Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Wiesbaden
Springer Vieweg
[2022]
|
| Subjects: | |
| Online Access: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Physical Description: | xxii, 148 Seiten |
| ISBN: | 9783658391782 3658391782 |
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MARC
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| 245 | 1 | 0 | |a Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling |c Schirin Bär |
| 264 | 1 | |a Wiesbaden |b Springer Vieweg |c [2022] | |
| 300 | |b xxii, 148 Seiten | ||
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| 653 | |a Production Scheduling | ||
| 653 | |a Flexible Manufacturing | ||
| 653 | |a Machine Learning | ||
| 653 | |a Multi-Agent System | ||
| 653 | |a Reinforcement Learning | ||
| 653 | |a Job Shop Scheduling | ||
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Record in the Search Index
| _version_ | 1804184770690678784 |
|---|---|
| adam_text | CONTENTS
1
INTRODUCTION
...............................................................................................
1
1.1
RESEARCH
GOALS
..................................................................................
4
1.2
METHODOLOGY
.....................................................................................
5
1.3
STRUCTURE
OF
THE
THESIS
....................................................................
6
2
REQUIREMENTS
FOR
PRODUCTION
SCHEDULING
IN
FLEXIBLE
MANUFACTURING
...........................................................................................
9
2.1
FOUNDATIONS
OF
FLEXIBLE
JOB-SHOP
SCHEDULING
PROBLEMS
...........
9
2.2
REQUIREMENT
ANALYSIS
OF
FLEXIBLE
SCHEDULING
SOLUTIONS
.............
12
2.2.1
INFLUENCES
ON
WAREHOUSE
CONTROL
SYSTEMS
.......................
12
2.2.2
INFLUENCES
ON
MANUFACTURING
CONTROL
SYSTEMS
..................
16
2.2.3
DERIVED
AND
RANKED
REQUIREMENTS
...................................
21
2.3
STATE
OF
THE
ART:
APPROACHES
TO
SOLVE
JOB-SHOP
SCHEDULING
PROBLEMS
...........................................................................................
23
2.3.1
CONVENTIONAL
SCHEDULING
SOLUTIONS
...................................
24
2.3.2
REINFORCEMENT
LEARNING
SCHEDULING
SOLUTIONS
................
26
2.4
IDENTIFICATION
OF
THE
RESEARCH
GAP
................................................
27
2.5
CONTRIBUTION
OF
THIS
WORK:
EXTENDED
FLEXIBLE
JOB-SHOP
SCHEDULING
PROBLEM
................................................................
29
3
REINFORCEMENT
LEARNING
AS
AN
APPROACH
FOR
FLEXIBLE
SCHEDULING
..........................................................................................
31
3.1
UNDERSTANDING
THE
FOUNDATIONS:
FORMALIZATION
AS
A
MARKOV
DECISION
PROCESS
...............................................................................
31
3.1.1
AGENT-ENVIRONMENT
INTERACTION
.........................................
32
3.1.2
POLICIES
AND
VALUE
FUNCTIONS
.............................................
34
3.1.3
CHALLENGES
ARISING
IN
REINFORCEMENT
LEARNING
..............
36
XI
XII
CONTENTS
3.2
DEEP
Q-LEARNING
..............................................................................
38
3.2.1
TEMPORAL
DIFFERENCE
LEARNING
AND
Q-LEARNING
.................
38
3.2.2
DEEP
Q-NETWORK
..................................................................
39
3.2.3
LOSS
OPTIMIZATION
................................................................
41
3.3
STATE
OF
THE
ART:
COOPERATING
AGENTS
TO
SOLVE
COMPLEX
PROBLEMS
...........................................................................................
42
3.3.1
MULTI-AGENT
LEARNING
METHODS
...........................................
44
3.3.2
LEARNING
IN
COOPERATIVE
MULTI-AGENT
RL
SETUPS
............
46
3.4
SUMMARY
...........................................................................................
48
4
CONCEPT
FOR
MULTI-RESOURCES
FLEXIBLE
JOB-SHOP
SCHEDULING
............
51
4.1
CONCEPT
FOR
AGENT-BASED
SCHEDULING
IN
FMS
..............................
51
4.1.1
OVERALL
CONCEPT
....................................................................
52
4.1.2
JOB
SPECIFICATION
..................................................................
52
4.1.3
PETRI
NET
SIMULATION
............................................................
53
4.2
FORMALIZATION
AS
A
MARKOV
DECISION
PROCESS
...............................
55
4.2.1
ACTION
DESIGNS
......................................................................
56
4.2.2
STATE
DESIGNS
........................................................................
57
4.2.3
REWARD
DESIGN
......................................................................
62
4.3
CONSIDERED
FLEXIBLE
MANUFACTURING
SYSTEM
.................................
66
4.4
EVALUATION
OF
THE
TECHNICAL
FUNCTIONALITIES
...................................
72
4.5
SUMMARY
...........................................................................................
74
5
MULTI-AGENT
APPROACH
FOR
REACTIVE
SCHEDULING
IN
FLEXIBLE
MANUFACTURING
...........................................................................................
75
5.1
TRAINING
SET-UP
.................................................................................
76
5.2
SPECIFICATION
OF
THE
REWARD
DESIGN
...............................................
80
5.3
EVALUATION
OF
SUITABLE
TRAINING
STRATEGIES
.....................................
85
5.3.1
EVALUATION
OF
MARL
ALGORITHMS
.......................................
86
5.3.2
SELECTION
OF
MARL
LEARNING
METHODS
.............................
88
5.3.3
EVALUATION
OF
PARAMETER
SHARING
AND
CENTRALIZED
LEARNING
................................................................................
89
5.4
TRAINING
APPROACH
TO
PRESENT
SITUATIONS
........................................
91
5.5
SUMMARY
............................................................................................
97
6
EMPIRICAL
EVALUATION
OF
THE
REQUIREMENTS
...........................................
99
6.1
GENERALIZATION
TO
VARIOUS
PRODUCTS
AND
MACHINES
..........................
100
6.2
ACHIEVING
THE
GLOBAL
OBJECTIVE
.......................................................
103
6.2.1
COMPARISON
OF
DENSE
AND
SPARSE
GLOBAL
REWARDS
..........
103
6.2.2
COOPERATIVE
BEHAVIOR
..........................................................
106
CONTENTS
XIII
6.3
BENCHMARKING
AGAINST
HEURISTIC
SEARCH
ALGORITHMS
...................
108
6.3.1
EVALUATION
FOR
UNKNOWN
AND
KNOWN
SITUATIONS
..............
109
6.3.2
EVALUATION
OF
REAL-TIME
DECISION-MAKING
.......................
ILL
6.4
CONSOLIDATED
REQUIREMENTS
EVALUATION
.........................................
112
6.5
SUMMARY
...........................................................................................
115
7
INTEGRATION
INTO
A
FLEXIBLE
MANUFACTURING
SYSTEM
.............................
117
7.1
ACCEPTANCE
CRITERIA
FOR
THE
INTEGRATION
CONCEPT
...........................
118
7.2
INTEGRATION
CONCEPT
OF
MARL
SCHEDULING
SOLUTION
.....................
119
7.2.1
INTEGRATION
IN
THE
MES
.......................................................
119
7.2.2
INFORMATION
EXCHANGE
.........................................................
122
7.3
DESIGN
CYCLE
.....................................................................................
123
7.3.1
FUNCTIONING
SCHEDULING
.....................................................
123
7.3.2
EFFICIENT
PRODUCTION
FLOW
.................................................
123
7.3.3
HANDLING
OF
UNFORESEEN
EVENTS
........................................
126
7.3.4
HANDLING
OF
NEW
MACHINE
SKILLS
......................................
127
7.3.5
HANDLING
OF
NEW
MACHINES
................................................
129
7.4
SUMMARY
.............................................................................
132
8
CRITICAL
DISCUSSION
AND
OUTLOOK
............................................................
135
9
SUMMARY
.....................................................................................................
139
BIBLIOGRAPHY
....................................................................................................
141
|
| adam_txt |
CONTENTS
1
INTRODUCTION
.
1
1.1
RESEARCH
GOALS
.
4
1.2
METHODOLOGY
.
5
1.3
STRUCTURE
OF
THE
THESIS
.
6
2
REQUIREMENTS
FOR
PRODUCTION
SCHEDULING
IN
FLEXIBLE
MANUFACTURING
.
9
2.1
FOUNDATIONS
OF
FLEXIBLE
JOB-SHOP
SCHEDULING
PROBLEMS
.
9
2.2
REQUIREMENT
ANALYSIS
OF
FLEXIBLE
SCHEDULING
SOLUTIONS
.
12
2.2.1
INFLUENCES
ON
WAREHOUSE
CONTROL
SYSTEMS
.
12
2.2.2
INFLUENCES
ON
MANUFACTURING
CONTROL
SYSTEMS
.
16
2.2.3
DERIVED
AND
RANKED
REQUIREMENTS
.
21
2.3
STATE
OF
THE
ART:
APPROACHES
TO
SOLVE
JOB-SHOP
SCHEDULING
PROBLEMS
.
23
2.3.1
CONVENTIONAL
SCHEDULING
SOLUTIONS
.
24
2.3.2
REINFORCEMENT
LEARNING
SCHEDULING
SOLUTIONS
.
26
2.4
IDENTIFICATION
OF
THE
RESEARCH
GAP
.
27
2.5
CONTRIBUTION
OF
THIS
WORK:
EXTENDED
FLEXIBLE
JOB-SHOP
SCHEDULING
PROBLEM
.
29
3
REINFORCEMENT
LEARNING
AS
AN
APPROACH
FOR
FLEXIBLE
SCHEDULING
.
31
3.1
UNDERSTANDING
THE
FOUNDATIONS:
FORMALIZATION
AS
A
MARKOV
DECISION
PROCESS
.
31
3.1.1
AGENT-ENVIRONMENT
INTERACTION
.
32
3.1.2
POLICIES
AND
VALUE
FUNCTIONS
.
34
3.1.3
CHALLENGES
ARISING
IN
REINFORCEMENT
LEARNING
.
36
XI
XII
CONTENTS
3.2
DEEP
Q-LEARNING
.
38
3.2.1
TEMPORAL
DIFFERENCE
LEARNING
AND
Q-LEARNING
.
38
3.2.2
DEEP
Q-NETWORK
.
39
3.2.3
LOSS
OPTIMIZATION
.
41
3.3
STATE
OF
THE
ART:
COOPERATING
AGENTS
TO
SOLVE
COMPLEX
PROBLEMS
.
42
3.3.1
MULTI-AGENT
LEARNING
METHODS
.
44
3.3.2
LEARNING
IN
COOPERATIVE
MULTI-AGENT
RL
SETUPS
.
46
3.4
SUMMARY
.
48
4
CONCEPT
FOR
MULTI-RESOURCES
FLEXIBLE
JOB-SHOP
SCHEDULING
.
51
4.1
CONCEPT
FOR
AGENT-BASED
SCHEDULING
IN
FMS
.
51
4.1.1
OVERALL
CONCEPT
.
52
4.1.2
JOB
SPECIFICATION
.
52
4.1.3
PETRI
NET
SIMULATION
.
53
4.2
FORMALIZATION
AS
A
MARKOV
DECISION
PROCESS
.
55
4.2.1
ACTION
DESIGNS
.
56
4.2.2
STATE
DESIGNS
.
57
4.2.3
REWARD
DESIGN
.
62
4.3
CONSIDERED
FLEXIBLE
MANUFACTURING
SYSTEM
.
66
4.4
EVALUATION
OF
THE
TECHNICAL
FUNCTIONALITIES
.
72
4.5
SUMMARY
.
74
5
MULTI-AGENT
APPROACH
FOR
REACTIVE
SCHEDULING
IN
FLEXIBLE
MANUFACTURING
.
75
5.1
TRAINING
SET-UP
.
76
5.2
SPECIFICATION
OF
THE
REWARD
DESIGN
.
80
5.3
EVALUATION
OF
SUITABLE
TRAINING
STRATEGIES
.
85
5.3.1
EVALUATION
OF
MARL
ALGORITHMS
.
86
5.3.2
SELECTION
OF
MARL
LEARNING
METHODS
.
88
5.3.3
EVALUATION
OF
PARAMETER
SHARING
AND
CENTRALIZED
LEARNING
.
89
5.4
TRAINING
APPROACH
TO
PRESENT
SITUATIONS
.
91
5.5
SUMMARY
.
97
6
EMPIRICAL
EVALUATION
OF
THE
REQUIREMENTS
.
99
6.1
GENERALIZATION
TO
VARIOUS
PRODUCTS
AND
MACHINES
.
100
6.2
ACHIEVING
THE
GLOBAL
OBJECTIVE
.
103
6.2.1
COMPARISON
OF
DENSE
AND
SPARSE
GLOBAL
REWARDS
.
103
6.2.2
COOPERATIVE
BEHAVIOR
.
106
CONTENTS
XIII
6.3
BENCHMARKING
AGAINST
HEURISTIC
SEARCH
ALGORITHMS
.
108
6.3.1
EVALUATION
FOR
UNKNOWN
AND
KNOWN
SITUATIONS
.
109
6.3.2
EVALUATION
OF
REAL-TIME
DECISION-MAKING
.
ILL
6.4
CONSOLIDATED
REQUIREMENTS
EVALUATION
.
112
6.5
SUMMARY
.
115
7
INTEGRATION
INTO
A
FLEXIBLE
MANUFACTURING
SYSTEM
.
117
7.1
ACCEPTANCE
CRITERIA
FOR
THE
INTEGRATION
CONCEPT
.
118
7.2
INTEGRATION
CONCEPT
OF
MARL
SCHEDULING
SOLUTION
.
119
7.2.1
INTEGRATION
IN
THE
MES
.
119
7.2.2
INFORMATION
EXCHANGE
.
122
7.3
DESIGN
CYCLE
.
123
7.3.1
FUNCTIONING
SCHEDULING
.
123
7.3.2
EFFICIENT
PRODUCTION
FLOW
.
123
7.3.3
HANDLING
OF
UNFORESEEN
EVENTS
.
126
7.3.4
HANDLING
OF
NEW
MACHINE
SKILLS
.
127
7.3.5
HANDLING
OF
NEW
MACHINES
.
129
7.4
SUMMARY
.
132
8
CRITICAL
DISCUSSION
AND
OUTLOOK
.
135
9
SUMMARY
.
139
BIBLIOGRAPHY
.
141 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Bär, Schirin |
| author_GND | (DE-588)1277082804 |
| author_facet | Bär, Schirin |
| author_role | aut |
| author_sort | Bär, Schirin |
| author_variant | s b sb |
| building | Verbundindex |
| bvnumber | BV048635101 |
| classification_rvk | ZG 9220 |
| ctrlnum | (OCoLC)1341993000 (DE-599)DNB1265700761 |
| discipline | Technik |
| discipline_str_mv | Technik |
| format | Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV048635101 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:16:36Z |
| indexdate | 2024-07-10T09:44:37Z |
| institution | BVB |
| institution_GND | (DE-588)1043386068 |
| isbn | 9783658391782 3658391782 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034010091 |
| oclc_num | 1341993000 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | xxii, 148 Seiten |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Vieweg |
| record_format | marc |
| spelling | Bär, Schirin Verfasser (DE-588)1277082804 aut Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling Schirin Bär Wiesbaden Springer Vieweg [2022] xxii, 148 Seiten txt rdacontent n rdamedia nc rdacarrier Fertigungssystem (DE-588)4154150-9 gnd rswk-swf Automation (DE-588)4003957-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Reihenfolgeproblem (DE-588)4242167-6 gnd rswk-swf Flexible Fertigung (DE-588)4017519-4 gnd rswk-swf Production Scheduling Flexible Manufacturing Machine Learning Multi-Agent System Reinforcement Learning Job Shop Scheduling (DE-588)4113937-9 Hochschulschrift gnd-content Automation (DE-588)4003957-2 s Fertigungssystem (DE-588)4154150-9 s Flexible Fertigung (DE-588)4017519-4 s DE-604 Reihenfolgeproblem (DE-588)4242167-6 s Maschinelles Lernen (DE-588)4193754-5 s Mehragentensystem (DE-588)4389058-1 s Springer Fachmedien Wiesbaden (DE-588)1043386068 pbl Erscheint auch als Online-Ausgabe 9783658391799 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=ff15871cbcb04850a15798517b8f6909&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034010091&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20220818 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Bär, Schirin Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling Fertigungssystem (DE-588)4154150-9 gnd Automation (DE-588)4003957-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Mehragentensystem (DE-588)4389058-1 gnd Reihenfolgeproblem (DE-588)4242167-6 gnd Flexible Fertigung (DE-588)4017519-4 gnd |
| subject_GND | (DE-588)4154150-9 (DE-588)4003957-2 (DE-588)4193754-5 (DE-588)4389058-1 (DE-588)4242167-6 (DE-588)4017519-4 (DE-588)4113937-9 |
| title | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling |
| title_auth | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling |
| title_exact_search | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling |
| title_exact_search_txtP | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling |
| title_full | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling Schirin Bär |
| title_fullStr | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling Schirin Bär |
| title_full_unstemmed | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling Schirin Bär |
| title_short | Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling |
| title_sort | generic multi agent reinforcement learning approach for flexible job shop scheduling |
| topic | Fertigungssystem (DE-588)4154150-9 gnd Automation (DE-588)4003957-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Mehragentensystem (DE-588)4389058-1 gnd Reihenfolgeproblem (DE-588)4242167-6 gnd Flexible Fertigung (DE-588)4017519-4 gnd |
| topic_facet | Fertigungssystem Automation Maschinelles Lernen Mehragentensystem Reihenfolgeproblem Flexible Fertigung Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=ff15871cbcb04850a15798517b8f6909&prov=M&dok_var=1&dok_ext=htm http://www.springer.com/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034010091&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT barschirin genericmultiagentreinforcementlearningapproachforflexiblejobshopscheduling AT springerfachmedienwiesbaden genericmultiagentreinforcementlearningapproachforflexiblejobshopscheduling |