Dynamic cloud manufacturing service composition with re-entrant services: an online policy perspective
研究了云制造中任务信息未知且服务可重入的动态服务组合问题，将其转化为在线装箱问题，并提出了四种有性能保证的在线策略，通过实验比较了它们的适用场景。
Cloud manufacturing (CMfg) emerges as a promising manufacturing paradigm, where service composition (SC) is a critical process concentrating on matching tasks and services. Existing studies usually ignore the dynamic nature of the CMfg environment, where task information is not always known before. Moreover, CMfg services are re-entrant, i.e. after being occupied for a period of service time, these services re-enter the CMfg platform (i.e. be available again). Re-entrant services significantly complicate CMfg platform revenue management. In this regard, we study the dynamic SC problem of CMfg (CMfg-DSC) incorporating re-entrant services within an online setting for the first time. CMfg-DSC is reformulated as an online packing problem. If a task is accepted, each requested service will be occupied until service time terminates. We propose online policies with performance guarantees, namely, static online packing policy (Static), opportunity-cost-based policy (Oppo), and dual-based policy with/without known distribution (Dual-k & Dual-u). Experiment results show that (1) Static is applicable for most cases; (2) Oppo has the potential for decent performance but at the cost of time; (3) Dual-u is reliable when only past observations are available; (4) Dual-k performs well given abundant service provision, but its performance would deteriorate if we lower the reward-cost threshold.