Real-time Logistics Research Articles

Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery.

Ant colony optimization (ACO) algorithm is widely used in the instant delivery order scheduling because of its distributed computing capability. However, the order delivery efficiency decreases when different logistics statuses are faced. In order to improve the performance of ACO, an adaptive ACO algorithm based on real-time logistics features (AACO-RTLFs) is proposed. First, features are extracted from the event dimension, spatial dimension, and time dimension of the instant delivery to describe the real-time logistics status. Five key factors are further selected from the above three features to assist in problem modeling and ACO designing. Second, an adaptive instant delivery model is built considering the customer's acceptable delivery time. The acceptable time is calculated by emergency order mark and weather conditions in the event dimension feature. Third, an adaptive ACO algorithm is proposed to obtain the instant delivery order schedules. The parameters of the probability equation in ACO are adjusted according to the extracted key factors. Finally, the Gurobi solver in Python is used to perform numerical experiments on the classical datasets to verify the effectiveness of the instant delivery model. The proposed AACO-RTLF algorithm shows its advantages in instant delivery order scheduling when compared to the other state-of-the-art algorithms.

Logistics distribution model and storage planning design based on multi-source information positioning in smart city development

The research mainly focuses on the inefficient planning of autonomous distribution and storage modes of distribution vehicles in smart city logistics and distribution, which in turn leads to poor customer experience, rising distribution costs, and wasted distribution resources. From the perspective of information processing in the logistics distribution process, the study takes multi-angle and multi-source information collection and fusion processing strategy as the main basis to help smart delivery robots realize map construction and autonomous positioning in the distribution process, and then facilitate real-time logistics distribution and storage mode planning by robots in combination with their own states. The research results show that the algorithm accuracy, standard error, and average running time of the extended Kalman filter localization model designed in the study are 0.96, 1.52, and 105s, respectively, with the algorithm accuracy being the highest and the other two values being the lowest in its class. Meanwhile, in the simulation logistics planning, the research-designed logistics planning model has the strongest information capturing ability, and the planning of distribution routes and storage modes is more reasonable, which can provide more efficient autonomous planning solutions.

Eksplorasi Potensi Penggunaan Blockchain Dalam Optimalisasi Manajemen Pelabuhan di Indonesia: Tinjauan Literatur

This research explores the potential use of Blockchain technology to optimize port management in Indonesia, which is a crucial element in the global supply chain. The research method employed is a literature review, with analysis and synthesis of information to understand Blockchain's contribution to improving port management. The identified potential uses of Blockchain include real-time logistics tracking and monitoring, data validation and authentication, increased transparency and trust, and port process efficiency. The study also highlights the use of smart technology and smart contracts in port management, as well as challenges in adopting Blockchain technology. A comprehensive conceptual framework is proposed for the application of Blockchain in Indonesia's maritime industry, including a feasibility analysis framework to assess implementation readiness. The results of this literature review are expected to provide deep insights and serve as a foundation for further research and assist stakeholders in making decisions related to the application of Blockchain technology in port management.

Learning from data to improve asphalt road construction processes

Asphalt road construction projects pose a lot of challenges in different disciplines, from the choice of construction methods, type and quantity of machines and personnel, pre-planning of all processes, ordering material from asphalt mixing plants, real-time logistics of handling material transport, and the asphalt paving and compaction process itself. Depending on the complexity and size of the construction site, preparation and execution can be influenced by a lot of unknown variables, like weather impact, material properties of the delivered asphalt or delays in the deliveries themselves. These circumstances might lead to irregularities in the asphalt material temperatures and thus suboptimal conditions for compaction or even construction stoppage - in any case, they lead to a reduction in quality of the built asphalt road. In recent years, a lot of mobile devices, sensors and software have been emerging as viable solutions in the construction industry to digitize and support these processes. Those real-time applications allow deeper insight into logistics or material properties on site and acquire data for downstream analysis. These endeavours in process optimization now benefit construction companies to increase efficiency, and for work safety and environmental aspects, it is crucial to optimize energy and CO2 consumption. Improved logistics and building roads with low-temperature asphalt will have to become a standard in road construction. The present study gives an overview of the digitalized processes in asphalt road construction with market-available solutions focusing on logistics data of the material delivery chain from mixing plant to paver, thermal imaging data of the built asphalt to validate asphalt temperatures, as well as machine and location data from asphalt paver and compaction rollers.

Internal Integration and Operational Performance of Kenya’s Public Hospitals

The purpose of this paper was to determine the influence of Internal Integration and operational performance in Kenya’s level five public hospitals, as well as the moderating effect of public procurement to the relationship. The study adopted causal and cross-sectional study design to examine the relationship between internal integration, operational performance using empirical data. One hundred sixty-four respondents drawn from the level five public hospitals participated in the research. These were selected from supply chain key personnel working in the supply by applying simple random sampling. Data was collected through a structured questionnaire that contained both open-ended and closed-ended questions and analysed. The results show a significant relationship between internal integration and operational performance (β1=0.286, p<0.05) with an R-square of 0.421, implying that 42.1% of the variation in the operational performance in level five public hospitals is attributed to changes in internal integration through responsiveness, integrated system, and real-time inventory management, online integration with internal customers, suppliers and warehouse, cross-functional teams, real-time logistics information, functional planning meetings, team building, and regular briefings. Public procurement was also found to significantly moderate the relationship between internal integration and operational performance (β2=0.378, p<0.05) of the level five public hospitals.The study recommends robust and effective internal integration by strengthening the facilities capabilities through information sharing, structuring of internal operations and process integration before they can engage in meaningful external integration.

Factors influencing crowdsourcing riders’ satisfaction based on online comments on real-time logistics platform

ABSTRACT Real-time logistics (RTL), which is mainly organized by crowdsourcing, has grown rapidly in recent years. Crowdsourcing riders are the main undertakers of RTL. This paper uses crowdsourcing riders’ online comments as data sources, and uses text mining techniques such as sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling to analyze the factors that bring satisfaction and dissatisfaction to riders. The research results show that in addition to basic income, riders expect the platform to provide them with better services, skills training and safety insurance before work can bring satisfaction to riders. The lack of timely information feedback on the current platform and inaccurate order matching are the reasons for the dissatisfaction of riders. Research also shows that riders can easily gain a sense of accomplishment to help others in the process of completing RTL distribution. Interactions with merchants and customers will also affect riders’ satisfaction.

Offline approximate value iteration for dynamic solutions to the multivehicle routing problem with stochastic demand

The multivehicle routing problem with stochastic demand (MVRPSD) is an important issue both in theory and practice. However, solving the MVRPSD through traditional methods, such as a priori optimization or rollout-algorithm-based dynamic programming is generally limited by the issues of computation efficiency and solution quality. Under increasing demand for efficient real-time logistics, we propose a novel offline approximate value iteration (OAVI) algorithm for dynamic solutions to the MVRPSD. Our algorithm benefits from offline training and thus can provide fast and effective online dynamic routing solutions. Adopting such a novel and effective algorithm presents two challenges: first, we must define a proper cost structure for the dynamic routing decision; second, we must efficiently address the curse of dimensionality of the multivehicle problem. To solve these problems, we first describe the cost structure through the value function approximation (VFA) with basis functions involving a priori cost and a priori credibility; we then design two strategies, recourse reduction (RR) and neighborhood reduction (NR), to prune the action space. The numerical experiments show that our algorithm can substantially enhance computation efficiency and solution quality compared to traditional methods.

DBNTFPO: ANN-Based Approach for Logistics Distribution Optimization

This research aims to improve the optimization ability of traditional algorithms under complex urban road conditions. Henceforth, in this paper, the logistics distribution path optimization problem model is established. Each part of the model is introduced in detail, the weight update of the ant algorithm is introduced, the problem of unreasonable road parameters set by the ant algorithm is solved, and the Deep Belief Network Traffic Forecast Path Optimization (DBNTFPO) algorithm is proposed. The related applications of deep learning technology are analyzed, and the relationship between deep learning technology and real-time distribution vehicle routing problem is discussed. Finally, the challenges brought by the real-time logistics distribution path optimization problem to deep learning are introduced. Finally, the effectiveness and feasibility of the algorithm in the actual logistics distribution are demonstrated through an example analysis.

Implementation of Real-Time Logistics with Transfer Learning at a Small Start-Up Enterprise in the Construction Sector

Implementation of Real-Time Logistics with Transfer Learning at a Small Start-Up Enterprise in the Construction Sector

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

PurposeThis paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.Design/methodology/approachIn this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.FindingsWith the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.Research limitations/implicationsThe threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.Practical implicationsIt can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.Originality/valueThe classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

Digital Twin for Supply Chain Coordination in Modular Construction

Over the past decades, the construction industry has been attracted to modular construction because of its benefits for reduced project scheduling and costs. However, schedule deviation risks in the logistics process of modular construction can derail its benefits and thus interfere with its widespread application. To address this issue, we aim to develop a digital twin framework for real-time logistics simulation, which can predict potential logistics risks and accurate module arrival time. The digital twin, a virtual replica of the physical module, updates its virtual asset based on building information modeling (BIM) in near real-time using internet of thing (IoT) sensors. Then, the virtual asset is transferred and exploited for logistics simulation in a geographic information system (GIS)-based routing application. We tested this framework in a case project where modules are manufactured at a factory, delivered to the site via a truck, and assembled onsite. The results show that potential logistical risks and accurate module arrival time can be detected via the suggested digital twin framework. This paper’s primary contribution is the development of a framework that mediates IoT, BIM, and GIS for reliable simulation which predicts potential logistics risks and accurate module delivery time. Such reliable risk prediction enables effective supply chain coordination, which can improve project performance and the widespread application of modular construction.

Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0

When studying the vehicle routing problem, especially for on-time arrivals, the determination of travel time plays a decisive role in the optimization of logistics companies. Traffic Internet of Things (IoT) connects ubiquitous devices and collects data from various channels like traffic cameras, vehicle detectors, GPS, sensors, etc. that can be used to analyze real-time traffic status and eventually increase the efficiency of logistics management for Logistics 4.0. However, big IoT data contain joint features that interact non-linearly and complicatedly, thus increasing the stochastic nature and difficulty of determining travel time on real-time basis. This research proposes a novel method (named the gradient boosting partitioned regression tree model) to forecast travel time based on big data collected from the industrial IoT infrastructure. The proposed method separates the global regression tree model based on the gradient boosting decision tree into several partitions to capture the time-varying features simultaneously – that is, to subdivide the non-linearity into fragments and to characterize the feature interactions in a manageable way with recursive partitions. We illustrate several analytical properties with manageable advantages in terms of big data analytics of the proposed method and apply it to real traffic IoT data. Findings of this research show that the proposed method performs successfully at enhancing the predictive accuracy of travel time after empirically comparing it with other computational methods.

Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

The next nine minutes: Lessons learned from the large-scale active shooter training prior to the STEM school shooting.

As the incidence of active shooters increase, local emergency response has also changed. South Metro Fire Rescue coordinated a series of hyper-realistic active shooter simulation drills involving multiple agencies. "The Next Nine Minutes" was one of the largest active shooter drills performed to date with 904 personnel that were trained in 18 mass casualty active shooter drills. Evaluation was from point of injury to and including care in the operating room (OR), and evaluation of real-time system logistics. A total of 126 patients in Cut Suits® received a total of 479 procedures such as needle decompressions, cricothyrotomies, tourniquets, wound packs, and chest tubes. Central to this exercise, law enforcement (LE) established a warm zone from the initial shooting. EMS was able to move into the facility, locate casualties, extract the first victim, move them to a casualty collection point (CCP), and transport them to safety within 12 minutes. Strengths and weaknesses were identified in prehospital and in-hospital care. These included what roles agencies play in a true event, specific timing in establishing areas such as the warm zone and CCP, transportation, and logistics at the accepting hospitals. Only after the barriers to success were identified and addressed did the timing of casualty movement drastically improve. Lessons learned from this training were ultimately used to save lives at the STEM School, Highlands Ranch, and Colorado Shooting. This in situ immersion training should be practiced as a whole system.

Robust facility location decisions for resilient sustainable supply chain performance in the face of disruptions

PurposeFacility location and re-location decisions are critical managerial decisions in modern supply chains. Such decisions are difficult in this environment as managers encounter uncertainty and risks. The study investigates establishing or moving distribution facilities in the global supply chain by considering costs, fulfilment, trade uncertainties, risks under environmental trade-offs and disruptive technologies.Design/methodology/approachThis paper combines the possibilities and probabilistic scenarios for a supply chain network by proposing the novel Robust Optimisation and Mixed Integer Linear Programming (ROMILP) method developed under the potential uncertainty of demand while considering the costs associated with a four-tier supply chain network. ROMILP has been solved in a real-time logistics environment by applying a case study approach.FindingsThe solution is obtained using an exact solution approach and provides optimality in all tested market scenarios along the proposed global logistics corridor. A sensitivity analysis examines potential facility location scenarios in a global supply chain context.Research limitations/implicationsLogistics managers can apply the ROMILP model to test the cost-benefit trade-offs against their facility location and relocation decisions while operating under uncertainty. Future research is proposed to extend the literature by applying data from the OBOR logistics corridor.Originality/valueThis study is the first to examine sustainable dimensions along the global logistics corridor and investigate the global container traffic perspective. The study also adds value to the Middle East logistics corridor regarding facility location decisions.

Digital connectivity in an innovative joint distribution system with real-time demand update

A joint distribution system based on digital connectivity to share logistics resources constitutes an innovative method of intelligent logistics. In general, O2O deliverymen are subjected to delivery capacity imbalances in different hours—peak hours and off-peak hours in particular—throughout the day. To solve the insufficient capacity problem in peak hours and the idle capacity problem in off-peak hours of O2O deliverymen, this paper introduces an innovative digital connectivity enabled B2C and O2O joint distribution system. In this system, all pickup and delivery nodes of both B2C and O2O orders are interconnected through the Physical Internet. In response to the dynamic arrivals of O2O orders, this paper develops a center dispatching system to collect and schedule the real-time logistics information via digital connectivity. An improved savings algorithm is proposed to support the real-time routing optimization of deliverymen. By making a comparative analysis with the traditional separate distribution mode, this paper proves the effectiveness of the joint distribution system. Results show that it outputs better optimization with larger order sizes, the same B2C and O2O order proportions as well as longer lengths of O2O time window. With the benefit of the digital connectivity, the joint distribution system contributes to providing real-time logistics services with high efficiency and reducing carbon emissions to realize sustainability. The prospect of this system exhibits the potential for use in richly varied delivery services, most notably meal delivery in urban areas.

Low-Cost Real-Time Logistics Tracking System with Mobile Application

Low-Cost Real-Time Logistics Tracking System with Mobile Application

Smart Connected Sensors, Cyber-Physical Networks, and Big Data Analytics Systems in Internet of Things-based Real-Time Production Logistics

Smart Connected Sensors, Cyber-Physical Networks, and Big Data Analytics Systems in Internet of Things-based Real-Time Production Logistics

Active acquisition system of LBS-based logistics freight source information

In order to solve the bottleneck of logistics information exchange and meet the demand for getting freight source information in time, this paper proposes a solution of active acquisition system of logistics sources information based on LBS with Android mobile terminal. By using GPS/WIFI positioning technology to achieve the acquisition of the information of user's current location and through wireless network, the data are further sent to the spatialtemporal database of service terminal, where the spatial data and attribute data about logistics sources information are integrated and stored. Then these data are analysed and processed by use of the dynamic programming algorithm based on real-time logistics sources information. Finally the Baidu API is used to realise the visualisation of post-processing of logistics sources information so that the services in active acquisition of peripheral logistic information and optimal receiving route planning might be provided.

An IoT-enabled Real-time Logistics System for A Third Party Company: A Case Study

With the boom of E-commerce in recent years, the third-party logistics (3PL) gains much favor for its large scope of service provided and economy of scale. Meanwhile, in the face of oceans of parcels to deliver, it casts requests on the efficiency of warehouse management to meet the need of customers. However, most companies conduct warehousing operations still relying on the paper for guiding and recording, and information is rarely shared even in the same company. Besides, few big companies are pursuing an automatic system to replace manpower, but in effect, the system is too mass and complicated to be stable and robust in usage. In this study, an IoT-enabled cyber-physical system assisted with wearable devices and wireless communication technologies, like RFID and Bluetooth, has been developed to cooperate with kinds of stakeholders to achieve higher operational efficiency. It has been deployed in a 3PL automotive-parts company at the Great Bay Area in China as a case study. Their nationally distributed warehouses realized real-time synchronizations of operations and information sharing within the company. Moreover, it not only highlighted the sustainability by nearly removing the paper in operations but also enhanced visibility and traceability throughout the whole process. The framework of the system and workflow proposed here can be duplicated or adjusted by other enterprises who share similar features. The success of this practice may motivate more 3PL companies to adopt cutting-edge technologies to create effective solutions for warehouse management.