An Extended Bayesian Belief Network Model of Multi-agent Systems for Supply Chain Managements View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2003

AUTHORS

Ye Chen , Yun Peng

ABSTRACT

In this paper, we describe our on-going research on uncertainty analysis in Multi-agent Systems for Supply Chain Management (MASCM). In a MASCM, an agent consists of automation processes within a legal entity in the specific supply chain network. It conducts supply chain planning, execution and cooperation on behalf of its owner. Each day these agents have to process a large volume of data from different sources with mixed signals not to be anticipated in advance. Thus, one challenge every agent has to face in this volatile environment is to quickly identify the impact of unexpected events, and take proper adjustments in both local procedures and related cross-boundary interactions. To facilitate the study of uncertainty in the complex system of MASCM, we model agent system behaviors by abstracting its significant operational aspects as observation, propagation and update of uncertainty ifnromation. The resulting theoretical model, called an extended Bayesian Belief Network (eBBN), may serve as the basis for developing an uncertainty management component for a large-scale electronic supply chain system. We also briefly describe ways this model can be used to solve different supply chain tasks and some simulation results that demonstrate the power of this model in improving the system performance. More... »

PAGES

335-346

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-45173-0_25

DOI

http://dx.doi.org/10.1007/978-3-540-45173-0_25

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018663987


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/15", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Commerce, Management, Tourism and Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1503", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Business and Management", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA", 
          "id": "http://www.grid.ac/institutes/grid.266673.0", 
          "name": [
            "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Ye", 
        "id": "sg:person.016231355743.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016231355743.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA", 
          "id": "http://www.grid.ac/institutes/grid.266673.0", 
          "name": [
            "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Peng", 
        "givenName": "Yun", 
        "id": "sg:person.01136741416.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136741416.72"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2003", 
    "datePublishedReg": "2003-01-01", 
    "description": "In this paper, we describe our on-going research on uncertainty analysis in Multi-agent Systems for Supply Chain Management (MASCM). In a MASCM, an agent consists of automation processes within a legal entity in the specific supply chain network. It conducts supply chain planning, execution and cooperation on behalf of its owner. Each day these agents have to process a large volume of data from different sources with mixed signals not to be anticipated in advance. Thus, one challenge every agent has to face in this volatile environment is to quickly identify the impact of unexpected events, and take proper adjustments in both local procedures and related cross-boundary interactions. To facilitate the study of uncertainty in the complex system of MASCM, we model agent system behaviors by abstracting its significant operational aspects as observation, propagation and update of uncertainty ifnromation. The resulting theoretical model, called an extended Bayesian Belief Network (eBBN), may serve as the basis for developing an uncertainty management component for a large-scale electronic supply chain system. We also briefly describe ways this model can be used to solve different supply chain tasks and some simulation results that demonstrate the power of this model in improving the system performance.", 
    "editor": [
      {
        "familyName": "Truszkowski", 
        "givenName": "Walt", 
        "type": "Person"
      }, 
      {
        "familyName": "Hinchey", 
        "givenName": "Mike", 
        "type": "Person"
      }, 
      {
        "familyName": "Rouff", 
        "givenName": "Chris", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-45173-0_25", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-40725-6", 
        "978-3-540-45173-0"
      ], 
      "name": "Innovative Concepts for Agent-Based Systems", 
      "type": "Book"
    }, 
    "keywords": [
      "multi-agent systems", 
      "supply chain management", 
      "agent system behaviors", 
      "chain management", 
      "supply chain tasks", 
      "supply chain planning", 
      "Bayesian belief networks", 
      "belief network model", 
      "belief network", 
      "supply chain network", 
      "automation process", 
      "supply chain system", 
      "chain planning", 
      "network model", 
      "management components", 
      "system behavior", 
      "Bayesian belief network model", 
      "chain network", 
      "complex systems", 
      "volatile environment", 
      "system performance", 
      "unexpected events", 
      "simulation results", 
      "large volumes", 
      "network", 
      "chain system", 
      "operational aspects", 
      "cross-boundary interactions", 
      "execution", 
      "system", 
      "different sources", 
      "task", 
      "study of uncertainty", 
      "model", 
      "update", 
      "mixed signals", 
      "environment", 
      "planning", 
      "management", 
      "local procedures", 
      "performance", 
      "uncertainty analysis", 
      "owners", 
      "challenges", 
      "entities", 
      "legal entities", 
      "cooperation", 
      "behalf", 
      "way", 
      "uncertainty", 
      "data", 
      "advances", 
      "research", 
      "aspects", 
      "agents", 
      "power", 
      "process", 
      "signals", 
      "components", 
      "proper adjustment", 
      "results", 
      "basis", 
      "source", 
      "propagation", 
      "behavior", 
      "analysis", 
      "interaction", 
      "theoretical model", 
      "procedure", 
      "volume", 
      "impact", 
      "events", 
      "adjustment", 
      "observations", 
      "study", 
      "paper", 
      "days"
    ], 
    "name": "An Extended Bayesian Belief Network Model of Multi-agent Systems for Supply Chain Managements", 
    "pagination": "335-346", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018663987"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-45173-0_25"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-45173-0_25", 
      "https://app.dimensions.ai/details/publication/pub.1018663987"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-06-01T22:28", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/chapter/chapter_144.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-45173-0_25"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-45173-0_25'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-45173-0_25'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-45173-0_25'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-45173-0_25'


 

This table displays all metadata directly associated to this object as RDF triples.

162 TRIPLES      23 PREDICATES      105 URIs      96 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-45173-0_25 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:15
4 anzsrc-for:1503
5 schema:author N1cfee9b3790a468ebac17bdc27762653
6 schema:datePublished 2003
7 schema:datePublishedReg 2003-01-01
8 schema:description In this paper, we describe our on-going research on uncertainty analysis in Multi-agent Systems for Supply Chain Management (MASCM). In a MASCM, an agent consists of automation processes within a legal entity in the specific supply chain network. It conducts supply chain planning, execution and cooperation on behalf of its owner. Each day these agents have to process a large volume of data from different sources with mixed signals not to be anticipated in advance. Thus, one challenge every agent has to face in this volatile environment is to quickly identify the impact of unexpected events, and take proper adjustments in both local procedures and related cross-boundary interactions. To facilitate the study of uncertainty in the complex system of MASCM, we model agent system behaviors by abstracting its significant operational aspects as observation, propagation and update of uncertainty ifnromation. The resulting theoretical model, called an extended Bayesian Belief Network (eBBN), may serve as the basis for developing an uncertainty management component for a large-scale electronic supply chain system. We also briefly describe ways this model can be used to solve different supply chain tasks and some simulation results that demonstrate the power of this model in improving the system performance.
9 schema:editor N717ba8f4fb8b411b98e861341623132b
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N0569cbb58d5e4aac9d4dd7f8cf319cdd
14 schema:keywords Bayesian belief network model
15 Bayesian belief networks
16 adjustment
17 advances
18 agent system behaviors
19 agents
20 analysis
21 aspects
22 automation process
23 basis
24 behalf
25 behavior
26 belief network
27 belief network model
28 chain management
29 chain network
30 chain planning
31 chain system
32 challenges
33 complex systems
34 components
35 cooperation
36 cross-boundary interactions
37 data
38 days
39 different sources
40 entities
41 environment
42 events
43 execution
44 impact
45 interaction
46 large volumes
47 legal entities
48 local procedures
49 management
50 management components
51 mixed signals
52 model
53 multi-agent systems
54 network
55 network model
56 observations
57 operational aspects
58 owners
59 paper
60 performance
61 planning
62 power
63 procedure
64 process
65 propagation
66 proper adjustment
67 research
68 results
69 signals
70 simulation results
71 source
72 study
73 study of uncertainty
74 supply chain management
75 supply chain network
76 supply chain planning
77 supply chain system
78 supply chain tasks
79 system
80 system behavior
81 system performance
82 task
83 theoretical model
84 uncertainty
85 uncertainty analysis
86 unexpected events
87 update
88 volatile environment
89 volume
90 way
91 schema:name An Extended Bayesian Belief Network Model of Multi-agent Systems for Supply Chain Managements
92 schema:pagination 335-346
93 schema:productId Ncdb40a4788224fb19bdfc1547e415b11
94 Ne1852fca3f434d54bd2ce20be82433fe
95 schema:publisher N58d9f72510d14353bf2f23633dd3d02e
96 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018663987
97 https://doi.org/10.1007/978-3-540-45173-0_25
98 schema:sdDatePublished 2022-06-01T22:28
99 schema:sdLicense https://scigraph.springernature.com/explorer/license/
100 schema:sdPublisher N1cf5f59ecff94d51bc748b0160ca4be1
101 schema:url https://doi.org/10.1007/978-3-540-45173-0_25
102 sgo:license sg:explorer/license/
103 sgo:sdDataset chapters
104 rdf:type schema:Chapter
105 N0569cbb58d5e4aac9d4dd7f8cf319cdd schema:isbn 978-3-540-40725-6
106 978-3-540-45173-0
107 schema:name Innovative Concepts for Agent-Based Systems
108 rdf:type schema:Book
109 N1cf5f59ecff94d51bc748b0160ca4be1 schema:name Springer Nature - SN SciGraph project
110 rdf:type schema:Organization
111 N1cfee9b3790a468ebac17bdc27762653 rdf:first sg:person.016231355743.14
112 rdf:rest Nfab8829e4b9f4d28837727e4c0f95bc2
113 N58d9f72510d14353bf2f23633dd3d02e schema:name Springer Nature
114 rdf:type schema:Organisation
115 N70c01ae79d2541a7877f88e3133e671a rdf:first Ncc9c04d6e1c74a49a2158814c4edeead
116 rdf:rest Na89c3c277e4c45f78542f750c4d3903f
117 N717ba8f4fb8b411b98e861341623132b rdf:first N71ee338ca90b4c19a71b1f62e4e13c4d
118 rdf:rest N70c01ae79d2541a7877f88e3133e671a
119 N71ee338ca90b4c19a71b1f62e4e13c4d schema:familyName Truszkowski
120 schema:givenName Walt
121 rdf:type schema:Person
122 N91f59b25370a43cbb8fc6919468532e4 schema:familyName Rouff
123 schema:givenName Chris
124 rdf:type schema:Person
125 Na89c3c277e4c45f78542f750c4d3903f rdf:first N91f59b25370a43cbb8fc6919468532e4
126 rdf:rest rdf:nil
127 Ncc9c04d6e1c74a49a2158814c4edeead schema:familyName Hinchey
128 schema:givenName Mike
129 rdf:type schema:Person
130 Ncdb40a4788224fb19bdfc1547e415b11 schema:name doi
131 schema:value 10.1007/978-3-540-45173-0_25
132 rdf:type schema:PropertyValue
133 Ne1852fca3f434d54bd2ce20be82433fe schema:name dimensions_id
134 schema:value pub.1018663987
135 rdf:type schema:PropertyValue
136 Nfab8829e4b9f4d28837727e4c0f95bc2 rdf:first sg:person.01136741416.72
137 rdf:rest rdf:nil
138 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
139 schema:name Information and Computing Sciences
140 rdf:type schema:DefinedTerm
141 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
142 schema:name Artificial Intelligence and Image Processing
143 rdf:type schema:DefinedTerm
144 anzsrc-for:15 schema:inDefinedTermSet anzsrc-for:
145 schema:name Commerce, Management, Tourism and Services
146 rdf:type schema:DefinedTerm
147 anzsrc-for:1503 schema:inDefinedTermSet anzsrc-for:
148 schema:name Business and Management
149 rdf:type schema:DefinedTerm
150 sg:person.01136741416.72 schema:affiliation grid-institutes:grid.266673.0
151 schema:familyName Peng
152 schema:givenName Yun
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136741416.72
154 rdf:type schema:Person
155 sg:person.016231355743.14 schema:affiliation grid-institutes:grid.266673.0
156 schema:familyName Chen
157 schema:givenName Ye
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016231355743.14
159 rdf:type schema:Person
160 grid-institutes:grid.266673.0 schema:alternateName Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA
161 schema:name Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA
162 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...