Valentas Gruzauskas

Valentas Gruzauskas
Vilnius University · Faculty of Mathematics and Informatics

MS

About

24
Publications
29,435
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192
Citations
Additional affiliations
February 2022 - present
Vilnius University
Position
  • Professor (Assistant)
September 2016 - June 2022
Kaunas University of Technology
Position
  • Professor (Assistant)

Publications

Publications (24)
Article
The inevitable transition from conventional to self-driving vehicles is a large-scale process involving a series of interactions and related to major challenges for human society. Autonomous vehicles (AVs) considered as a future of road transport, have the potential to significantly reduce transport costs, improve road safety by significantly reduc...
Article
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Consumer demand for organic products, rapidly growing urbanizations levels requires the food supply chain to reduce lead-time and maintain higher product quality. For the food supply chain to cope with the raising issues an e-commerce type of supply chain must be implemented. This approach creates challenges for supply chain, because the food indus...
Article
Full-text available
Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of indicators from large-scale orthoimages of different re...
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Full-text available
The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price...
Book
The COVID-19 pandemic has left a clear mark on virtually every area of human activity. Arguably, most prominent changes may be observed in the global supply chain where the delivery times have changed, and even minor outbreaks of the pandemic pose ever-increasing risks in logistics, supply, and infrastructure. The authors of the scientific study an...
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Today we discover ourselves in different transformational periods of human history. Significant as the farming and industrial revolutions before it, this digital reconstruction is redefining various characters of contemporary life throughout the world. Artificial intelligence (AI) represents a frequently fundamental role in this transformation. In...
Article
Full-text available
Real estate valuation uses 3 main approaches: income, cost and comparative. When applying the comparative method, correction coefficients based on similar real estate transactions are determined. In practice, the coefficients and similar real estate objects are usually determined by using qualitative approach based on the valuators’ experience. The...
Article
The current approaches to supply chain management generate large amounts of food waste due to the growing urbanization levels, increasing consumer demand for organic products and the growth of e-commerce distribution channel. These trends require the organizations to rethink their approaches to supply chain management, so that they could cope with...
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This paper focuses on efficiency of banks and human resources. The subjects of our analysis are banks based in Slovakia. The aim of this paper is to determine the relationship between technical efficiency on one side and the number of banks’ employees and personnel expenses/number of employees on the other side. Technical efficiency scores were det...
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The activities of the most enterprises are related to the difficulties while adapting to the current market demand, therefore, it is vitally important to define and solve the arising problems. Today consumers require non-expensive, personalized, ecological products just on time to their doorstep. Most government policies in this area are focused on...
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The changing business environment has influenced new innovation development, which is currently described as the upcoming 4 industrial revolution. The development of Internet of Things, Big Data concepts increased the productivity of various businesses and influenced the appearance of new business models. A more advanced concept of technologies in...
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Annotation The upcoming fourth industrial revolutions causes instability in the society and economy. From one side, the development of robots and artificial intelligence allows automation of business processes, which in result stimulates the growth of the economy. In 2016 an insurance company implemented artificial intelligence for insurance pay-ou...
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The trend for e-commerce, estimated population size to 11 billion by 2050, and an increase in urbanization level to 70 % is requiring to re-think the current supply chain. These trends changed the distribution process: delivery distances are decreasing, the product variety is increasing, and more products are being sold in smaller quantities. There...
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The strategies of competitive advantage are changing dramatically because of high technology development. The data size in the world is multiplying rapidly - the amount of information in the world doubles every 12 months. Therefore, the authors analyzed Big data in the food supply chain. The methodology used in the paper consists of a review of glo...
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The objective of this article is to develop a methodology in order to implement real-time customers segmentation analysis in the decision making process of the enterprise. A review of big data usage in retail stores was conducted along with a document-based descriptive analysis of secondary data and further critical literature analysis. Decision ma...
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The globalization is affecting the economic development of world. Internet provided a dramatic increase in information and money transaction speed, therefore small markets are also growing rapidly in the international markets. The focus of this paper is on a cluster of companies with logistics intensive activity. This includes mainly logistics serv...
Article
Full-text available
In recent years the manufacturer’s competitiveness environment has been greatly influenced by the new consumer trend. Consumers developed a need for variety, high quality, low price and just on time products. This affect has influenced the producer’s profitability dramatically. The food industry is one of the most influenced industries, because the...
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The main problem of the paper is that globalization has effected the world's market, now all markets are relatively small by comparing it with the international. Therefore, the main objective of paper is to create a competiveness strategy that is oriented to the small market. The author's results indicates that usually researches position small mar...
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Full-text available
The authors analyzes non-economic and non-material factors usage for maximizing business's competitiveness level. These findings was amplified by showing how weather condition's variation can become an economic factor. The authors done research indicates that when proper situation happens or by making, it happen non-economic factors can be used to...
Article
Full-text available
In this, paper the authors analysed the food industry's total logistic cost and supply chain, reviewed the temperature control technology usage possibilities in logistic clusterization processes. Then conducted the food industries supply chain cost statistical analysis and determined the influence of logistic cost components to total logistic cost....

Questions

Questions (2)
Question
Good day,
I am doing my PhD in operation research in logistics. I want to create an urban logistics simulation, during which I would show how autonomous vehicles can adapt to disruptions and resilience would emerge. The theoretical approach is based on complex-adaptive systems theory.
As I understand, the routing should be made by using “reinforcement learning for combinatorial optimization”, however, I do not have experience related to RL application for route scheduling. Could anyone recommend courses or literature related to this topic?
If anyone has developed such a model, maybe could provide some insights in to the data architecture? As I understand this would be similar to supervised learning, however sequence of categories must also be taken into consideration. Should the implementation be based on graphs? Or should I have features categorizing a trip with evaluation of the cost function?
In my case, I am having a e-commerce industry with product delivery to end-consumer, during the day I am generating traffic jams, which would block the routs. The algorithm should learn from the environment and select better routes automatically by considering the goal function.
Question
Hello,
Recently I found "Flexible Large Scale Agent Modelling Environment for the GPU (FLAMEGPU)" - http://www.flamegpu.com/
Maybe someone is using it and could explain if it is worth learning to use Flame-GPU? As I understand FLAMEGPU uses XML to build models and runs them on CUDA. However, I do not understand why it uses XML? Is it not worth using Python C# with CUDA or something similar?
I also found great looking agent-based models with visualization, could someone explain more precisely how FLAMEGPU is used? e.g. https://www.youtube.com/watch?v=7cjorOe810o&t=2s
What technical requirements are needed for it? Would a laptop with 500 GB SSD hard drive, 16 GB Ram, i7-4710 HQ (8 CPU) ~2.5Ghz and NVIDIA GeForce GTX 860M - 2 GB enough, or a high-performance computing server is needed to use FLAMEGPU? e.g. Amazon web services?

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Projects

Projects (2)
Project
The research explores new ways of producing geographical knowledge (socio-environmental, geoeconomic, territorial) in artificial intelligence from Earth observation systems, including the new sensors in tests (hyperspectral, superspectral, thermal, etc.), the new approaches for extracting the geographical data, recognise and identifying geographic objects (geographic ontologies, morpho-spectral databases, etc.) with the vernacular sensory knowledge contributing to the implementation of new socio-environmental systems of analysis of territories. The research explores new ways of producing geographical knowledge (socio-environmental, geoeconomic, territorial) in artificial intelligence from Earth observation systems, including the new sensors in tests (hyperspectral, superspectral, thermal, etc.), the new approaches for extracting the geographical data, recognise and identifying geographic objects (geographic ontologies, morpho-spectral databases, semantics, etc.) with the vernacular sensory knowledge contributing to the implementation of new socio-environmental systems of analysis of territories. Currently, research in space and airborne remote sensing and in geomatics is focused on 1.) the exploitation of hyperspectral, thermal, and complex multi-source data for the analysis of geographical space, applications to planning, urban territories, coastal zones, and the environment (multi-temporal and multi-scale analyses). 2.) The development of semantic analysis and modelling methods based on the concepts and theory of geographical and spatial ontologies in AI. 3.) The development of spatial analysis and geo-simulation models based on or combining spatial remote sensing images. 4.) The conceptual, theoretical development of geographic knowledge systems.
Project
My area of research concerns the analysis of the processes of urbanisation and territorial development in the context of globalisation, climate changes, and adaption in the post-soviet territories: South-Est Baltic, Nordic countries including the coastal zones. 1.) Nordic Post-Soviet Urbanisation, Land Cultural Reappropriation by Geo-Simulation, and Territorial Convergences. 2.) Coastal Urbanisation, Environmental Changes, and Policy Issues. 3.) Spatial indexes for the Urban Change's Monitoring. 4.) Comparative Spatial Analysis of the Territorial and Geopolitical Changes. Geographic Analysis is based on the development and implementation of Earth Observation remote sensing monitoring systems and geo-simulations: multispectral, hyperspectral imagery, artificial intelligence, massive data image processing, multi-temporal geographic knowledge systems, GIS, etc.