استثنای نظارت لجستیک خارج از محدوده : رویکرد هستی شناسی چند بعدی با عوامل هوشمند
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1413||2011||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 11, October 2011, Pages 13604–13611
Logistics consists of a complex network of organizations and processes where exception monitoring is critical for the success of logistics service. In order to detect exceptions effectively, exception monitoring requires proper understanding of the possible exceptions. However, the extant exception monitoring approaches or systems still lack sufficient emphasis in exceptions understanding. This paper presents a novel outbound logistics exception monitoring approach by incorporating multi-perspective ontologies and intelligent agents. Specially, the multi-perspective ontologies, involving static ontology, social ontology and dynamic ontology, are firstly employed to develop the taxonomy of the logistics exception, to reflect the situation dependencies of logistics exception and to represent the dynamic nature of business processes. From this point forwards, an outbound exception monitoring system is designed by introducing multi-intelligent agents, which can ensure autonomous, flexible, and collaborative exception monitor in logistics service. Finally, the presented approach and designed system are exhibited through a case study of two ubiquitous logistics exceptions, which indicates that the proposed multi-perspective ontologies provide better understanding of exceptions thereby enabling the designed outbound exception monitoring system to perform well.
Exception management consists of monitoring, diagnosis, and resolution (Wang, Wang, Xu, Wan, & Vogel, 2004b), which respectively concerns the process of keeping track of the activities of the business process, the process of checking the cause of the exception found, and the process of deciding the solution and applying the solution for the exception. For example, a late delivery exception could be detected by monitoring the actual delivery time against the scheduled delivery time. Then by diagnosing the cause of the late delivery (e.g. traffic congestion), a resolution can be presented by assessing all possible options for an effective resolution. Throughout the literature, extant researches place more emphasis on the design of the whole exception management, i.e. how to coordinate every part (e.g. monitoring, diagnosis, resolution) involved in exception management system (Ozkohen and Yolum, 2006 and Wang et al., 2004b). Only a few researches focused on the design of particular part – exception monitoring. They deployed such techniques as exception patterns (Russell, Aalst, & Hofstede, 2006), AND/OR trees (Ozkohen & Yolum, 2006), and fishbone diagrams (Wang, Wang, Kit, & Xu, 2004a). However, those techniques are insufficient to provide proper understanding of exception situations, i.e. what, when, where, and how the problems happen, which, as a matter of fact, is critical to exception monitoring since it could help to improve exception monitoring thereby resulting in better exception management. Therefore, in order to provide a proper understanding of exception situations, this paper firstly introduces multi-perspective ontologies to represent the exception situations, especially for the outbound exception, which refers to the process related to the movement and storage of products from the end of the production line to the end user. Multi-perspective ontologies mean dividing a single ontology into multiple ontologies according to the type of knowledge that is addressed. They are able to avoid ambiguity in single ontology thereby providing better understanding (Kingston, 2008 and Wemmerlöv, 1990). Specially, the multi-perspective ontologies in this study include static ontology (using taxonomy), which presents the things that exists along with its attributes and relationships; social ontology (using dependencies), which presents the organizational structures and interdependencies; and dynamic ontology (using business rules), which presents the dynamic nature of the phenomena such as the state in relation to transition and the process (Jurisca et al., 2004 and Ye et al., 2009). The aforementioned ontologies present the structural nature, interdependencies, and dynamic state of the exception situations respectively. In addition, an effective monitoring system requires to be autonomous (free from human intervention), cooperative (working with other agents/systems effectively), pro-active (taking the initiative in order to achieve the designed objective) and be reactive (responding to the changes in the environment) (Wang et al., 2004a). On the other hand, autonomy, cooperative, pro-activity and reactivity are properties of intelligent agents (Wooldridge, 2001). Hence, considering the complexity of the logistics networks and the properties of intelligent agents, the paper proposes a logistics exception monitoring system using intelligent agents along with reflection of taxonomy, dependencies, and business rules. The proposed system is explained and demonstrated by using two hypothetical cases, by which the ontological views are demonstrated and reflected back to the exception situation. The remainder of the paper is structured as follows: The next section briefly reviews the relevant literatures in logistics exception monitoring and ontology. Section 3 expatiates the multi-perspective ontologies development, including taxonomy (static ontology), dependency in exception situations (social ontology) and business rules in exception scenarios (dynamic ontology). Subsequently, an exception monitoring prototype system is designed in Section 4. In Section 5, two ubiquitous logistics exceptions are employed to demonstrate how the proposed approach can be applied in logistics management. Finally, Section 6 concludes with some recommendations for future research.
نتیجه گیری انگلیسی
Proper understanding of logistics exceptions is critical to efficient and effective exception monitoring. In this study, we propose a novel outbound logistics exception monitoring approach by incorporating multi-perspective ontologies with intelligent agents. This approach provides a more appropriate understanding of outbound logistics exceptions. Based on this approach, an intelligent outbound logistics monitoring systems is designed, in which an intelligent agent framework is developed. The primary contributions of this study can be summarized as follows: •Development of multi-perspective ontologies, including static ontology, social ontology and dynamic ontology, for outbound logistics exceptions. Those ontologies capture the characteristics of exceptions from distinct perspectives; therefore, they provide relatively appropriate understanding of exceptions and further contribute to better logistics exception monitoring and exception management. •A novel rule extraction approach. This approach only requires to identify the edge and superedges involved in the supergraphs generated by developed hierarchical system (like Fig. 3) but it can ensure generate complete, no-redundant and well-structured rules. •Innovation of system design. A multi-agent system is designed to assist outbound logistics exceptions monitoring. The designed intelligent agents framework can identify and diagnosing various outbound logistics exceptions. The future work will primarily concern the implementation of the designed prototype system. After that the system effectiveness can be entirely evaluated. In addition, some specific tasks for some modules in the system will be further explored, e.g. the consistency check of business rules in the supergraph-based context (Beydoun et al., 2005 and Sarkar and Ramaswamy, 2000).