Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques

Material handling equipment (MHE) selection is a rapidly increasing multi-criteria decision-making (MCDM) issue with a plethora of factors affecting the selection process. The proper selection of MHE is a crucial issue for industrial organization's efficiency and productivity in the global market. The selection of the most suitable MHE for a specific engineering application is a costly as well as time-consuming process in which many candidate alternatives available on the market are considered as preliminary choices. Despite the facts that there are already a large number of mathematical methods available to evaluate, choose, and rate the alternatives for a given engineering application, this paper investigates the ability and applicability of two MCDM methods, namely, Additive Ratio ASsessment (ARAS) and COmplex PRoportional ASsessment (COPRAS). The present research article investigates three real-time MHE selection problems, i.e., conveyor selection, automated guided vehicles (AGV) selection, and selection of robots. All the three MCDM problems are evaluated and alternative rankings are proposed using these two methods, while the objective weighted system, entropy is used to assess the relative weights of the criteria. The current outcome rankings are also validated by using other six MCDM methods and comparing them to the findings of the previous researchers. Each example provides a listing of all the possible options from optimum to worst MHE that could potentially improve the previously proposed rankings and sensitivity analysis is computed to demonstrate that these two MCDM methods are robust enough and capable of being used effectively in solving MHE selection problems. Finally, conveyor 3, AGV 5, and ASEA-IRB 60/2 robot are found to be the best choice, while conveyor 1, AGV 3, and Unimation PUMA 500/600 robot are the worst alternative for the three MHE selection issues respectively.