Evaluation of rope shovel operators in surface coal mining using a Multi-Attribute Decision-Making model

Abstract

Rope shovels are used to dig and load materials in surface mines. One of the main factors that influence the production rate and energy consumption of rope shovels is the performance of the operator. This paper presents a method for evaluating rope shovel operators using the Multi-Attribute Decision-Making (MADM) model. Data used in this research were collected from an operating surface coal mine in the southern United States. The MADM model consists of attributes, their weights of importance, and alternatives. Shovel operators are considered the alternatives. The energy consumption model was developed with multiple regression analysis, and its variables were included in the MADM model as attributes. Preferences with respect to min/max of the defined attributes were obtained with multi-objective optimization. Multi-objective optimization was conducted with the overall goal of minimizing energy consumption and maximizing production rate. Weights of importance of the attributes were determined by the Analytical Hierarchy Process (AHP). The overall evaluation of operators was performed by one of the MADM models, i.e., PROMETHEE II. The research results presented here may be used by mining professionals to help evaluate the performance of rope shovel operators in surface mining.