Research on multi-factory combination optimization based on DOSTAR

Abstract

With the development of industrial big data, it has become an important research direction to use combinatorial optimization to coordinate multi-objective problems in complex manufacturing scenarios with multiple factories. At present, most of the multi-objective problems are decomposed into single-objective solutions. However, it is difficult to resolve the contradiction between multiple goals. There are many participants in multi-objective problems and complex data types, so there is no suitable research method at present. Based on big data, this paper integrates various aspects of supply chain management of multiple factories, and proposes a DOSTAR combined model. On the one hand, it conducts knowledge discovery based on the fusion of human-cyber-physical ternary data, on the other hand, it conducts multi-objective optimization through knowledge structure. Among them, the most important thing is to establish the six-tuple as the basic model. Then the space weight, time weight and decision weight are obtained through the weight sub-model. Finally, the improved reinforcement learning algorithm is used to extract relevant new knowledge and complete multi-objective coordination. This article takes the supply chain management of Haier water heaters as an example, using the above-mentioned combined model, and the experimental results show that the purpose of improving performance has been achieved. • This article builds a combined optimization model based on knowledge discovery and reinforcement learning. • The combined model mainly includes: six tuples, weight sub-models (AHP and spatiotemporal data), and improved reinforcement learning algorithms. • This research solves the multi-objective optimization problem in the complex manufacturing environment of multi-factories.