Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection

Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection

Mehmet Ali Ilgın (Manisa Celal Bayar University, Turkey)
Copyright: © 2020 |Pages: 17
DOI: 10.4018/978-1-7998-1754-3.ch005

Abstract

An increasing number of companies are using robots to perform a variety of repetitive and hazourdous tasks. Existence of many different robot alternatives force companies to consider several conflicting criteria before determining the most suitable robot alternative. Researchers have developed various multi-criteria decision making based methodologies in order to assist the decision makers in robot selection process. However, those methodologies require decision makers to assign physically meaningless weights to evaluation criteria. This article eliminates this weight assignment process by proposing a robot selection methodology based on linear physical programming. In addition, fuzzy logic was integrated into the proposed approach in order to determine the preference values of subjective robot evaluation criteria. A numerical example is also provided in order to present the applicability of the proposed methodology.
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1. Introduction

Rapid developments in numerical control technology stimulated the use of automation in industry. One of the most widely used forms of industrial automation is the use of robots for the execution of hazardous, repetitious and difficult tasks. Robots can achieve a wide variety of tasks in a production facility including spot welding, material handling, spray painting, machine loading and assembly. Since they are controlled by computers, they can be connected to the other computers systems in the production facility to achieve computer integrated manufacturing. In addition, a robot can perform a task with a consistency and repeatability that can not be achieved by a worker (Groover, 2008).

Due to the above-cited advantages, robots can provide significant improvements in product quality and production efficiency. However, a company should determine the most suitable robot alternative for a particular task in order to fully experience the benefits provided by a robotic system. That is why robot selection is a crucial process affecting the competitiveness and profitability of a company. Decision makers must consider various objective and subjective criteria during this process. Objective criteria can be defined in numerical terms while the subjective criteria can be defined qualitatively. Table 1 presents the objective and subjective robot selection criteria commonly employed in the literature.

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