At present, there are mainly two service modes in the field of industrial service, referring to field service and factory service, in which all the service processes occur in user-specified places and the factory workshops, respectively. Their compulsive constraints on the service sites inevitably result in the limited service areas, high service costs, and long service cycles. However, with the market demands on quality and efficiency of industrial services increase, especially with the extensive use of cloud platform, the service schemes of the traditional industrial service modes have gradually been unable to meet user requirements. Therefore, this paper proposes a field-factory hybrid service (FFHS) mode, in which service providers are allowed to transport service resources to the user-specified places for providing field services, and they also can establish temporary factories at certain user sites to provide factory services. FFHS removes the constraints on service locations, so it can generate better industrial service schemes. On this basis, the FFHS process is analyzed and a bi-objective resource scheduling model considering the emergence of cloud platform is established for it. A two- segment code is designed and an enhanced multi-objective jellyfish search (EMOJS) algorithm is developed for solving the above model. In EMOJS, the elitist preservation strategy, a parameter adaptive adjustment strategy and an opposition-based learning strategy are developed to improve the search performance. Comparison experiments with several state-of-the-art algorithms on 16 typical bi-objective instances are carried out and prove that EMOJS possesses better search performance. Case studies on 9 real industrial service instances of different sizes show that the scheduling schemes generated by the FFHS mode have better qualities and faster response speeds, so the its superiority in engineering practice is verified.

目前工业服务领域主要有两种服务模式，即现场服务和工厂服务，所有服务流程分别发生在用户指定场所和工厂车间。他们对服务站点的强制约束，必然导致服务区域有限、服务成本高、服务周期长。然而，随着市场对工业服务质量和效率的要求提高，特别是随着云平台的广泛使用，传统工业服务模式的服务方案已逐渐无法满足用户需求。因此，本文提出了一种现场工厂混合服务（FFHS）模式，允许服务提供者将服务资源运输到用户指定的地点提供现场服务，他们也可以在某些用户地点建立临时工厂，提供工厂服务。FFHS 消除了对服务地点的限制，因此可以产生更好的工业服务方案。在此基础上，对FFHS过程进行分析，建立考虑云平台出现的双目标资源调度模型。设计了一个两段代码，并开发了一种增强的多目标水母搜索（EMOJS）算法来解决上述模型。在 EMOJS 中，开发了精英保留策略、参数自适应调整策略和基于对抗的学习策略来提高搜索性能。在 16 个典型的双目标实例上与几种最先进的算法进行了比较实验，证明 EMOJS 具有更好的搜索性能。