A genetic algorithm for scheduling an automated machine cell with tool and pallet–fixture setups
Abstract
This paper solves the scheduling problem of identical parallel machines with tooling and pallet–fixture constraints in a high‐mix, low‐volume, high‐complexity manufacturing environment. The machines are connected through an automated material handling device and in‐between buffer places that allow for autonomous production. We first develop a mixed‐integer linear program that captures production constraints on pallet reconfigurations, tool switches, and unsupervised shifts. The objective is to minimize the total operational cost, which consists of the costs of machines, tool switches, and pallet reconfigurations. We then propose a genetic algorithm (GA) to solve industry‐sized problem instances. The proposed GA captures the decisions on operation, machine, and pallet allocation simultaneously. We present a case study and an extended set of computational experiments based on data obtained from our industry partner. Our computational experiments show that the proposed GA is able to reduce approximately 20% the total operational cost obtained by a practitioner heuristic currently used in practice.