Abstract
In intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and typically have an earliest and latest possible start time. We introduce and analyze four algorithmic ideas for this class of time/sequence-dependent over-subscribed scheduling problems with time windows: a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS), a new randomization strategy for neighbourhood operators, a partial sequence dominance heuristic, and a fast insertion strategy. Through factor analysis, we demonstrate the performance of these new algorithmic features on problem domains with varying properties. Evaluation of the resulting general purpose algorithm on three domains—an order acceptance and scheduling problem, a real-world multi-orbit agile Earth observation satellite scheduling problem, and a time-dependent orienteering problem with time windows—shows that our hybrid algorithm robustly outperforms general algorithms including a mixed integer programming method, a constraint programming method, recent state-of-the-art problem-dependent meta-heuristic methods, and a two-stage hybridization of ALNS and TS.
Highlights
Manufacturing planning is an essential element of supply chain management (Jacobs et al 2010)
This article studied an important class of over-subscribed scheduling problems in the intelligent manufacturing industry, which is characterised by time-dependency and/or sequence-dependency with time windows
We developed a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS)
Summary
Manufacturing planning is an essential element of supply chain management (Jacobs et al 2010). This makes the problem become an over-subscribed scheduling problem, consisting of simultaneously selecting a subset of jobs to be processed as well as the associated schedule This problem is important because it represents a class of real-world problems including the Earth observation satellite scheduling problem (Augenstein et al 2016; Akturk and KiliÇ 1999), the order acceptance and scheduling problem (Oguz et al 2010; Wang et al 2017), the orienteering problem (Verbeeck et al 2017), and selective maintenance scheduling (Duan et al 2018). 2. Through factor analysis, we show the robust performance of the new algorithmic features, and we derive useful conclusions on how to use tabu search for problem domains with different properties. In “Comparison with state-of-the-art algorithms” section, the algorithm is compared with state-of-the-art methods on three problem domains; the conclusions are summarized in “Conclusions” section
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