A Genetic Algorithm-Based Approach for Optimizing Short-term Production Schedules of Multi-mine Mineral Value Chains

This article presents a customized genetic algorithms (GA) with new crossover and mutation operators to solve the short-term production scheduling problem of multi-mine mineral value chains (MVC). The preceding problem consists of determining the extraction sequence and destination allocation of blocks from all the mines collaboratively while closely meeting the quality and quantity requirements of the processing units subject to relevant technical and operational constraints. The short-term production scheduling is carried out at shorter scales wherein the operations are modeled in a great detail with large number of constraints. This makes the industry-scale instances of the problem computationally intractable for standard mixed-integer programming (MIP) solvers. Thus, a GA-based heuristic approach is developed to obtain nearoptimal solutions to the large-scale instances of the problem in a reasonable amount of computational time. Computational experiments show that the developed GA-based method is a promising way to handle industry-scale instances of the problem. Moreover, the sensitivity analysis on various parameter combinations of crossover and mutation operators indicates that the customized global mutation operator, when used in combination with the customized crossover operators, took on average 12.5% less time than the customized local mutation operator to converge to a solution.