Efficient production planning and scheduling: an integrated approach with genetic algorithms and simulation
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | German |
| Veröffentlicht: |
Wiesbaden
Dt. Univ.-Verl.
1996
Wiesbaden Gabler |
| Schriftenreihe: | Gabler Edition Wissenschaft : Information-Engineering und IV-Controlling
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 152 S. graph. Darst. |
| ISBN: | 3824464268 |
Internformat
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| 100 | 1 | |a Shiroma, Patricia Jay |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Efficient production planning and scheduling |b an integrated approach with genetic algorithms and simulation |c Patricia Jay Shiroma |
| 264 | 1 | |a Wiesbaden |b Dt. Univ.-Verl. |c 1996 | |
| 264 | 1 | |a Wiesbaden |b Gabler | |
| 300 | |a XIV, 152 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Gabler Edition Wissenschaft : Information-Engineering und IV-Controlling | |
| 502 | |a Zugl.: Regensburg, Univ., Diss., 1996 | ||
| 650 | 0 | 7 | |a Produktionsplanung |0 (DE-588)4047360-0 |2 gnd |9 rswk-swf |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-007413721 | |
Datensatz im Suchindex
| _version_ | 1807501862916587520 |
|---|---|
| adam_text |
TABLE
OF
CONTENTS
1
INTRODUCTION
.
1
1.1
MOTIVATION
.
1
1.2
PROBLEM
DEFINITION
AND
EXTENSIONS
.
5
1.2.1
DEFINITION
OF
THE
STANDARD
JOB-BASED
PRODUCTION
PLANNING
PROBLEM
.
5
1.2.2
DEFICIENCIES
IN
THE
STANDARD
PROBLEM
DEFINITION
.
6
1.2.2.1
TIME-COST
TRADEOFFS
.
6
1.2.2.2
TIME
ASPECT
OF
DYNAMIC
RESOURCE
ALLOCATION
.
7
1.2.3
EXPANDED
PROBLEM
DEFINITION
.
8
1.3
FAILURE
OF
TRADITIONAL
OPERATIONS
RESEARCH
METHODS
TO
SOLVE
PROBLEMS
WITH
NONLINEAR
DEPENDENCIES
.
9
1.3.1
LINEAR
PROGRAMMING
.
9
1.3.2
HILLCLIMBING
METHODS
.
9
1.4
SIMULATION
.
11
1.5
CYBERNETIC
SYSTEM
THEORY
.
12
1.5.1
AUTOMATA
THEORY
.
13
1.5.2
CYBERNETICS
.
15
1.6
GENERATION
OF
INPUT
PARAMETERS
FOR
SIMULATION
STUDIES
.
16
1.6.1
SIMPLE
HEURISTICS
.
16
1.6.2
NEAREST
NEIGHBOR
.
16
1.6.3
BALANCING
MACHINE
LOAD
.
18
1.6.4
EXPERT
SYSTEMS
.
19
1.6.5
ENUMERATION
.
21
1.6.6
DYNAMIC
PROGRAMMING
.
22
1.6.7
BRANCH
AND
BOUND
.
24
1.6.8
SIMULATED
ANNEALING
.
29
2
THE
NATURE
OF
EVOLUTIONARY
ALGORITHMS
.
33
2.1
EVOLUTIONARY
PROGRAMMING
.
35
2.2
EVOLUTIONARY
STRATEGIES
.
36
2.3
GENETIC
ALGORITHMS
.
37
2.3.1
SELECTION
.
38
2.3.2
CROSSOVER
.
39
2.3.3
MUTATION
.
40
2.3.4
CONVERGENCE
.
41
2.3.5
EXAMPLE
OF
A
GENETIC
ALGORITHM
IN
PSEUDO-PASCAL
.
42
3
THEORETICAL
FOUNDATIONS
OF
GENETIC
ALGORITHMS
.
43
3.1
SCHEMA
THEOREM
.
43
3.1.1
SCHEMATA
.
44
3.1.2
UPPER
AND
LOWER
BOUNDS
ON
THE
NUMBER
OF
SCHEMATA
EVALUATED
.
45
3.1.3
HYPERPLANE
SAMPLING
.
45
3.1.4
EFFECTS
OF
FITNESS
PROPORTIONAL
REPRODUCTION
.
47
3.1.5
EFFECTS
OF
CROSSOVER
.
49
3.1.6
EFFECTS
OF
MUTATION
.
50
3.1.7
SCHEMA
THEOREM
SUMMARIZED
.
51
3.2
THE
BUILDING
BLOCK
HYPOTHESIS
.
52
3.3
INTERACTING
ROLES
OF
CROSSOVER
AND
MUTATION
.
54
3.4
SELF-ORGANIZING
SYSTEMS
AND
ARTIFICIAL
LIFE
.
55
3.4.1
ARTIFICIAL
LIFE
.
55
3.4.2
THE
GAME
OF
LIFE
.
56
3.4.3
MANIPULATION
OF
DNA
TO
SOLVE
COMBINATORIAL
PROBLEMS
.
57
3.4.4
GENERATION
OF
COMPUTER
PROGRAMS
WITH
NATURAL
SELECTION
.
59
3.4.5
SIMULATION
OF
A
MARKET
ECONOMY
WITH
AUTONOMOUS
AGENTS
.
61
4
METHODOLOGY
.
63
4.1
CLASSIC
VS.
HYBRID
GENETIC
ALGORITHMS
.
65
4.2
TIME
CONSTRAINTS
WHEN
COMBINING
GENETIC
ALGORITHMS
WITH
SIMULATION
.
66
4.2.1
MINIMIZATION
OF
THE
NUMBER
OF
SIMULATION
RUNS
.
67
4.2.1.1
DELTA
EVALUATION
.
68
4.2.1.2
MESSY
GENETIC
ALGORITHMS
.
68
4.2.1.3
CHROMOSOME
REPRESENTATION
.
69
4.2.1.3.1
BINARY
VS.
REAL
CODING
SCHEMES
.
69
4.2.1.3.2
HIERARCHICAL,
DYNAMIC
DATA
STRUCTURE
.
71
4.2.1.4
MODIFICATION
OF
GENETIC
OPERATORS
.
73
4.2.1.4.1
SELECTION
.
73
4.2.1.4.1.1
GENERATIONAL
VS.
STEADY
STATE
ALGORITHMS
.
74
4.2.1.4.1.2
FITNESS
PROPORTIONAL
VS
.
RANK-BASED
SELECTION
.
75
4.2.1.4.2
MODIFICATIONS
TO
THE
CROSSOVER
OPERATOR
.
77
4.2.1.4.2.1
MULTI-POINT
CROSSOVER
.
77
4.2.1.4.2.2
UNIFORM
CROSSOVER
.
78
4.2.1.4.2.3
INTELLIGENT
CROSSOVER
.
79
4.2.1.4.2.4
ORDER
CROSSOVER
.
80
X
4.2.1.4.3
ORDER-BASED
MUTATION
.
81
4.2.1.5
ADAPTIVE
FEEDBACK
CONTROLLER
TO
VARY
MUTATION
RATE
.
82
4.2.1.6
TABU
LIST
OF
PREVIOUSLY
EVALUATED
SCHEDULES
.
85
4.2.2
MINIMIZATION
OF
THE
PROCESSING
TIME
FOR
EACH
SIMULATION
.
86
4.2.2.1
SIMULATION
MODEL
.
86
4.2.2.1.1
PETRI-NETS
.
87
4.2.2.1.2
DYNAMIC
AGENTS
.
88
4.2.2.2
HIERARCHICAL,
DYNAMIC
DATA
STRUCTURE
.
89
4.3
MULTIOBJECTIVE
FITNESS
FUNCTION
.
91
4.3.1
HARD
CONSTRAINTS
.
91
4.3.2
SOFT
CONSTRAINTS
.
92
4.3.3
COMPETING
OBJECTIVES:
MINIMIZATION
OF
TIME
AND/OR
COSTS
.
93
5
FEASIBILITY
STUDY:
A
HYBRID
GENETIC
ALGORITHM
EMBEDDED
IN
AMTOS
.
97
5.1
AMTOS
SIMULATION
SOFTWARE
.
97
5.2
FEEDBACK
LOOP
BETWEEN
AMTOS
AND
THE
GENETIC
ALGORITHM
.
97
5.3
FIRST
EXPERIMENTS
WITH
SMALL
PROBLEMS
.
99
5.4
RESULTS
.
101
5.5
CONCLUSIONS
.
102
6
CASE
STUDY:
IMPLEMENTATION
OF
A
HYBRID
GENETIC
ALGORITHM
FOR
PRODUCTION
PLANNING
IN
A
LARGE
PHARMACEUTICAL
COMPANY
.
105
6.1
PROBLEM
DESCRIPTION
.
105
6.2
HYBRIDIZATION
.
107
6.2.1
DISYS
OBJECT-ORIENTED
DATA
MANAGEMENT
AND
LOGISTICS
SYSTEM
.
107
6.2.2
AMTOS
SIMULATION
SYSTEM
.
110
6.2.3
GENETIC
ALGORITHM
.
110
6.3
COMPARISON
OF
TWO
DIFFERENT
TYPES
OF
GENETIC
ALGORITHMS
.
113
6.3.1
GENETIC
ALGORITHM
TYPE
1
.
113
6.3.1.1
CONVERGENCE
RATE
.
114
6.3.1.2
MUTATION
RATE
.
115
6.3.1.3
OPTIMIZATION
ACCORDING
TO
DIFFERENT
GOALS
.
116
6.3.2
GENETIC
ALGORITHM
TYPE
2
(WITH
TABU
LIST)
.
117
6.3.2.1
CONVERGENCE
RATE
.
118
6.3.2.2
MUTATION
RATE
.
119
6.3.2.3
OPTIMIZATION
ACCORDING
TO
DIFFERENT
GOALS
.
120
XI
6.4
COMPARISON
TO
OTHER
STOCHASTIC
OPTIMIZATION
METHODS
.
121
6.4.1
MONTE
CARLO
RANDOM
SEARCH
.
122
6.4.2
TABU
SEARCH
.
123
6.4.3
EVOLUTIONARY
STRATEGY
.
125
6.5
RESULTS
.
126
6.6
CONCLUSIONS
.
134
7
SUMMARY
AND
PLANS
FOR
FUTURE
RESEARCH
.
137
7.1
SUMMARY
.
137
7.2
PLANS
FOR
FUTURE
RESEARCH
.
138
7.2.1
SEEDED
GENETIC
ALGORITHMS
.
138
7.2.2
RESCHEDULING
.
139
7.2.3
PARALLEL
GENETIC
ALGORITHMS
.
139
7.2.4
FUZZY
LOGIC
FITNESS
FUNCTIONS
.
141
7.2.5
SELF-ORGANIZING
SYSTEMS:
AUTONOMOUS
AGENTS
.
142
BIBLIOGRAPHY
.
143 |
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| author_facet | Shiroma, Patricia Jay |
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| author_sort | Shiroma, Patricia Jay |
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| bvnumber | BV011068286 |
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| ctrlnum | (OCoLC)231688526 (DE-599)BVBBV011068286 |
| discipline | Wirtschaftswissenschaften |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV011068286 |
| illustrated | Illustrated |
| indexdate | 2024-08-16T00:28:21Z |
| institution | BVB |
| isbn | 3824464268 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007413721 |
| oclc_num | 231688526 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-739 DE-19 DE-BY-UBM DE-12 DE-473 DE-BY-UBG DE-945 DE-703 DE-521 DE-634 DE-188 |
| owner_facet | DE-355 DE-BY-UBR DE-739 DE-19 DE-BY-UBM DE-12 DE-473 DE-BY-UBG DE-945 DE-703 DE-521 DE-634 DE-188 |
| physical | XIV, 152 S. graph. Darst. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | Dt. Univ.-Verl. Gabler |
| record_format | marc |
| series2 | Gabler Edition Wissenschaft : Information-Engineering und IV-Controlling |
| spelling | Shiroma, Patricia Jay Verfasser aut Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation Patricia Jay Shiroma Wiesbaden Dt. Univ.-Verl. 1996 Wiesbaden Gabler XIV, 152 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Gabler Edition Wissenschaft : Information-Engineering und IV-Controlling Zugl.: Regensburg, Univ., Diss., 1996 Produktionsplanung (DE-588)4047360-0 gnd rswk-swf Genetischer Algorithmus (DE-588)4265092-6 gnd rswk-swf Reihenfolgeproblem (DE-588)4242167-6 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Produktionsplanung (DE-588)4047360-0 s Genetischer Algorithmus (DE-588)4265092-6 s DE-604 Reihenfolgeproblem (DE-588)4242167-6 s DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007413721&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Shiroma, Patricia Jay Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation Produktionsplanung (DE-588)4047360-0 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Reihenfolgeproblem (DE-588)4242167-6 gnd |
| subject_GND | (DE-588)4047360-0 (DE-588)4265092-6 (DE-588)4242167-6 (DE-588)4113937-9 |
| title | Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation |
| title_auth | Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation |
| title_exact_search | Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation |
| title_full | Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation Patricia Jay Shiroma |
| title_fullStr | Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation Patricia Jay Shiroma |
| title_full_unstemmed | Efficient production planning and scheduling an integrated approach with genetic algorithms and simulation Patricia Jay Shiroma |
| title_short | Efficient production planning and scheduling |
| title_sort | efficient production planning and scheduling an integrated approach with genetic algorithms and simulation |
| title_sub | an integrated approach with genetic algorithms and simulation |
| topic | Produktionsplanung (DE-588)4047360-0 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Reihenfolgeproblem (DE-588)4242167-6 gnd |
| topic_facet | Produktionsplanung Genetischer Algorithmus Reihenfolgeproblem Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007413721&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT shiromapatriciajay efficientproductionplanningandschedulinganintegratedapproachwithgeneticalgorithmsandsimulation |