Feedback about (Blockchain OR ML) AND (logistics)

Title: Multi-agent deep learning for simultaneous optimization for time and energy in distributed routing system

Background

• Packet routing problem
• Current routing system only focuses on computer network
• 

Objectives

• Packet routing at heterogeneous environment

Methodology

• deep reinforcement learning
• link-state protocol
• preliminary supervised learning
• each router has its own neural network
• 
• 
• 
• 

Results

• outperform state-of-art routing algorithm

Implication

• More realistic

---

Title: An analysis of agent-based approaches to transport logistics

Background

• agent based approach for transportation and traffic management
• freight transportation

Objectives

• 

Methodology

• 

Results

• Agent based approach for modelling transport logistics is great

Implication

• can be applied in that domain

---

Title: Tortoise,not the hare: Digital transformation of supply chain business processes

Background

• rapid development of digital tech
• supply chain
• tech
  • robotic process automation
  • AI
  • blockchain
• 

Objectives

• promise and potential
• and suggestions to business

Methodology

• 14 interviews

Results

• (1) identify a supply chain technology visionary
• (2)develop a digital technology roadmap
• (3)update foundational information systems

Implication

• (1) identify a supply chain technology visionary
• (2)develop a digital technology roadmap
• (3)update foundational information systems

---

Title: Learning-based logistics planning and scheduling for crowdsourced parcel delivery

Background

• AI
• Logistics
  • 

Objectives

• crowd sourced parcel delivery problem
• complex and practical cases, such as
  • multiple delivery vehicles
  • just-in-time (JIT)
  • pick up and delivery
  • minimum fuel consumption
  • maximum profitability

Methodology

• For this we suggest a learning-based logistics planning and scheduling (LLPS) algorithm that controls admission of order requests and schedules the routes of multiple vehicles altogether.
  • 
• For the admission control, we utilize reinforcement learning (RL) with a function approximation using an artificial neural network (ANN).
• Also, we use a continuous-variable feedback control algorithm to schedule routes that minimize both JIT penalty and fuel consumption.

Results

• Computational experiments show that the LLPS outperforms other similar approaches by 32% on average in terms of average reward earned from each delivery order.
• In addition, the LLPS is even more advantageous when the rate of order arrivals is high and the number of vehicles that transport parcels is low.

Implication

• Computational experiments show that the LLPS outperforms other similar approaches by 32% on average in terms of average reward earned from each delivery order.
• In addition, the LLPS is even more advantageous when the rate of order arrivals is high and the number of vehicles that transport parcels is low.

---

Title: Machine learning in agent-based stochastic simulation: Inferential theory and evaluation in transportation logistics

Background

• Multiagent-based simulation is an approach to realize stochastic simulation where both the behavior of the modeled multiagent system and dynamic aspects of its environment are implemented with autonomous agents.
• Such simulation provides an ideal environment for intelligent agents to learn to perform their tasks before being deployed in a real-world environment.

Objectives

• theoretical and practical aspects of learning by autonomous agents within stochastic agent-based simulation
  • which describes learning processes from the perspective of a learner’s goal as a search through knowledge space.

Methodology

• Inferential Theory of Learning
• The theory is extended for approximate and probabilistic learning to account for the situations encountered when learning in stochastic environments.

Results

• Practical aspects are exemplified by two use cases in autonomous logistics: learning predictive models for environment conditions in the future, and learning in the context of evolutionary plan optimization.

Implication

• Practical aspects are exemplified by two use cases in autonomous logistics: learning predictive models for environment conditions in the future, and learning in the context of evolutionary plan optimization.

---

Title: The strategic role of logistics in the industry 4.0 era

Background

• By leveraging new technologies (Additive Manufacturing, Advanced Robotics, Artificial Intelligence, Autonomous Vehicles, Blockchain, Drones, Internet of Things, etc.),
• many companies are developing cyber-physical systems that can change the competition landscape.

Objectives

• In the midst of this exciting development, we examine the strategic role of logistics and transportation services for creating economic, environmental and social values
• Also, we discuss some new research direction

Methodology

• 
• 
• 

Results

• In the midst of this exciting development, we examine the strategic role of logistics and transportation services for creating economic, environmental and social values
• Also, we discuss some new research direction

Implication

• In the midst of this exciting development, we examine the strategic role of logistics and transportation services for creating economic, environmental and social values
• Also, we discuss some new research direction

---

Title: Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach

Background

• With the rise of “cross-border-e-commerce”, the third-party-forwarding-logistics (3PFL) service becomes increasingly popular.
• Different from the traditional third-party-logistics (3PL) service, the 3PFL company provides forwarding services cost-effectively by consolidating orders from different e-tailers/platforms.
• 

Objectives

• The random arrivals of orders create a big challenge.

Methodology

• a deep learning based one-step integration optimal decision making approach S2SCL(Seq2Seq based CNN-LSTM)
• 
• intelligently integrates inventory optimization and demand-forecasting process
• The Seq2Seq based forecasting architecture, which integrates CNN and LSTM network, is able to model the system dynamics and dependency-relations in varying demand for logistics services. Besides generating the point forecasting results, the proposed approach can quantify demand uncertainty via a dynamic distribution and make optimal decision on logistics service capacity allocation.

Results

• Through a casestudy analysis with real data obtained from a 3PFL company in China’s Great Bay Area, we compare the proposed S2SCL with two benchmark models, including a one-step statistics based integration approach ARIMA and a two-step optimization based approach PSO-ELM, for two tasks: (1) point forecasting and (2) optimal logistic service capacity (LSC) allocation.
• 

Implication

• Experimental results show that S2SCL outperforms the two benchmark models in both tasks significantly

---

Title: Applications of smart technologies in logistics and transport: A review

Background

• The emergence of smart technologies (STs) is inducing significant transformation in logistics and transport nowadays. STs refer to the applications of artificial intelligence and data science technologies, such as machine learning, big data, to create cognitive awareness (autonomous) of an object with the support of information and communication technologies such as IoT and Blockchain.
• Currently, many applications of STs have demonstrated potential promise in enhancing the efficiency and effectiveness in various logistics operations and transportation systems.
• Further, these new advanced technologies create huge modelling challenges to traditional optimization approaches and thus create rich new research opportunities for developing new optimization methodologies in the field of logistics and transport studies.

Objectives

• As such, our aim is to conduct a comprehensive review on noteworthy contributions made in the applications of STs in improving logistics operations and transportation network efficiency.
• More importantly, we explore and discuss the technical difficulties encountered by researchers in the development of optimization methodologies caused by the applications of STs.
• Finally, we conclude the studies with suggestions for future research.

Methodology

• review
• 
• 
• 
• 
• 

Results

Implication

---

Title: A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems

Background

• Cyber-Physical Production Systems (CPPSs) are complex manufacturing systems which aim to integrate
  and synchronize machine world and manufacturing facility to the cyber computational space.
• 

Objectives

• However, having intensive interconnectivity and a computational platform is crucial for real-world implementation
  of CPPSs.

Methodology

• In this paper, the potential impacts of blockchain technology in development and realization of
  real-world CPPSs are discussed.
• A unified three-level blockchain architecture is proposed as a guideline
  for researchers and industries to clearly identify the potentials of blockchain and adapt, develop, and
  incorporate this technology with their manufacturing developments towards Industry 4.0.
• 

Results

• In this paper, the potential impacts of blockchain technology in development and realization of
  real-world CPPSs are discussed.
• A unified three-level blockchain architecture is proposed as a guideline
  for researchers and industries to clearly identify the potentials of blockchain and adapt, develop, and
  incorporate this technology with their manufacturing developments towards Industry 4.0.

Implication

• In this paper, the potential impacts of blockchain technology in development and realization of
  real-world CPPSs are discussed.
• A unified three-level blockchain architecture is proposed as a guideline
  for researchers and industries to clearly identify the potentials of blockchain and adapt, develop, and
  incorporate this technology with their manufacturing developments towards Industry 4.0.

---

Title: Machine learning and multi-agent systems in oil and gas industry applications: A survey

Background

• The oil and gas industry (OGI) has always been associated with challenges and complexities. It involves many processes and stakeholders, each generating a huge amount of data.

Objectives

• This paper aims to summarise the efforts to date of applying ML and MAS to OGI tasks, identify possible reasons for their low and slow uptake and suggest ways to ensure a greater adoption of these technologies in the OGI.
• Due to the global and distributed nature of the business, processing and managing this information is an arduous task.
• Many issues such as orchestrating different data sources, owners and formats; verifying, validating and securing data streams as they move along the complex business process pipeline; and getting insights from data for improving business efficiency, scheduling maintenance and preventing theft and fraud are to be addressed.

Methodology

• Artificial intelligence (AI), and machine learning (ML) in particular, have gained huge acceptance in many areas recently, including the OGI, to help humans tackle such complex tasks.
• Furthermore, multi-agent systems (MAS) as a sub-field of distributed AI meet the requirement of distributed systems and have been utilised successfully in a vast variety of disciplines.
• Several studies have explored the use of ML and MAS to increase operational efficiency, manage supply chain and solve various production- and maintenance-related tasks in the OGI.
• However, ML has only been applied to isolated tasks, and while MAS have yielded good performance in simulated environments, they have not gained the expected popularity among oil and gas companies yet.

Results

• Further research in the fields is necessary to realise the potential of ML and MAS and encourage their wider acceptance in the OGI.
• In particular, embedding ML into MAS can bring many benefits for the future development of the industry.

Implication

• Further research in the fields is necessary to realise the potential of ML and MAS and encourage their wider acceptance in the OGI.
• In particular, embedding ML into MAS can bring many benefits for the future development of the industry.

---

Title: Evaluating city logistics measure in e-commerce with multi- agent systems

Background

• This approach seeks to represent the behaviour of each stakeholder involved in the delivery of goods between producers and customers.
• 

Objectives

• This paper presents a multi-agent systems (MAS) model to evaluate City Logistics measure for an urban road network in an e-commerce delivery system environment.

Methodology

• 
• 

Results

• The preliminary results of the model shows that Government-driven City Logistics measures such as freight vehicle road pricing has the potential of reducing truck emission when the administrator learns and price the road links.

Implication

• Most notable contribution of this evaluation methodology is the combination of vehicle routing and scheduling problem with time window (VRPTW), auction theory and reinforcement learning in a multi-agent framework.

---

Title: Development of a decision support tool for sustainable urban logistics optimization logistics oa,bptimization

Background

• Traffic flows

• Congestions

  • pollution

• 

Objectives

• sustainable alternative solution to road transportation

Methodology

• Co-creation methodology
  • stakeholders
    • Local authorities
    • companies
    • citizens
  • alternative solution to road transportation
• 
• 

Results

• decision aided tool for
  • simulating
  • optimizing
• road transportation

Implication

• sustainable alternative solution to road transportation

---

Title: Convergence of blockchain and artificial intelligence in IoT network for the T sustainable smart city

Background

• In the digital era, the smart city can become an intelligent society by utilizing advances in emerging technol- ogies. Specifically, the rapid adoption of blockchain technology has led a paradigm shift to a new digital smart city ecosystem. A broad spectrum of blockchain applications promise solutions for problems in areas ranging from risk management and financial services to cryptocurrency, and from the Internet of Things (IoT) to public and social services. Furthermore, the convergence of Artificial Intelligence (AI) and blockchain technology is revolutionizing the smart city network architecture to build sustainable ecosystems.
• 
• 
• 

Objectives

• However, these advance- ments in technologies bring both opportunities and challenges when it comes to achieving the goals of creating a sustainable smart cities.
• This paper provides a comprehensive literature review of the security issues and pro- blems that impact the deployment of blockchain systems in smart cities.

Methodology

• This work presents a detailed discussion of several key factors for the convergence of Blockchain and AI technologies that will help form a sustainable smart society.
• We discuss blockchain security enhancement solutions, summarizing the key points that can be used for developing various blockchain-AI based intelligent transportation systems.

Results

• Also, we discuss the issues that remain open and our future research direction, this includes new security suggestions and future guidelines for a sustainable smart city ecosystem.

Implication

• Also, we discuss the issues that remain open and our future research direction, this includes new security suggestions and future guidelines for a sustainable smart city ecosystem.

---

Title: TRENDS IN BLOCKCHAIN AND NEWER TECHNOLOGIES USES IN LOGISTICS AND SUPPLY CHAIN MANAGEMENT

Background

• Modern logistics and supply chain management use customer relations management software (CRM) to manage customers, vendors, manufacturing, sales force marketing and demand estimates using mobile connected tools. The mobile phone and hand- held radio frequency identification (RFID) readers can connect with CRM system with virtually no delay to share data and information.
• These efficiencies bring complete and accurate information in real-time to make necessary transactions, inventory and manufacturing schedules status and adjustments. The latest trends of information technology that is impacting logistics and supply chain management are blockchain transaction processing, artificial intelligence (AI), internet of things (IoT), autonomous vehicles and drones, and advanced analytics.
• 
• 
• 

Objectives

• Selecting proper technology for logistics and supply chains operations or sub-processes is very critical for business enterprise to gain competitive advantage in today’s competitive market.

Methodology

• The paper will explore the impact of these new technologies with emphasis on blockchain potential to improve logistics and supply chain management to make these important sectors of the economy more efficient, effective and profitable.
• 

Results

• The paper will explore the impact of these new technologies with emphasis on blockchain potential to improve logistics and supply chain management to make these important sectors of the economy more efficient, effective and profitable.
• 
• 

Implication

• The paper will explore the impact of these new technologies with emphasis on blockchain potential to improve logistics and supply chain management to make these important sectors of the economy more efficient, effective and profitable.

---

Title: Artificial Intelligence and Data Science in the Automotive Industry

Background

• Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future.

Objectives

• This article defines the terms “data science” (also referred to as “data analytics”) and “machine learning” and how they are related. In addition, it defines the term “optimizing analytics“ and illustrates the role of automatic optimization as a key technology in combination with data analytics. It also uses examples to explain the way that these technologies are currently being used in the automotive industry on the basis of the major subprocesses in the automotive value chain (development, procurement; logistics, production, marketing, sales and after-sales, connected customer). Since the industry is just starting to explore the broad range of potential uses for these technologies, visionary application examples are used to illustrate the revolutionary possibilities that they offer. Finally, the article demonstrates how these technologies can make the automotive industry more efficient and enhance its customer focus throughout all its operations and activities, extending from the product and its development process to the customers and their connection to the product.

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Implication

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