A review of process planning from an operations research perspective: models, algorithms, and benchmarks

Process planning bridges product design and manufacturing and increasingly involves multiple types of flexibility. As an NP-hard combinatorial optimisation problem, it poses substantial methodological challenges, yet effective optimisation is critical for improving production performance. This paper presents a review of process planning from an operations research perspective, covering models, algorithms, and benchmarks. We systematically classify mathematical programming models according to key characteristics, including model class, flexibility types, and objective settings, and highlight recent advances in linearisation techniques and compact formulations. From an algorithmic standpoint, we review and categorise solution paradigms, encompassing metaheuristics, multi-objective optimisation, deterministic methods, heuristics, and reinforcement learning, and analyse their respective strengths and limitations. In addition, we compile benchmark test cases together with technical specifications and best-known solutions to support systematic performance comparison. Finally, we identify research gaps and outline promising future directions, with particular emphasis on advanced optimisation methods and practically relevant modelling.