A new branch-and-cut approach for integrated planning in additive manufacturing

TL;DR

This paper introduces a novel branch-and-cut algorithm utilizing combinatorial Benders decomposition for integrated planning in additive manufacturing, improving efficiency in component assignment and sequencing for 3D printing.

Contribution

It presents a new branch-and-cut approach with enhanced sub-problem solving for better integrated planning in additive manufacturing.

Findings

Superior computational performance over existing models

Effective handling of orthogonal packing with rotation

Validated on new benchmark instances

Abstract

In recent years, there has been considerable interest in the transformative potential of additive manufacturing (AM) since it allows for producing highly customizable and complex components while reducing lead times and costs. The rise of AM for traditional and new business models enforces the need for efficient planning procedures for AM facilities. In this area, the assignment and sequencing of components to be built by an AM machine, also called a 3D printer, is a complex problem joining the nesting and scheduling of parts to be printed. This paper proposes a new branch-and-cut algorithm for integrated planning for unrelated parallel machines. The algorithm is based on combinatorial Benders decomposition: The scheduling problem is considered in the master problem, while the feasibility of a solution is checked in the sub-problem. Current state-of-the-art techniques are extended to solve the orthogonal packing with rotation to speed up the solution of the sub-problem. Extensive computational tests on existing instances and a new benchmark instance set show the algorithm's superior performance compared to an existing integrated mixed-integer programming model.