CAJ | 학술논문

物联网的飞速发展给生产调度系统带来了前所未有的机遇和挑战，数据的多源异构和连续涌入性、信息的透明性以及人、物料、设备、生产过程、产品等众多对象呈现出的连续动态存在性，这些新特征导致传统的调度优化方法难以适用。在已有研究的基础上，总结了物联网、调度优化方法以及基于情景的建模方法的研究现状和存在的问题，分析基于物联网的在线智能调度涉及到的问题及其特征，并提出其中的关键科学问题是基于情景的在线建模方法；提出该问题未来的研究目标为：针对物联网环境下调度对象状态的动态连续变化性，提供一种在线实时的智能优化调度方法，以最终实现调度优化过程的连续性以及调度优化决策的科学性、有效性和实用性；并详细阐述了未来关于基于情景的建模方法、基于情景的模型实时求解方法和基于情景的在线调度决策支持方法三方面的研究内容，为后续的深入研究做前期的思考和探索。
물련망적비속발전급생산조도계통대래료전소미유적궤우화도전，수거적다원이구화련속용입성、신식적투명성이급인、물료、설비、생산과정、산품등음다대상정현출적련속동태존재성，저사신특정도치전통적조도우화방법난이괄용。재이유연구적기출상，총결료물련망、조도우화방법이급기우정경적건모방법적연구현상화존재적문제，분석기우물련망적재선지능조도섭급도적문제급기특정，병제출기중적관건과학문제시기우정경적재선건모방법；제출해문제미래적연구목표위：침대물련망배경하조도대상상태적동태련속변화성，제공일충재선실시적지능우화조도방법，이최종실현조도우화과정적련속성이급조도우화결책적과학성、유효성화실용성；병상세천술료미래관우기우정경적건모방법、기우정경적모형실시구해방법화기우정경적재선조도결책지지방법삼방면적연구내용，위후속적심입연구주전기적사고화탐색。
The great development of Internet of Things ( IoT) brings chances and challenges for production scheduling systems . Traditional scheduling optimization methods are usually based on human experience , mathematical models or the both .However, under IoT environment , the soaring multi-source and heterogeneous data , the apparent information , and the continuous and dy-namic existence of “humans, materials, facilities, production processes, products, etc.” in production scheduling systems disa-ble these traditional scheduling optimization methods . Based on literature review , the state-of-the-art of IoT, scheduling optimization methods and context-based modeling methods is summarized.For the field of IoT, specific scheduling problems should be considered further to apply the current general IoT the -ories to the practice .For the field of scheduling optimization , existing methods almost developed for structured or semi-structured problems which couldn′t resolve the complex and unstructured problems under IoT environment .For the field of context-based modeling, although existing methods pave the way for the development of context -aware systems, further study is still needed in the aspects of capturing and representing typical contexts from multi-source, heterogeneous, and massive data, and reasoning based on them to realize the modeling process to support the decision making .We conclude that the key scientific issue of IoT-based online intelligent scheduling is the context-based online modeling .The modeling process is “capturing context→represen-ting context→reasoning based on context”, which could realize the translation process of “data→information→model→schedu-ling policies”.Then, the future research goal is presented for dealing with the dynamic and continuous variations of scheduling objects under IoT environment , an online real-time and intelligent optimization method of scheduling should be invented to smooth the scheduling process and to provide scientific , efficient and practical decision support policies .Finally, the future research content is described in detail , which includes the following three aspects:context-based modeling methods , context-based and re-al-time model-solving methods , and context-based online decision support methods of scheduling .For the aspect of context-based modeling methods , the main research content includes the following:①the judgment of context series and the robustness analysis of them,②the representation of context series , and③the distributed modeling methods based on context series .As to context-based and real-time model-solving methods , the main research content includes the following:①online learning-feedback meth-ods based on context ,②distributed online and real-time model-solving algorithms , and③self-adaptive algorithms for distributed models.As to context-based online decision support methods of scheduling , the main research content includes the following:①cooperative and interactive decision-making methods , ②human-computer interactive methods based on context series , ③effec-tiveness and robustness analysis of scheduling decisions , and④the application research of scheduling decision support systems . This exploring work would pave the way for future research of the IoT-based decision support systems of scheduling .And the re-search results could have wide application in areas of production scheduling and logistics scheduling .