**1. Introduction**

The high and volatile commodity prices are caused by unanticipated changes demand and supply [1]. These volatile prices put cost pressure on mining organization to optimize operations. The availability and utilization of mining equipment's, is the major contributor for an organization to manage costs and supply disruptions.

Traditionally, maintenance activity for mining equipment, relies on a series of time based or equipment running hours based checks for scheduling maintenance activities. The fourth industrial revolution provide organizations with a balanced approach to reduce costs with safety. As new technologies get deployed, the operations and maintenance landscape is continually being digitized by mechanization, automation, industrial internet of things (IIoT) and IT-OT (information technology – operational technology) integration. These technologies provide visibility to real time operations data. Analysing the data with artificial intelligence (AI) adds the ability to predict and respond to operational disruptions, for example – predict the next failure date of the asset and provide perspective guidance for maintenance. Further, optimization adds

the capability to synchronizing the scheduled, predicted maintenance activity with production schedule to minimize the maintenance costs and production losses.

Ant colony optimization (ACO) [2, 3] is a metaheuristic for solving hard combinatorial optimization problems. This was proposed by Dorigo et al., inspired from the behaviour of real ants, which use pheromones as a communication medium to find the shortest path to food from the colony. Analogous to the biological example, ACO is based on indirect communication within a colony of simple agents, called (artificial) ants, mediated by (artificial) pheromone trails. In ACO algorithms there are several generations of artificial ants which search for good solutions. In each generation, each ants find a solution by going step by step through many probabilistic decision till a solution is found. Ants that found good solutions put some amount of pheromone on the edges of path to mark their path. This will help attract the next generation of ants to find solutions near the good space. Generally pheromone values of ants are guided by the specific heuristic that is used for evaluating decisions.

In this chapter, we will focus on the framework for optimizing the preventive maintenance, predictive maintenance and production schedule. The first section will cover the maintenance strategies and framework. The second section will give a brief overview of the ACO. The third section will cover the maintenance solution framework for mining equipment's. In the last section we will conclude our recommendations for mine equipment maintenance scheduling.
