Multi-task and Multi-team Work Order Scheduling Using Non-dominated Sorting Genetic Algorithm II

Work orders in the maintenance field and in general resource-constrained problems have integrated multi-objective scheduling in order to produce an efficient job routine. One of the current drawbacks of today’s scheduling apparatuses often introduces conflicting schema and illogical arrangements. In this research, non-dominated sorting genetic algorithm version II (NSGA-II) is proposed for work order scheduling applications, using a set of five-step genetic-oriented workflow. The set includes data (chromosome) representation, high-fitness parents selection, adjusted binary crossover, mutation, and survival selection. NSGA-II is investigated in conjunction with multi-team and multi-task directives or MT \(^2\) WOS-NSGA-II. The scheduling results show noteworthy improvements compared to traditional NSGA-II with binary n-crossover, having minimum timing and resource constraints violation, and reducing execution hours by approximately 300 units.