An optimization method of manufacturing service supply chain oriented to network dependency perception

Abstract

The invention discloses an optimization method of manufacturing service supply chain oriented to network dependency perception, and proposes a mathematical model of manufacturing service composition oriented to network dependency perception to describe the optimization problem of manufacturing service supply chain. The method uses a matrix-based coding scheme, thereby formally representing a manufacturing service supply chain candidate. The first element of each column in the matrix represents the composition structure type (selection or parallelism) of the vertically composed service group, while the other elements in the column represent the selected basic candidate services, thereby cooperating to complete the corresponding subtasks. By combining the migration operator of a biogeography-based optimization (BBO) algorithm with an ABC algorithm, the optimization efficiency of a manufacturing service supply chain is improved. The method of the invention is superior to other methods ofthe prior art in solving the optimization problem of the manufacturing service supply chain through the comprehensive comparison of three aspects of practicability, effectiveness and efficiency.

Description

technical field [0001] The invention belongs to the technical field of manufacturing service supply chain optimization, and in particular relates to a method for optimizing a manufacturing service supply chain oriented to networked correlation perception. Background technique [0002] With the continuous development of service-oriented architecture (Service-oriented Architecture, SOA) and cloud computing, distributed manufacturing resources can be packaged into manufacturing services. In the past decade, manufacturing service recommendation methods have been widely used, which can select an optimal manufacturing service from many candidate manufacturing services to complete a single manufacturing task. However, when faced with complex manufacturing tasks, companies prefer to obtain a manufacturing service supply chain. For example, gear manufacturing is usually composed of several subtasks such as forging and normalizing. Each subtask has its own candidate service set, and...

AI Technical Summary

Problems solved by technology

However, traditional manufacturing service supply chain optimization methods usually assume that candidate services are independent of each other, and traditional service composition optimization methods only select a single suitable candidate service to complete a single subtask, while other services with the same functionality are left idle.

Therefore, potentially better manufacturing service supply chain candidates may be ignored because they do not consider the impact of the correlation between services on the QoS value, so the obtained service combination is not optimal

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