Fuzzy and Evolutionary Algorithms for Transport Logistics Under Uncertainty

Kondratenko, Yuriy; Kondratenko, Galyna; Sidenko, Ievgen; Taranov, Mykyta

doi:10.1007/978-3-030-51156-2_169

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Furthermore, intelligent decision support systems based mainly on fuzzy logic are used to automate various complex dynamic objects and processes at higher and strategic hierarchical control levels (Solesvik, 2017, Kondratenko, 2006b, Kondratenko, 2014. Thus, the paper (Sekretarev, 2018) presents an intelligent control system for higher-level control of power generation modes at a hydroelectric power plant.…”

Section: Fig 2 Hierarchical Levels Of Control Of Complex Dynamic Proc...mentioning

confidence: 99%

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

2023

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The collective monograph elucidates the trends in the formation and development of the digital society in the 21st century, and the role of information and communication technologies therein. Presently, information and innovative technologies are shaping more efficient management mechanisms, expanding access to healthcare, education, science, culture, and banking, enhancing the quality of public services, and broadening communication and information access capabilities.The research findings presented in the monograph underscore the significance and necessity of digital technologies for the welfare of the population and economic development. Today, information and innovative technologies are integrated into business processes across virtually all spheres of activity, as they enhance decision-making efficiency. Information technologies enable any activity to be conducted online, facilitating solutions to social issues by easing access to education through remote learning, local government services through e-governance, electronic registration systems, medical services via digital health systems, and the provision of electronic financial and banking services.The first chapter of the monograph analyses the current state and prospects of using information and innovative technologies in education, presents research results on the application of artificial intelligence, machine learning, nanotechnologies, and virtual reality in higher education training. It also highlights the nuances of developing digital competencies among schoolchildren and students.The second chapter is dedicated to the application of information and innovative technologies in economics and management, examining their impact on the economy of the information society. The digital economy is grounded on information and communication technologies that facilitate ecommerce through the operation of electronic digital devices, tools, and automated systems.The third chapter contemplates the theoretical concepts of employing information and innovative technologies in society at large, considering their influence in fields such as ecology, social issues, psychology, and professional adaptation to digital existence.The fourth chapter is devoted to the utilization of artificial intelligence, studying its societal impact and evaluating potential opportunities and risks. Particular attention is given to the status and prospects of artificial intelligence in education, manufacturing, medical fields, and the development of communicative competencies.The fifth chapter of the monograph focuses on contemporary technologies in physical education, sports, and physical rehabilitation, which are paramount in the context of ensuring effective approaches to physical development, rehabilitation, and fostering a healthy lifestyle approach.

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Section: Fig 2 Hierarchical Levels Of Control Of Complex Dynamic Proc...mentioning

confidence: 99%

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

2023

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“…In [18], an algorithm based on Clarke and Wright savings heuristic where three criterion of the network namely the distance of various edges, time required to travel these edges and road quality are considered as fuzzy. In [23], fuzzy and evolutionary algorithms were used for planning the routes of tankers when the demand of fuel at various nodes in the network is presented by using Triangular fuzzy numbers.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving capacitated vehicle routing problem with demands as fuzzy random variable

2023

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Capacitated vehicle routing problem (CVRP) is a classical combinatorial optimization problem in which a network of customers with specified demands is given. The objective is to find a set of routes which originates as well as terminates at the depot node. These routes are to be traversed in such a way that the demands of all the customers in the network are satisfied and the cost associated with traversal of these routes come out to be a minimum. In real-world situations, the demand of any commodity depends upon various uncontrollable factors, such as, season, delivery time, market conditions and many more. Due to these factors, the demand can always not be told in advance and a precise information about the demand is nearly impossible to achieve. Hence, the demands of the customers always experience impreciseness and randomness in real-life. The decisions made by the customers about the demands may also have some scope of hesitation as well. In order to handle such demands of customers in the network, fuzzy random variables and intuitionistic fuzzy random variables are used in this work. The work bridges the gap between the classical version of CVRP and the real-life situation and hence makes it easier for the logistic management companies to determine the routes that should be followed for minimum operational cost and maximum profit. Mathematical models corresponding to CVRP with fuzzy stochastic demands (CVRPFSD) and CVRP with Intuitionistic fuzzy stochastic demands (CVRPIFSD) have been presented. A two-stage model has been proposed to find out the solution for the same. To explain the working of the methodology defined in this work, two different example of a network with fuzzy and intuitionistic fuzzy demands have been worked out. The proposed solution approach is also tested on modified fuzzy versions of some benchmark instances.

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“…The method to solve quadratic programming problem involving fuzzy variables in objective function as well as constraints has been explained by Mahajan and Gupta(3).Concept of fuzzy c-means clustering heuristic is used effectively to solve CVRP with large numbers of customers by zheng et.al. (13).Analysis of fuzzy and evolutionary approach to solve vehicle routing problems (VRP) with various constraint has been discussed in (14,15). An advanced PSO technique(APSO) was explained to solve VRP problem by Moghaddam et.al.…”

Section: Introductionmentioning

confidence: 99%

Meta Heuristic Algorithm for Vehicle Routing Problem with Uncertainty in Customer Demand

Garg¹,

Kakkar²,

Gupta³

et al. 2022

ECS Trans.

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The Vehicle Routing Problem is an optimization problem with number of applications in the transportation field. In this paper, Vehicle Routing Problem (VRP) with uncertainty in customer demand is considered. Often in real world situation, the information on demand on each customer is not accurate enough. Here, in this article we are assuming that the customer demands in each time period are uncertain and are considered as fuzzy variables. This kind of problem is known as an NP-hard problem. The main purpose of this article is to give conceptual and practical idea on how fuzzy logic with meta heuristic algorithm can be implemented to improve the optimization of vehicle routing problem with uncertainty in customer demand. To solve this problem, a hybrid Meta heuristic algorithm (HMHA) including clustering is proposed, with the objective of determining an optimal route to minimize the total cost.

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Fuzzy and Evolutionary Algorithms for Transport Logistics Under Uncertainty

Cited by 9 publications

References 16 publications

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

Solving capacitated vehicle routing problem with demands as fuzzy random variable

Meta Heuristic Algorithm for Vehicle Routing Problem with Uncertainty in Customer Demand

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