Routing and scheduling of intelligent autonomous vehicles in industrial logistics systems

Abosuliman, Shougi Suliman; Almagrabi, Alaa Omran

doi:10.1007/s00500-021-05633-4

Cited by 26 publications

(18 citation statements)

References 32 publications

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“…Additionally, context-aware routing can enhance safety by avoiding high-risk areas and providing real-time updates on road conditions. Overall, context-aware routing [34][35][36] can improve the reliability and effectiveness of industrial transport systems. Based on the identified problems in context-aware routing for industrial transport systems, the following research objectives have been formulated: 1.…”

Section: Research Gapsmentioning

confidence: 99%

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

Nagappan,

Maheswari,

Siva

et al. 2023

Int J Communication

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SummaryIndustrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.

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Section: Research Gapsmentioning

confidence: 99%

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

Nagappan,

Maheswari,

Siva

et al. 2023

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In the context of logistics, the authors in [64] have proposed an IoT-assisted intelligent logistics transportation management framework to design an optimized logistics plan, improve customer service and reduce transportation costs. The authors focused on identifying the optimal routes for the directed autonomous vehicle, considering different vehicles.'…”

Section: Overview Of Smart Transportation Industrymentioning

confidence: 99%

“…To assess the potential risks within the decentralized logistics manufacturing delivery process (e.g., whether it is harmful to the products such as medicines and frozen food), a prediction is needed to estimate how long a refrigerated truck will require to arrive at one or more processing plants. The proposed framework can incorporate blockchain with AI and DTs to predict the potential risks in advance and then take the appropriate action like routing redirection [64]. On the other hand, the proposed framework can improve the secure delivery process by reducing fraud and theft issues.…”

mentioning

confidence: 99%

Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case

et al. 2021

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Digital twins (DTs) is a promising technology in the revolution of the industry and essential for Industry 4.0. DTs play a vital role in improving distributed manufacturing, providing up-to-date operational data representation of physical assets, supporting decision-making, and avoiding the potential risks in distributed manufacturing systems. Furthermore, DTs need to collaborate within distributed manufacturing systems to predict the risks and reach consensus-based decision-making. However, DTs collaboration suffers from single failure due to attack and connection in a centralized manner, data interoperability, authentication, and scalability. To overcome the above challenges, we have discussed the major high-level requirements for the DTs collaboration. Then, we have proposed a conceptual framework to fulfill the DTs collaboration requirements by using the combination of blockchain, predictive analysis techniques, and DTs technologies. The proposed framework aims to empower more intelligence DTs based on blockchain technology. In particular, we propose a concrete ledger-based collaborative DTs framework that focuses on real-time operational data analytics and distributed consensus algorithms. Furthermore, we describe how the conceptual framework can be applied using smart transportation system use cases, i.e., smart logistics and railway predictive maintenance. Finally, we highlighted the future direction to guide interested researchers in this interesting area.

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“…The solution of VRP is a group of optimal routes; those routes start and end in the hub, satisfying demands by serving customers. The process requires route selection, estimating alternate routes, and predicting driving time based on criteria variables, objective functions, and constraints [10].…”

Section: Introductionmentioning

confidence: 99%

“…Further-more, cloud computing increases supply chain efficiency through increased visibility and collaboration in order processing, inventory management, warehousing, and logistics [15]. The assimilation of Internet of Things (IoT) to cloud-based system enables dynamic management of vehicle routing operations by inter-communication, connection, and action among various supply chain components [10] as proposed in Figure 1. In the retail industry, artificial intelligence (AI) applications are increasing in various areas of ordering, processing, and transporting goods [16].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh

et al. 2021

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Background: Retail chains aim to maintain a competitive advantage by ensuring product availability and fulfilling customer demand on-time. However, inefficient scheduling and vehicle routing from the distribution center may cause delivery delays and, thus, stock-outs on the store shelves. Therefore, optimization of vehicle routing can play a vital role in fulfilling customer demand. Methods: In this research, a case study is formulated for a chain of retail stores in Dhaka City, Bangladesh. Orders from various stores are combined, grouped, and scheduled for Region-1 and Region-2 of Dhaka City. The ‘vehicle routing add-on’ feature of Google Sheets is used for scheduling and navigation. An android application, Intelligent Route Optimizer, is developed using the shortest path first algorithm based on the Dijkstra algorithm. The vehicle navigation scheme is programmed to change the direction according to the shortest possible path in the google map generated by the intelligent routing optimizer. Results: With the application, the improvement of optimization results is evident from the reductions of traveled distance (8.1% and 12.2%) and time (20.2% and 15.0%) in Region-1 and Region-2, respectively. Conclusions: A smartphone-based application is developed to improve the distribution plan. It can be utilized for an intelligent vehicle routing system to respond to real-time traffic; hence, the overall replenishment process will be improved.

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Routing and scheduling of intelligent autonomous vehicles in industrial logistics systems

Cited by 26 publications

References 32 publications

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case

Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh

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