Automated Design of Production Scheduling Heuristics: A Review

By J. Branke, Su Nguyen, Christoph W. Pickardt, Mengjie Zhang. IEEE Transactions on Evolutionary Computation, 2016

408 citations recorded by the source corpus.

Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyper-heuristics have been developed and are shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem to be a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasized in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This paper strives to fill this gap by summarizing the state-of-the-art approaches, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2926c676419a076d3f9294edf5fbd78bb1d5a6c4

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In Industrial and Manufacturing Engineering

1. David B. Fogel
2. Ender Özcan
3. Edmund Kieran Burke
4. Jun Zhang
5. John H. Drake
6. Ahmed Kheiri
7. Rhyd Lewis

Citations in the corpus

• Recent advances in selection hyper-heuristics (2020, background): “A more recent publication by Branke et al. (2016) provided a comprehensive review of recent developments in generation hyper-heuristics, with an emphasis on the design of construction heuristics in production scheduling optimisation problems.”
• An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing (2018, methodology): “…successfully applied to many COPs, such as protein structure prediction [46], music composition [47], multiple sequence alignment [48], distribution network restoration [49], constrained optimization [50], scheduling problems [51], and haystack problem [52], and have shown promising performance.”
• Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review (2018, positive): “In particular, evolutionary algorithms [11,66], which use computer simulations of nature's processes of variation and selection to solve problems, can assign the most appropriate study center for each prospective patient in a trial based on patient and study center availability.”