Article Preview
TopIntroduction
As time passed by, manufacturing concerns are mainly concentrating on the automated-driven systems within an industry. Automation helps to achieve the anticipated goals and can accomplish a tedious task repetitively without disruption. In today's technological advancements, most industries are focusing on lowering production costs while increasing productivity by improving computerized-driven systems. According to Kulak (2005), “the material handling task accounts for 30-75% of the total cost of a product, and efficient material handling can be responsible for 15-30% reduction in manufacturing system operations cost” (pp. 310). The robot is a type of computer-programmed automated material handling device mounted to accomplish several types of jobs like loading, unloading, welding, parts assembling, spray painting, picking and placing, etc. Hence, well-organized and efficient handling systems are required to increase material flow efficiency, productivity, system flexibility, improve facility utilization, minimization of lead time and labor cost (Karande and Chakraborty, 2013). Improper selection of industrial robots not only hampers productivity but also puts a negative impression on the organization’s status. Therefore, appropriate robots should be selected to enhance production with the highest precision. There are many objective and subjective conflicting criteria are present that can influence the selection of a suitable robot (Mondal and Chakraborty, 2013). Despite the high capital investment, installing robots in industries has many benefits. For example, industrial robots can dramatically enhance the manufacturing organization’s efficiency and productivity, it can perform dangerous, complex and repetitive tasks with high accuracy. Bhangale et al. (2004) stated that “there are over 75 attributes that are to be considered while selecting a robot for a particular industrial application”. Athawale and Chakraborty (2011) outlined some of the essential variables to consider while picking an appropriate robot alternative, e.g. load-carrying capabilities, manipulator distance, durability, man-machine interface, cost, accuracy, etc. are some of these features. Decision-makers are having difficulty selecting the best robot choice because there are many competing robot performance qualities present, and MCDM coordination is the best solution to these kinds of difficulties.
Because of the advantages listed above, there is an urgent need to resolve this issue and offer the best robot option to be incorporated into an industry, while also providing some simple ideas to the industrial sectors before investing in the installation of automated machinery. When carrying out any robot selection process, the decision maker (DM) must examine many subjective and quantifiable factors, some of which are exploiting (beneficial) or diminishing (non-beneficial) (Rao, 2007). As a result, MCDM techniques are the ideal optimization tools for executing these types of situations with multiple competing criteria. An example of a robot assortment MCDM problem is offered in this research article and evaluated using the newly developed COPRAS-ARAS hybrid MCDM methodology. Several researchers have previously tackled the current robot selection problem using various MCDM strategies (Rao, 2007). Nonetheless, the authors of this paper found all of those studies to be contradictory and inconsistent, implying that there is room for improvement by employing additional viable MCDM methods and comparing the results to the prior ones. According to Lit et al. (2002), “The best way to handle the material is not always clear. In some cases, the requirements are satisfied by several different methods. For this reason, the selection of MHE is a critical stage in the facilities planning or the construction of assembly lines. As several parameters must be considered, some are crucial, and others are more sensitive to design. Therefore, the developer must be given an interactive method, allowing him to easily track the effects of his decisions on the solutions proposed” (p. 331).