Sustainable negotiation-based nesting and scheduling in additive manufacturing systems: A case study and multi-objective meta-heuristic algorithms.

This paper proposes a novel integrated framework for nesting (i.e., part orientation selection and two-dimensional packing) and scheduling parts assigned to batches (or jobs) on additive manufacturing machines. For the first time, a tri-objective optimization model is designed that interprets profit, energy utilization of machines, and goodwill losses (i.e., tardiness, negotiation, and increasing due date) as three sustainability criteria. Besides, considered negotiation plans may reduce the prices of ordered parts for a possible increase in the initial due date set by customers. The model's scalability is supported by tailoring three algorithms: robust improved ɛ -constraint method, non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimizer (MOGWO). Several insights are derived by analyzing the model's sensitivity to its key parameters and validating its applicability by a case study of Amazon's last-mile delivery process. The results confirm the conflicting objectives, suggested action plans, and proposed algorithms. [Display omitted] • Developing a flexible/faster nesting method in additive manufacturing (AM) systems. • Considering sustainability criteria for nesting and scheduling in AM systems. • Proposing a framework to set negotiation plans for scheduling and nesting decisions. • Developing a representation method for meta-heuristic algorithms. • Deriving insights for a last-mile delivery system in AM by a real-world case study. [ABSTRACT FROM AUTHOR]