Utilizing Constraint Optimization for Industrial Machine Workload Balancing

Abstract

Efficient production scheduling is an important application area of constraint-based optimization techniques. Problem domains like flow- and job-shop scheduling have been extensive study targets, and solving approaches range from complete and local search to machine learning methods. In this paper, we devise and compare constraint-based optimization techniques for scheduling specialized manufacturing processes in the build-to-print business. The goal is to allocate production equipment such that customer orders are completed in time as good as possible, while respecting machine capacities and minimizing extra shifts required to resolve bottlenecks. To this end, we furnish several approaches for scheduling pending production tasks to one or more workdays for performing them. First, we propose a greedy custom algorithm that allows for quickly screening the effects of altering resource demands and availabilities. Moreover, we take advantage of such greedy solutions to parameterize and warm-start the optimization performed by integer linear programming (ILP) and constraint programming (CP) solvers on corresponding problem formulations. Our empirical evaluation is based on production data by Kostwein Holding GmbH, a worldwide supplier in the build-to-print business, and thus demonstrates the industrial applicability of our scheduling methods. We also present a user-friendly web interface for feeding the underlying solvers with customer order and equipment data, graphically displaying computed schedules, and facilitating the investigation of changed resource demands and availabilities, e.g., due to updating orders or including extra shifts.