify the arriving orders according to the Radio Frequency Identification (RFID) tags on the orders, which contain data for each order such as material type, forbidden policies, detergent suggestion, and other relevant specifications. The orders are immediately processed and assigned to a laundry terminal with least waiting time and with optimal laundry parameters learned by the algorithm. The processed orders are then shipped back to the hotels by static scheduling routes with the optimal patterns estimated from real-time traffic big data. The entire operation, from initial orders to dropping back orders, are monitored by hotel supply managers and laundry SMEs’ personnel on their computers or mobile devices such as cell phones via an APP. As a result, this proposed integrated Smart Manufacturing architecture characterized with cutting-edge technologies such as big data analytics and intelligent production will make the commercial laundry SMEs more competitive in local markets.

Container Terminal Oriented Logistics Generalized Computational Complexity

The planning and scheduling of container terminal logistics systems (CTLS) are the multi-objective and multiple strong constraints combinatorial optimization challenges under the uncertain environments, and those are provided with high goal orientation, dynamics, context-sensitivity, coupling, timeliness, and complexity. The increasingly sophisticated decision-making for CTLS is one of the most pressing problems for the programming and optimization method available. This paper discusses CTLS in terms of logistics generalized computation complexity based on computational thinking, great principles of computing, and computational lens, which three are abbreviated with 3CTGPL, and makes a definition of container terminal oriented logistics generalized computational complexity (CTO-LGCC) and container terminal logistics generalized computation comprehensive performance perspective (CTL-GCCPP) from the dimensions of time, space, communication, processor, and memory access. Both can analyze, generalize, migrate, translate, localize, modificate, and evaluate the above-complicated problems and lay solid foundations and establish a feedback improvement framework for the computational model and scheduling algorithms of the CTLS, which is an essential complement to the modeling and optimization methodology and solutions to CTLS with computational logistics. Finally, aimed at the logistics service cases for a large-scale container terminal, the simulation is designed and implemented for different scheduling algorithms, and the qualitative and quantitative comprehensive analysis is executed for the concomitant CTO-LGCC that demonstrates and verifies the feasibility and credibility of the CTO-LGCC and CTL-GCCPP from the viewpoint of the practice of container terminal decision-making support on the tactical level.

Modelling and Simulation of a Physical Internet of Transportation Hubs

A Physical Internet of hubs (‘PI-hubs’) is a network of logistics terminals that allow shipments to flow through, similar to the flow of Internet data packets. PI-hub networks are a recent proposal and their properties have not been studied in detail. In this paper we model PI-hub networks using the theory of queuing networks, particularly, Jackson queuing networks. Under certain assumptions, PI-hub networks behave like Jackson queuing networks, and thus analytical methods can be used to calculate important properties such as throughputs, processing times, traffic size etc. In this paper we analyse the performance characteristics (throughput, queue size) for each PI-hub and for the whole PI-hub network.

Capital investments in the context of sustainable development of railways

Ukraine, due to its geographical position and developed transport infrastructure, has significant potential as a transit country, primarily in the supply chain of trade between Asia and Europe. According to the estimates of the British Institute for Transport Rendell Ukraine, the transit coefficient of Ukraine is 3,75 (with a maximum of 5); This is the best indicator among the countries of Europe (for comparison, in Poland, which occupies the second place, this indicator is 2,92).Nowadays, Ukraine uses its transit potential extremely insufficiently and inefficiently; over the past 10 years, the volume of transit traffic through the territory of Ukraine has declined by more than 2,5 times, primarily due to the fall in transit traffic by rail. One of the reasons for this situation is, of course, the general political and economic situation in the country. However, there are a number of other negative factors that prevent Ukraine from fully and effectively using its transit potential. These problems lie both in the purely technical plane (deterioration of transport infrastructure and rolling stock, lack of capacity of the main transport routes, underdevelopment of logistics terminals in the country and at land borders, difference in width with the European railway system) and the legislato-thick (high level of port dues, bureaucratization and regulation of customs procedures, lack of flexible tariff policy for carriers, etc.).

Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal

Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal.

Optimization of cold chain logistics distribution network terminal

By studying the development status of cold chain logistics in China, and the current situation in the three specific industries of electronics commerce, supermarkets, and the food and beverage industry, this paper analyzes and concludes that the terminal logistics distribution capacity of the regional distribution centers in Chinese cities is insufficient. In order to solve this problem—based on the idea of joint distribution—the paper makes a specific study of the terminal problem and establishes a joint distribution node. Different distribution schemes are adopted for individual, medium, and large customers, and we find that the semi-trailer is used to carry out the transportation of jammers by using mathematical modeling and time window constraint analysis, which can effectively integrate cold chain logistics resources, save logistics space and human resources, and improve terminal logistics distribution capacity.

A heterogeneous fleet two-echelon capacitated location-routing model for joint delivery arising in city logistics

This paper focuses on optimisation issue in designing urban logistics network for joint delivery alliances in parcel delivery industry, explicitly taking into account heterogeneous fleet and practices of joint delivery alliances in China. The objective is to determine the set of Intermediate Depots (IDs) to open and the allocation of city logistics terminals to minimise a total cost comprised of the set-up costs of IDs and the total variable cost of routes under joint delivery. A powerful cooperative approximation heuristic algorithm is developed and a comparative analysis is performed with three published approaches based on the well-known benchmark instances in this field. The results suggest that the proposed method can find good solutions in a reasonable amount of time for most data-sets. The proposed method has also been successfully applied to an industrial case for a joint delivery alliance in Chongqing, China. Comprehensively analysis are conducted between joint delivery and separate delivery, heterogeneous and homogeneous fleets, considering the carbon emissions or not, and demand variations.

Location characteristics and differentiation mechanism of logistics nodes and logistics enterprises based on points of interest (POI): A case study of Beijing

The logistics nodes and logistics enterprises are the core carriers and organizational subjects of the logistics space, and their location characteristics and differentiation strategies are of key importance to optimizing urban logistics spatial patterns and ensuring reasonable resource allocation. Based on Tencent Online Maps Platform from December 2014, 4396 logistics points of interest (POI) were collected in Beijing, China. By the methods of industrial concentration evaluation and kernel density analysis, the spatial distribution pattern of logistics in Beijing are explored, the interaction mechanism among the type difference, supply-demand side factors and location choice behavior are clarified, and the internal mechanism of spatial differentiation under the combined influence of transportation, land rent and assets are revealed. The following conclusions are drawn in the paper. (1) Logistics enterprises and logistics nodes exhibit the characteristic of both co-agglomeration and spatial separation in location, and logistics activities display the spatial pattern of "marginal area of downtown area, suburbs and exurban area", which have a weak coupling degree with logistics employment space. (2) The public logistics space, namely, logistics parks and logistics centers, is produced under the guidance of the government, and the terminal logistics space consisting of logistics distribution centers serving for the specific industries and terminal users is dominated by enterprises. The locational differentiation between the two modes of logistics space is significant. (3) In the formation of the logistics spatial location, the government can change the traffic condition by re-planning the transport routes and freight station locations, and control the land rent and availability of different areas by increasing or decreasing the land use of logistics, to impact the enterprise behavior and form different types of logistics space and function differentiation. In comparison, logistics enterprises meet the diverse demands of service objects through differentiation of asset allocation to promote the specialization of division and form the object differentiation of logistics space.