基于HTN的供应链过程优化策略及其应用研究
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摘要
供应链管理涉及到大量潜在的业务过程，这些业务活动由不同的供应链参与方执行，并且一项活动可能由不同的伙伴各方共同担当。只有这些关系和活动被清楚的识别并加以管理，供应链才能高效的运作。传统工业系统中广泛应用的计算机集成制造系统 (Computer Integrated Manufacture System, CIMS) 往往是高度集中的，并依赖于全局计划，系统的运行柔性欠佳，不能满足这种敏捷制造的要求。
本文从供应链生产服务任务的角度来看待供应链的优化。首先分析了供应链管理的特点，提出采用分布式人工智能规划技术可以通过任务描述和分解方法的策略进行供应链优化。然后，在具体方案的层次上，基于层次任务网络（Hierarchical Task Network，HTN）方法的多智能主体系统（Multi-Agent System，MAS）来处理这些潜在的复杂性，该系统是通过对供应链参与各方的关键活动和关系进行识别和建模来实现的，在Agent通信的基础上通过智能的任务分解分配来进一步优化供应链过程。最后，以UN/CAEFECT组织（United Nations Center for Trade Facilitation and Electronic Commerce，联合国促进贸易和电子商务中心）的研究成果为依据，以一套具体业务流程为研究对象，以ebXML为描述背景，给出了基于HTN进行供应链过程优化的步骤和实现方案。
本文提出的供应链的运作管理优化策略旨在实现对供应链上的业务过程进行合理地规划调度。在任务分解分派的基础上，通过供应链上各实体间的合作，充分利用链上资源，进一步加强企业间的业务过程、信息的集成，完成较为复杂的供应链业务过程管理。
The supply chain management involves a potentially large number of business processes performed by a number of different parties. The supply chain could be operated efficiently only when these activities or relations was identified and managed. The CIMS applied widely in traditional industry systems cannot meet the demands of the agility manufacturing for it usually has centered architecture and depends on the global planning so as to be a inflexible alternative.
This dissertation views the optimization from standpoint of supply chain tasks on product and services. Firstly, on the basis of feature analysis of supply chain, the planning technologies, which are widely used in distributed intelligent artificial domain, could be applied to the optimization of supply chain through the policy of task representation and decomposition. Furthermore, on the practical level a multi-agent system combined with the hierarchical task network (HTN) was established to handle the complexity of supply chain management. The system was based on the identification and models of the key activities of the parts of the supply chain and on the intelligent tasks decomposition and dispatch based on the communication among software agents. Finally, this paper gives the steps and schema to optimize the supply chain based on HTN, and takes the practical supply chain processes as research object according to the efforts of UN/CAEFECT (United Nations Center for Trade Facilitation and Electronic Commerce) and adopted the ebXML architecture.
Aimed at planning and scheduling the business processes efficiently, the optimization strategy of supply chain process operation is developed. To archive complex supply chain processes management, the integration of business and information among the enterprises is enforced on basis of decomposition and dispatch of the production or service tasks, the cooperation between the entities of the supply chain, as well the sufficiency application of the production resource.
本文从供应链生产服务任务的角度来看待供应链的优化。首先分析了供应链管理的特点，提出采用分布式人工智能规划技术可以通过任务描述和分解方法的策略进行供应链优化。然后，在具体方案的层次上，基于层次任务网络（Hierarchical Task Network，HTN）方法的多智能主体系统（Multi-Agent System，MAS）来处理这些潜在的复杂性，该系统是通过对供应链参与各方的关键活动和关系进行识别和建模来实现的，在Agent通信的基础上通过智能的任务分解分配来进一步优化供应链过程。最后，以UN/CAEFECT组织（United Nations Center for Trade Facilitation and Electronic Commerce，联合国促进贸易和电子商务中心）的研究成果为依据，以一套具体业务流程为研究对象，以ebXML为描述背景，给出了基于HTN进行供应链过程优化的步骤和实现方案。
本文提出的供应链的运作管理优化策略旨在实现对供应链上的业务过程进行合理地规划调度。在任务分解分派的基础上，通过供应链上各实体间的合作，充分利用链上资源，进一步加强企业间的业务过程、信息的集成，完成较为复杂的供应链业务过程管理。
The supply chain management involves a potentially large number of business processes performed by a number of different parties. The supply chain could be operated efficiently only when these activities or relations was identified and managed. The CIMS applied widely in traditional industry systems cannot meet the demands of the agility manufacturing for it usually has centered architecture and depends on the global planning so as to be a inflexible alternative.
This dissertation views the optimization from standpoint of supply chain tasks on product and services. Firstly, on the basis of feature analysis of supply chain, the planning technologies, which are widely used in distributed intelligent artificial domain, could be applied to the optimization of supply chain through the policy of task representation and decomposition. Furthermore, on the practical level a multi-agent system combined with the hierarchical task network (HTN) was established to handle the complexity of supply chain management. The system was based on the identification and models of the key activities of the parts of the supply chain and on the intelligent tasks decomposition and dispatch based on the communication among software agents. Finally, this paper gives the steps and schema to optimize the supply chain based on HTN, and takes the practical supply chain processes as research object according to the efforts of UN/CAEFECT (United Nations Center for Trade Facilitation and Electronic Commerce) and adopted the ebXML architecture.
Aimed at planning and scheduling the business processes efficiently, the optimization strategy of supply chain process operation is developed. To archive complex supply chain processes management, the integration of business and information among the enterprises is enforced on basis of decomposition and dispatch of the production or service tasks, the cooperation between the entities of the supply chain, as well the sufficiency application of the production resource.
引文
董千里，供应链管理，北京：人民交通出版社，2002.35~65.
黄培清，供应链管理的本质，工业工程与管理，1997，2（6）：12~15
袁小雯，基于移动AGENT的供应链管理研究：[硕士学位论文]，天津；天津大学，2003
http://www.supply-chain.org/
http://www.unece.org/cefact/
http://www.ebXML.org
柴晓路， ebXML、UDDI与XML/EDI比较, http://www-900.ibm.com/developerWorks/cn/xml/x-compare/index.shtml
许焕梅，王正肖，基于多代理技术的敏捷供应链管理系统研究，机械科学与技术，2003，22(2)：309-311
汪良主，支持分布信息系统敏捷化的多代理技术研究：[博士学问论文]，上海：上海交通大学，2002
郁春波,万麟瑞等，基于UML/APN集成建模技术的ISCM模型研究，计算机应用研究，2003，20(6)： 46~48
N. Sadeh, D. Hildum, etc. An agent-based architecture for coordinated mixed-initiative supply chain planning and scheduling, In Working notes of the ACM Autonomous Agents Workshop on Agent-Based Decision Support for Managing the Internet-Enabled Supply Chain, 1999.
马士华，林勇等，供应链管理[M]，北京：机械工业出版社，2000，23~587
赵林度, 基于资源的供应链管理决策模式研究,物流技术，2003，No11：28~30
V.K. Khanna, Prem Vrat, R. Shanker,etc., Developing Causal Relationship for TQM Index for Indian Automobile Sector, Journal of Work Study, Emerald, 2002,51(7)：364-373.
SAP special report，convergence vol2：61~81.
Dinesh Garg, Y. Narahari，Achieving Sharp Deliveries in Supply Chains through Variance Pool Allocation，IEEE International Conference on Robotics and Automation, Washington, DC, USA, 2002
BPAWG,BPAWG Reference Model of the The International Supply Chain, July 2001
Sunil Chopr，PeterMeind，Supply Chain Management: Strategy, Planning and Operation, IIE Transactions (2002) 34：221~222
董进，柴跃廷等, 基于多代理协调的企业敏捷供需链管理模型，计算机集成制造，2000，6（1）：41~45
G. Agha. ACTORS: A Model of Concurrent Computation in Distributed Systems, US：The MIT Press, 1986，54~97
E. Durfee. Coordination of Distributed Problem Solvers. Kluwer Academic Press, 1988
Cohen, P. R., Levesque, H. J., Intention is Choice with Commitment Artificial Intelligence 42,1990, 213~261.
Durfee, E. H., Lesser, V. R., etc., Trends in Cooperative Distributed Problem Solving, IEEE Trans. on Knowledge and Data Engineering,1989, 1 (1): 63~83.
Gasser, L., DAI Approaches to Coordination in Distributed Artificial Intelligence:Theory and Praxis, Kluwer Academic Publishers,1992. 31~51.
Bond, A. H., Commitment: Some DAI insights from Symbolic Interactionist Society, Proc. of 9th Workshop on Distributed AI, 1989.239~261.
W.M.P. van der Aalst, Akhil Kumar, XML Based Schema Definition for Support of Inter-organizational Workflow, 2000.
Kutluhan Erol ,James Hendler etc., Semantics for Hierarchical Task- Network Planning, Technical Report UMIACS TR-94-31, 1994
H. Van Dyke Parunak, Sven Brueckner,etc, How to calm hyperactive agents, AAMAS 2003: 1092-1093
http://www.fipa.org
张艳，史美林等，一个基于Agent实现虚拟企业域间协作的框架，软件学报，2003, 14(增刊):61～73
陈小平等， 一个意图后承的形式理论， 自然辩证法研究（逻辑学专题），2000，16(S1)：28～31
李斌，吕建，基于情境演算的智能体结构，软件学报，2003，14(4)： 733－742
程涛,胡春华, 基于CORBA的分布式多自主体系统研究,2000，11（4）：441～445
Rosy Barruffi, Michela Milano,etc., Distributed AI Planning via Mobile Agents, http://lia.deis.unibo.it/Research/SOMA/
薛宏涛 沈林成等，方兴未艾的多智能体机器人技术，http://www.robotdiy.com/article.php?sid=24
黄双喜，范玉顺，基于Web服务的企业应用集成 2003，9（10）：864～867
Nils J.Nilsson?, 人工智能，北京：中国机械工业出版社，2000.309~453
陶雷，费奇，人工智能中的合作规划，计算机工程与应用，2000,38（17），，26－27
Kautz, H., and Selman, B.. Unifying SAT-based and Graphbased planning. In IJCAI, 1999.318–325.
Refanidis, I., and Vlahavas, I.，A domain independent heuristic for STRIPS worlds based on greedy regression tables. In ECP, 1999.346–358.
史忠植， 智能主体及其应用， 北京：科学出版社；2000.34～430
Erol.K, Hendler J, etc.Complexity results for HTN planning ,Annals of Mathematics and Artificial Intelligence 18，1996，69－93
S. Kambhampati， A Comparative analysis of Partial Order Planning and Task Reduction Planning， SIGART Bulletin，1995，6 (1) 16–25.
谷长兴，冯志勇，基于HTN的供应链规划策略，计算机应用，2003，23（10）：103～106
Manuela Veloso , PDDL by Example, http://www-2.cs.cmu.edu/~mmv/planning/ homework/PDDL_Examples.pdf
The Object Centered Language Manual OCLh -Version1.2, http://scom.hud.ac.uk/planform/resource/sec3.html
Georgeff, M. P. ，Planning， Annual Review in Computer Science 2，1987. 359～400.
Chapman, D.，Planning for Conjunctive Goals， Artificial Intelligence 32，1987. 333～378.
Stephen J. J. Smith,etc.，Computer Bridge A Big Win for AI Planning，American Association for Artificial Intelligence，1998.93～106
S. Badaloni ，M. Berati，Hybrid temporal reasoning for planning and scheduling， In Time-96: Proc. of the 3 rd International Workshop on Temporal Representation and Reasoning, Los Alamitos, CA, 1996. IEEE Computer Society
Erol K, Hendler, UMCP: A Sound and Complete Procedure for Hierarchical Task－Network Planning，Second International Conference on AI Planning System，1994.249~254
Larry Ground, Alexander Kott ,EA Knowledge-Based Tool for Planning of Military Operations: the Coalition Perspective , In Proceedings of the Second International Conference on Knowledge Systems for Coalition Operations 2002
Robert P. Goldman, Karen Zita Haigh, MACBeth: A Constraint-Based Planner, 21st Digital Avionics Systems Conference, 2002.
Smith, S.J.J., Nau, D.S., etc., Computer Bridge：A Big win for AI Planning，AI Magazine, 1998,19(2):93~105
FL. Swafford, P., S. Ghosh, A Model of Global Supply Chain Agility and Its Impact on Competitive Performance, 2000 National DSI Conference Proceedings, 2000,
Marc Cavazza, Fred Charles , Interactive Storytelling-from AI Experiment to New Media, ICEC 2003,2003
http://www.wfmc.org/pr/XPDL_10_Release.pdf
Mohammad Akif，马树奇译，Java XML 程序员参考手册，北京：电子工业出版社，2002.45~310
黄培清，供应链管理的本质，工业工程与管理，1997，2（6）：12~15
袁小雯，基于移动AGENT的供应链管理研究：[硕士学位论文]，天津；天津大学，2003
http://www.supply-chain.org/
http://www.unece.org/cefact/
http://www.ebXML.org
柴晓路， ebXML、UDDI与XML/EDI比较, http://www-900.ibm.com/developerWorks/cn/xml/x-compare/index.shtml
许焕梅，王正肖，基于多代理技术的敏捷供应链管理系统研究，机械科学与技术，2003，22(2)：309-311
汪良主，支持分布信息系统敏捷化的多代理技术研究：[博士学问论文]，上海：上海交通大学，2002
郁春波,万麟瑞等，基于UML/APN集成建模技术的ISCM模型研究，计算机应用研究，2003，20(6)： 46~48
N. Sadeh, D. Hildum, etc. An agent-based architecture for coordinated mixed-initiative supply chain planning and scheduling, In Working notes of the ACM Autonomous Agents Workshop on Agent-Based Decision Support for Managing the Internet-Enabled Supply Chain, 1999.
马士华，林勇等，供应链管理[M]，北京：机械工业出版社，2000，23~587
赵林度, 基于资源的供应链管理决策模式研究,物流技术，2003，No11：28~30
V.K. Khanna, Prem Vrat, R. Shanker,etc., Developing Causal Relationship for TQM Index for Indian Automobile Sector, Journal of Work Study, Emerald, 2002,51(7)：364-373.
SAP special report，convergence vol2：61~81.
Dinesh Garg, Y. Narahari，Achieving Sharp Deliveries in Supply Chains through Variance Pool Allocation，IEEE International Conference on Robotics and Automation, Washington, DC, USA, 2002
BPAWG,BPAWG Reference Model of the The International Supply Chain, July 2001
Sunil Chopr，PeterMeind，Supply Chain Management: Strategy, Planning and Operation, IIE Transactions (2002) 34：221~222
董进，柴跃廷等, 基于多代理协调的企业敏捷供需链管理模型，计算机集成制造，2000，6（1）：41~45
G. Agha. ACTORS: A Model of Concurrent Computation in Distributed Systems, US：The MIT Press, 1986，54~97
E. Durfee. Coordination of Distributed Problem Solvers. Kluwer Academic Press, 1988
Cohen, P. R., Levesque, H. J., Intention is Choice with Commitment Artificial Intelligence 42,1990, 213~261.
Durfee, E. H., Lesser, V. R., etc., Trends in Cooperative Distributed Problem Solving, IEEE Trans. on Knowledge and Data Engineering,1989, 1 (1): 63~83.
Gasser, L., DAI Approaches to Coordination in Distributed Artificial Intelligence:Theory and Praxis, Kluwer Academic Publishers,1992. 31~51.
Bond, A. H., Commitment: Some DAI insights from Symbolic Interactionist Society, Proc. of 9th Workshop on Distributed AI, 1989.239~261.
W.M.P. van der Aalst, Akhil Kumar, XML Based Schema Definition for Support of Inter-organizational Workflow, 2000.
Kutluhan Erol ,James Hendler etc., Semantics for Hierarchical Task- Network Planning, Technical Report UMIACS TR-94-31, 1994
H. Van Dyke Parunak, Sven Brueckner,etc, How to calm hyperactive agents, AAMAS 2003: 1092-1093
http://www.fipa.org
张艳，史美林等，一个基于Agent实现虚拟企业域间协作的框架，软件学报，2003, 14(增刊):61～73
陈小平等， 一个意图后承的形式理论， 自然辩证法研究（逻辑学专题），2000，16(S1)：28～31
李斌，吕建，基于情境演算的智能体结构，软件学报，2003，14(4)： 733－742
程涛,胡春华, 基于CORBA的分布式多自主体系统研究,2000，11（4）：441～445
Rosy Barruffi, Michela Milano,etc., Distributed AI Planning via Mobile Agents, http://lia.deis.unibo.it/Research/SOMA/
薛宏涛 沈林成等，方兴未艾的多智能体机器人技术，http://www.robotdiy.com/article.php?sid=24
黄双喜，范玉顺，基于Web服务的企业应用集成 2003，9（10）：864～867
Nils J.Nilsson?, 人工智能，北京：中国机械工业出版社，2000.309~453
陶雷，费奇，人工智能中的合作规划，计算机工程与应用，2000,38（17），，26－27
Kautz, H., and Selman, B.. Unifying SAT-based and Graphbased planning. In IJCAI, 1999.318–325.
Refanidis, I., and Vlahavas, I.，A domain independent heuristic for STRIPS worlds based on greedy regression tables. In ECP, 1999.346–358.
史忠植， 智能主体及其应用， 北京：科学出版社；2000.34～430
Erol.K, Hendler J, etc.Complexity results for HTN planning ,Annals of Mathematics and Artificial Intelligence 18，1996，69－93
S. Kambhampati， A Comparative analysis of Partial Order Planning and Task Reduction Planning， SIGART Bulletin，1995，6 (1) 16–25.
谷长兴，冯志勇，基于HTN的供应链规划策略，计算机应用，2003，23（10）：103～106
Manuela Veloso , PDDL by Example, http://www-2.cs.cmu.edu/~mmv/planning/ homework/PDDL_Examples.pdf
The Object Centered Language Manual OCLh -Version1.2, http://scom.hud.ac.uk/planform/resource/sec3.html
Georgeff, M. P. ，Planning， Annual Review in Computer Science 2，1987. 359～400.
Chapman, D.，Planning for Conjunctive Goals， Artificial Intelligence 32，1987. 333～378.
Stephen J. J. Smith,etc.，Computer Bridge A Big Win for AI Planning，American Association for Artificial Intelligence，1998.93～106
S. Badaloni ，M. Berati，Hybrid temporal reasoning for planning and scheduling， In Time-96: Proc. of the 3 rd International Workshop on Temporal Representation and Reasoning, Los Alamitos, CA, 1996. IEEE Computer Society
Erol K, Hendler, UMCP: A Sound and Complete Procedure for Hierarchical Task－Network Planning，Second International Conference on AI Planning System，1994.249~254
Larry Ground, Alexander Kott ,EA Knowledge-Based Tool for Planning of Military Operations: the Coalition Perspective , In Proceedings of the Second International Conference on Knowledge Systems for Coalition Operations 2002
Robert P. Goldman, Karen Zita Haigh, MACBeth: A Constraint-Based Planner, 21st Digital Avionics Systems Conference, 2002.
Smith, S.J.J., Nau, D.S., etc., Computer Bridge：A Big win for AI Planning，AI Magazine, 1998,19(2):93~105
FL. Swafford, P., S. Ghosh, A Model of Global Supply Chain Agility and Its Impact on Competitive Performance, 2000 National DSI Conference Proceedings, 2000,
Marc Cavazza, Fred Charles , Interactive Storytelling-from AI Experiment to New Media, ICEC 2003,2003
http://www.wfmc.org/pr/XPDL_10_Release.pdf
Mohammad Akif，马树奇译，Java XML 程序员参考手册，北京：电子工业出版社，2002.45~310