Order Acceptance and Scheduling with Sequence-dependent Setup Times: a New Memetic Algorithm and Benchmark of the State of the Art

TL;DR

This paper introduces Sparrow, a novel memetic algorithm combining genetic algorithms and large neighborhood search to improve order acceptance and scheduling with sequence-dependent setup times, validated on new and standard benchmarks.

Contribution

The paper presents Sparrow, a new hybrid memetic algorithm for OAS problems with sequence-dependent setup times, outperforming existing algorithms on benchmark instances.

Findings

Sparrow achieves better solution quality than state-of-the-art algorithms.

Sparrow has comparable runtimes to existing methods.

HSSGA performs well on large instances but requires more time.

Abstract

The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling. This problem consists of simultaneously selecting a subset of orders to be processed as well as determining the associated schedule. A common generalization includes sequence-dependent setup times and time windows. A novel memetic algorithm for this problem, called Sparrow, comprises a hybridization of biased random key genetic algorithm (BRKGA) and adaptive large neighbourhood search (ALNS). Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS. On a set of standard benchmark instances, this algorithm obtains better-quality solutions with runtimes comparable to state-of-the-art algorithms. To further understand the strengths and weaknesses of these algorithms, their performance is also compared on a set of new benchmark instances with more realistic properties. We conclude that Sparrow is distinguished by its ability to solve difficult instances from the OAS literature, and that the hybrid steady-state genetic algorithm (HSSGA) performs well on large instances in terms of optimality gap, although taking more time than Sparrow.