A Biologically Inspired Solution for Fuzzy Travelling Salesman Problem

Pezhhan, Elham; Mansoori, Eghbal G.

doi:10.1007/978-3-319-10849-0_28

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Therefore, it tends to be used in different areas in the detection of short routes [25]. The traveling salesman problem has been used as a solution in many studies with its dynamic analysis structure [26][27][28][29][30]. In a sifting system where object recognition and TSP algorithms work simultaneously, the locations of unsuitable fruits and foreign materials to be selected can be shown as target coordinates in the TSP algorithm and an optimum route can be created for the orientation of the vacuum head.…”

Section: Figure 6 Selecting Unit Covering Large Area In Factoriesmentioning

confidence: 99%

Real-time Fast Selection System with Object Recognition and TSP algorithms

DEMİR

2023

International Journal of Applied Mathematics Electronics and Computers

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The stage before the conversion of agricultural products into post-harvest consumer products is the process of separating the raw products into appropriate classes. Today, this difficult manual separating process is a process in which a large number of workers work at an intense pace on the product line and the workforce is intensively spent. Disruptions in separating as a result of carelessness cause product loss, loss of time and cost increases. In this study, as an alternative to manual separating processes, a real-time separating system, which detects the products in the factory band with object recognition methods and enables fast positioning of the separating tool on the products, works simultaneously with object recognition and traveling salesman problem algorithms has been created. In this way, a low-budget separating system is recommended for large selecting processes with a time- and cost-effective selecting model. In the study, the creation of a real-time fast separating system with the support of the traveling salesman algorithm, performance evaluation and research and findings on the fast separating model are presented.

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