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A Novel Rough Range of Value Method (R-ROV) for Selecting Automatically Guided Vehicles (AGVs)
Author(s) -
Edmundas Kazimieras Zavadskas,
Zdravko Nunić,
Željko Stjepanović,
Olegas Prentkovskis
Publication year - 2018
Publication title -
studies in informatics and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.321
H-Index - 22
eISSN - 1841-429X
pISSN - 1220-1766
DOI - 10.24846/v27i4y201802
Subject(s) - computer science , range (aeronautics) , value (mathematics) , remotely operated underwater vehicle , simulation , artificial intelligence , machine learning , aerospace engineering , robot , mobile robot , engineering
Material Handling Equipment is a set of different tools, devices, applications that aim to facilitate the handling of materials and products. It is used inside the warehouse, but also between warehouses and production facilities. As an important type of material handling equipment, automatically guided vehicles (AGVs) play one of the key roles in warehouse automation. The benefits of applying the AGVs in the warehouse automation process include: reducing labor costs, increasing reliability and productivity, reducing the damage of goods, safety improving, managing and controlling the complete system, etc. In this paper, a Novel Rough Range of Value Method (R-ROV) for evaluating and selecting AGVs in the warehouse has been developed, which is one of the main contributions. In addition, the Full Consistency Method (FUCOM) was used to determine the weight values of the criteria. The model was formed through nine AGVs and 7 criteria. In the framework of checking the stability of the obtained results and the developed model, the comparison was performed using: Rough WASPAS (Weighted Aggregated Sum Product ASsessment), Rough SAW (Simple Additive Weighting) and Rough MABAC (Multi-Attributive Border Approximation area Comparison). Sensitivity analysis showed high correlation of ranks with all the applied methods by employing the Spearman’s Correlation Coefficient (SCC).

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