A real-time Decision Support System for Big Data Analytic: A case of Dynamic Vehicle Routing Problems

Sbai, Ines; Krichen, Saoussen

doi:10.1016/j.procs.2020.09.089

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…An improved RTR algorithm and plug-in real-time path planning algorithm [48] NSGA-III [49] Simulated Annealing (SA) algorithm [44] Parallel Sparks Genetic Algorithm [50] A two-stage approach [39]…”

Section: Rtvrpmentioning

confidence: 99%

Review of research on vehicle routing problems

Hao,

Mao,

Wei

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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With the rise of the social logistics industry, the vehicle routing problem has always been a hot topic for scholars to study. This paper focuses on the recent research advances in the last 5 years of the vehicle routing problem, which contains the basic vehicle routing problem (VRP), vehicle routing problem with capacity constraints (CVRP), vehicle routing problem with time windows (VRPTW), split delivery vehicle routing problem (SDVRP), dynamic vehicle routing problem (DVRP), other types of VRPs, and so on. By subdividing the above according to VRP characteristics and describing its solution method. Finally, future research trends and development trends of VRP are summarized.

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Section: Rtvrpmentioning

confidence: 99%

Review of research on vehicle routing problems

Hao,

Mao,

Wei

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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show abstract

“…When it is at the front and middle end of the process, the vehicle needs to meet the requirements of the IoT customer, collect the goods in order and subsequently deliver them uniformly to the collection point. Online shopping is a common IoT model, individual users (IoT users) use smart devices to make online orders, followed by cooperation between merchants and logistics companies to achieve product pickup and transportation, in the process, the product transportation status will change in real time according to the DD of IoT users, which is the dynamic vehicle scheduling (Dynamic vehicle scheduling Problem (DVSP) process [14]. The DVRPTW is shown in Figure 3.1.…”

Section: Vehicle Path Modeling For Iot Customer Ddmentioning

confidence: 99%

Time Window Oriented IoT Vehicle Pathway Study for the Dynamically Changing Needs of E-Commerce Customers

Chen

2023

SCPE

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The main dynamic truck routing problem also presents a significant difficulty in the logistics sector, which is an unavoidable development trend of the contemporary technological changing society. A dynamic vehicle routing problem with time window model is suggested by the study in order to establish an effective and low-energy dynamic response method. The fundamental concept is to disrupt the conventional strategy of static dynamic consumers responding in time slots by dividing the dynamic time window into a static time window with several time slice intervals. The study makes use of cutting-edge ideas including dynamic attitude, before-and-after time slicing, and continuous optimisation while proposing a new method for model solution to optimise dynamic vehicle route issues effectively and affordably. The study employs the Solomon optimisation dataset and runs simulation studies on the Java platform to confirm its efficacy. The experimental findings demonstrated that the optimisation technique employed in the study reduced the cost of travelling by 83.8 miles while also considerably increasing the average vehicle utilisation by 3.6%. Because driving distance cost and vehicle number cost are typically positively connected with dynamic attitude, the study employs solutions that can increase dynamic response efficiency and save money. As a result, their robustness is higher.

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“…e authors described that the dynamic vehicle routing problem is combined with Spark cluster computing system for Big Data processing. e authors concluded that the proposed architecture is improved due to its capacity [40]. e determination of the research is to propose a Map-Reduce framework for improving the performance for monitoring fluoride-producing process using Big Data.…”

Section: Artificial Intelligence and Big Datamentioning

confidence: 99%

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

Chang

Nazir

2022

Scientific Programming

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Big Data is data whose shape and volume are rising with the passage of time and innovations in technology. This increase will give birth to more uncertain and complex situations, which will then be difficult to properly analyze and manage. Various devices are interconnected with each other, which communicate different types of information. This information is used for different purposes. A huge volume of data is produced, and the storage becomes larger. Computational modeling is the tool that helps analyze, process, and manage the data to extract useful information. The modern industry's challenge is to incorporate knowledge into Big Data applications to deal with distinguishing difficulties in computational models. The techniques and models are delivered with guides to help analysts quickly fit models to information insights. The decision support system is a strong system that plays a significant role in shaping Big Data for sustaining efficiency and performance. Decision-making through computational modeling is also a powerful mechanism for supporting efficient tools for managing Big Data for influential use. Keeping in view the issues of modern-day industry, the proposed study has been considered to present decision-making and computational modeling of Big Data for sustaining influential usage. The existing state-of-the-art literature is presented in an organized way to analyze the currently available research.

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A real-time Decision Support System for Big Data Analytic: A case of Dynamic Vehicle Routing Problems

Cited by 13 publications

References 23 publications

Review of research on vehicle routing problems

Review of research on vehicle routing problems

Time Window Oriented IoT Vehicle Pathway Study for the Dynamically Changing Needs of E-Commerce Customers

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

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