Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic taskarrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms.

中文翻译：

---

使用改进的基于多种群生物地理的优化，协作任务调度和云计算中的新任务到达

由于客户日益个性化的需求，任务调度在云制造中很重要。但是，当发生各种变化时，由于实际制造环境的不确定性，先前的最佳计划可能会变得不理想，甚至变得不可行，因为随着时间的推移动态任务的到来是至关重要的来源。在本文中，我们提出了一种新的协作任务调度（CTS）模型，该模型处理了考虑多供应链协作的新任务到达。我们提出了一种改进的基于生物地理的多种群优化（IMPBBO）算法，该算法使用基于矩阵的解决方案表示形式，并集成了多种群策略，针对最佳解决方案的本地搜索以及确定最佳计划的协作机制。通过与其他五种算法进行比较，进行了一系列实验，以验证IMPBBO算法解决CTS模型的有效性。在45个案例中，有41个案例通过IMPBBO算法获得的关于平均最佳值的实验结果优于通过比较算法获得的平均值，显示了其优越的性能。已采用Wilcoxon检验来加强IMPBBO算法的性能优于五种比较算法的事实。