Search

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 task arrival 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. [ABSTRACT FROM AUTHOR]

Horizontal cooperation in logistics refers to several logistics service providers cooperating to accomplish common goals, which usually involves a collaborative vehicle routing problem. In this paper, we present a new collaborative vehicle routing problem with rough location (CVRPRL), which considers both the security of sharing detailed customer information and the configuration of shared resource. We utilize the rough location of the customer to replace the detailed customer location and introduce the concept of collaborative logistics sharing degree in the CVRPRL model. Subsequently, the cost allocation mechanism is designed based on an extended Shapley value, which allocates fixed costs and risks in different ways. Further, an extended ant colony optimization (EACO) algorithm is proposed to solve the CVRPRL. The EACO algorithm combines both large neighborhood search and local search strategies. Finally, we perform a series of simulation experiments to verify the effectiveness of EACO compared with other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]