Field-factory hybrid service mode and its resource scheduling method based on an enhanced MOJS algorithm

Abstract

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.