2023, Volume 22, Issue 3
Engineering >> 2023, Volume 22, Issue 3 doi: 10.1016/j.eng.2022.07.020
Dual-Dimensional Manufacturing Service Collaboration Optimization Toward Industrial Internet Platforms
a School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China
b Hubei Digital Manufacturing Key Laboratory, Wuhan University of Technology, Wuhan 430070, China
c Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden
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Abstract
An Industrial Internet platform is acknowledged to be a requisite promoter for smart manufacturing, enabling physical manufacturing resources to be virtualized and permitting resources to collaborate in the form of services. As a central function of the platform, manufacturing service collaboration optimization is dedicated to establishing high-quality service collaboration solutions for manufacturing tasks. Such optimization is inseparable from the functional and amount requirements of a task, which must be satisfied when orchestrating services. However, existing manufacturing service collaboration optimization methods mainly focus on horizontal collaboration among services for functional demands and rarely consider vertical collaboration to cover the needed amounts. To address this gap, this paper proposes a dual-dimensional service collaboration methodology that combines functional and amount collaboration. First, a multi-granularity manufacturing service modeling method is presented to describe services. On this basis, a dual-dimensional manufacturing service collaboration optimization (DMSCO) model is formulated. In the vertical dimension, multiple functionally equivalent services form a service cluster to fulfill a subtask; in the horizontal dimension, complementary service clusters collaborate for the entire task. Service selection and amount distribution to the selected services are critical issues in the model. To solve the problem, a multi-objective memetic algorithm with multiple local search operators is tailored. The algorithm embeds a competition mechanism to dynamically adjust the selection probabilities of the local search operators. The experimental results demonstrate the superiority of the algorithm in terms of convergence, solution quality, and comprehensive metrics, in comparison with commonly used algorithms.
Keywords
Manufacturing service collaboration ; Service optimal selection ; Service granularity ; Industrial Internet platform
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