Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models
Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models
复制标题
DOI：
10.1007/s00170-017-1167-3
复制
发表时间：
2017-10
期刊：
影响因子：
--
通讯作者：
H. Akbaripour;M. Houshmand;T. Woensel;Nevin Mutlu
中科院分区：
文献类型：
--
作者：
H. Akbaripour;M. Houshmand;T. Woensel;Nevin Mutlu
云制造是一种新兴的服务型制造范式，通过整合和管理分布式制造资源，满足高度定制化的复杂制造需求。服务选择优化和调度（SSOS）过程是云制造实际实施的一个重要问题。在本文中，我们提出了新的混合整数规划（MIP）模型，用于解决具有基本组合结构（即顺序、并行、循环和选择性）的 SSOS 问题。通过结合所提出的 MIP 模型，可以解决具有混合组成结构的 SSOS。由于运输在云制造环境中不可或缺，因此该模型还优化了给定的混合轴辐式运输网络内的路线决策，其中核心决策是最佳地确定一对分布式制造资源之间的运输是直接路线还是使用枢纽设施路线。与之前在云制造中进行的大多数研究不同，假设制造资源不是连续可用于处理的，但其占用间隔的开始时间和结束时间是预先已知的。通过解决 SSOS 中的不同场景来评估所提出模型的性能。此外，为了检验结果的稳健性，对关键参数进行了一系列敏感性分析。本研究的结果表明，对交通和可用性的考虑不仅可以显着改变 SSOS 的结果，而且对于获得更现实的解决方案也是必要的。结果还表明，与纯轴辐式网络或纯直接网络相比，混合轴辐式交通网络内的路由具有更大的灵活性，并具有节省成本和时间的优势。节省的水平取决于降低枢纽设施之间运输成本的折扣系数的值。
Cloud manufacturing is an emerging service-oriented manufacturing paradigm that integrates and manages distributed manufacturing resources through which complex manufacturing demands with a high degree of customization can be fulfilled. The process of service selection optimization and scheduling (SSOS) is an important issue for practical implementation of cloud manufacturing. In this paper, we propose new mixed-integer programming (MIP) models for solving the SSOS problem with basic composition structures (i.e., sequential, parallel, loop, and selective). Through incorporation of the proposed MIP models, the SSOS with a mixed composition structure can be tackled. As transportation is indispensable in cloud manufacturing environment, the models also optimize routing decisions within a given hybrid hub-and-spoke transportation network in which the central decision is to optimally determine whether a shipment between a pair of distributed manufacturing resources is routed directly or using hub facilities. Unlike the majority of previous research undertaken in cloud manufacturing, it is assumed that manufacturing resources are not continuously available for processing but the start time and end time of their occupancy interval are known in advance. The performance of the proposed models is evaluated through solving different scenarios in the SSOS. Moreover, in order to examine the robustness of the results, a series of sensitivity analysis are conducted on key parameters. The outcomes of this study demonstrate that the consideration of transportation and availability not only can change the results of the SSOS significantly, but also is necessary for obtaining more realistic solutions. The results also show that routing within a hybrid hub-and-spoke transportation network, compared with a pure hub-and-spoke network or a pure direct network, leads to more flexibility and has advantage of cost and time saving. The level of saving depends on the value of discount factor for decreasing transportation cost between hub facilities.