Revenue management for manufacturing companies:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Hochschulschrift/Dissertation Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
2009
|
| Schlagwörter: | |
| Links: | http://www.opus-bayern.de/ku-eichstaett/volltexte/2009/55/ http://d-nb.info/997408154/34 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017685662&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVIII, 156 Bl. graph. Darst. |
Internformat
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Datensatz im Suchindex
| DE-BY-TUM_katkey | 1699998 |
|---|---|
| _version_ | 1821934545662902272 |
| adam_text | Titel: Revenue Management for Manufacturing Companies
Autor: Defregger, Florian
Jahr: 2009
Contents
List of Figures xi
List of Tables xv
List of Symbols xix
1 Introduction 1
1.1 Motivation and Outline...................... 1
1.2 Revenue Management....................... 4
1.3 Literature Review......................... 7
2 Empirical Study 11
2.1 Overview.............................. 11
2.2 Companies Using Revenue Management ............ 13
2.3 Potential of Revenue Management................ 15
2.4 Influence of the Size of a Company............... 19
3 Basic Model 23
3.1 Model............................... 23
3.1.1 Model Assumptions.................... 23
3.1.2 Model Formulation.................... 24
3.1.3 Model Classification .................... 26
3.1.4 Creating Problem Instances............... 27
3.2 Evaluating a Policy......................» . 30
3.2.1 Evaluation Equations................... 30
3.2.2 Stationary Probabilities ................. 32
3.2.3 Simulation......................... 33
3.2.4 Value Iteration...........,......... , 35
viii CONTENTS
3.2.5 Comparing Procedures to Evaluate a Policy...... 37
3.2.5.1 Small Problem Instances............ 37
3.2.5.2 A Large Problem Instance........... 42
3.3 Solving the Markov Decision Process.............. 44
3.3.1 Policy Iteration...................... 44
3.3.2 Value Iteration...................... 46
3.3.3 Linear Programming................... 46
3.3.4 Comparing Solution Procedures............. 48
3.3.4.1 Small Problem Instances............ 48
3.3.4.2 A Large Problem Instance........... 50
3.4 A Heuristic Procedure...................... 51
3.5 Numerical Results......................... 56
3.5.1 Comparing the Optimal Policy to a FCFS Policy ... 56
3.5.1.1 Influence of the Traffic Intensity........ 59
3.5.1.2 Influence of the Tightness of Lead Times . . • 62
3.5.2 Comparing the Heuristic to an Optimal Procedure ... 66
3.5.2.1 Low Traffic Intensity.............. 70
3.5.2.2 High Traffic Intensity.............. 73
3.5.3 Comparing the Heuristic to a FCFS Policy....... ?4
Limited Inventory Capacity 79
4.1 Model............................... 80
4.1.1 Model Formulation.................... 80
4.1.2 Model Classification ................... 85
4.2 A Heuristic Procedure...................... 89
4.2.1 Determining a Maximum Inventory Level........ 9®
4.2.2 Finding a Capacity Allocation Policy.......... 94
4.3 Numerical Results......................... 98
4.3.1 Creating Problem Instances............... 98
4.3.2 Comparing the Optimal Policy to a FCFS Policy . • ¦ 98
4.3.3 Comparing the Heuristic to an Optimal Procedure . • ¦ 106
4.3.4 Comparing the Heuristic to a FCFS Policy....... 1:Ll
Setup Times and Costs
5.1 Model.................,.............II7
5.1.1 Model Formulation.................... 117
5.1.2 Model Classification ..........,........ll$
5.2 A Heuristic Procedure......................120
CONTENTS ix
5.3 Numerical Results......................... 125
5.3.1 Creating Problem Instances............... 125
5.3.2 Comparing the Optimal Policy to a FCFS Policy ... 128
5.3.3 Comparing the Heuristic to an Optimal Procedure . . . 134
5.3.4 Comparing the Heuristic to a FCFS Policy....... 139
6 Conclusions and Future Research 145
Bibliography 151
List of Figures
1.1 Revenue management system, following Talluri and van Ryzin
(2004)............................ . . . 2
1.2 Phases of implementing revenue management ......... 4
1.3 Capacity control at a manufacturing company......... 5
2.1 Proportion of companies that use capacity allocation..... 14
2.2 Proportion of companies that use revenue management .... 14
2.3 Distribution of the average time between receiving an order
and the latest possible production start to fulfill that order . . 15
2.4 Proportion of companies with inflexible production capacities . 17
2.5 Proportion of companies with perishable production capacities 18
2.6 Proportion of the companies which fulfill the conditions to use
revenue management....................... 18
2.7 Proportions of the companies which use revenue management. 20
2.8 Proportions of the companies which have the potential for rev-
enue management......................... 21
3.1 Basic decision problem...................... 24
3.2 Density function of a left triangular [1,5] distribution..... 29
3.3 Procedure of Welch to determine the warrnup period and pro-
cedure to determine the run length of a single replication ... 35
3.4 Histograms and Q-Q plots of 100 running times for solving the
evaluation equations and for obtaining the stationary proba-
bilities with the Gauss-Seidel procedure ,,..,,,......39
3.5 Histograms and Q~Q plots of 100 running times for obtaining
the stationary probabilities with CPLEX, simulation and value
iteration..............................40
xii LIST OF FIGURES
3.6 Histograms and Q-Q plots of 100 running times for obtaining
the optimal policy......................... 49
3.7 Accepting and rejecting order classes.............. 51
3.8 Partial rejection of order class n,ln — un + 1 = 6,Tn= 3 . ... 52
3.9 Comparing policies........................ 53
3.10 Policy 7T = 2............................ 53
3.11 Policies tt+ and tt~........................ 54
3.12 Different run lengths for comparing and evaluating policies . . 56
3.13 Histograms of the percentage deviations AFGFS~opt of the op-
timal policy compared to the FCFS policy........... 58
3.14 Histograms and Q-Q-plots of AFGFS-°pt with a traffic intensity
of p = 1..............................60
3.15 Histograms and Q-Q-plots of AFCFS~opt with a traffic intensity
ofp = 2.5.............................61
3.16 Histograms and Q-Q-plots of AFCFS-°pt with tight lead times
and a traffic intensity of p = 2.5................. 4
3.17 Histograms of the percentage deviations AH~°pt of average re-
wards when comparing the optimal procedure to the heuristic
procedure.............................67
3.18 Two problem instances with the worst performance of the
heuristic..............................69
3.19 Histograms of the percentage deviations AH~opt of the optimal
procedure compared to the heuristic procedure.........?1
3.20 Comparing the FCFS policy, the heuristic policy and the op-
timal policy for the problem instance where AH~opt — 2.9% • 72
3.21 Histograms of AH~opt at high traffic intensities p = 2.5 . . . • 74
3.22 Histograms of the percentage deviations AFGFS~H of the heuris-
tic policy compared to the FCFS policy.............?6
3.23 Histograms of the percentage deviations AFCFS~H at varying
traffic intensities.........................77
4.1 Decision problem with limited inventory capacity....... 80
4.2 Sequence of decisions...............,,..,..-• 82
4.3 Stochastic process resulting from a policy which induces a mul-
tichain structure......................... 86
4-4 Example for a multichain policy which induces two aperiodic
recurrent classes ................ ,....... . • 89
LIST OF FIGURES xiii
4.5 Assumption of the average reward g(I) depending on the max-
imum inventory level /...................... 90
4.6 Initializing the heuristic procedure................ 92
4.7 Setting Ix and I2 if / has the same distance to the lower and
to the upper bound........................ 93
4.8 Setting Ii and 1% if / does not have the same distance to the
lower and to the upper bound ................... 93
4.9 Accepting and rejecting order classes.............. 94
4.10 Partial rejection of order class n,ln = 4,un = 3......... 95
4.11 Policy approximation....................... 96
4.12 Comparing policies........................ 97
4.13 Policy n = 2............................ 97
4.14 Policies ?r+ and n~........................ 97
4.15 Histograms of the percentage deviations AFCFS~opt of the e-
optimal policy compared to the FCFS policy.......... 101
4.16 Histograms of the percentage deviations AFGFS~opt at a low
and a high inventory holding cost................103
4.17 Histograms of the percentage deviations AFGFS~opt at varying
traffic intensities .........................105
4.18 Histograms of the percentage deviations AH~opt of the e-optimal
policy compared to the heuristic policy............. 107
4.19 Histograms of the percentage deviations AH~opt at varying
inventory holding costs......................109
4.20 Histograms of the percentage deviations AH~opt at varying
traffic intensities......................... 110
4.21 Histograms of the percentage deviations AFCFS~H of the heuris-
tic policy compared to the FCFS policy.............113
4.22 Histograms of the percentage deviations AFCFS~H at low and
high inventory holding costs...................115
4.23 Histograms of the percentage deviations AFGFS~H at a low and
a high traffic intensity ......................116
5.1 Example for the assumption that the average reward is a con-
cave function of the number of rejected artificial order classes . 121
5.2 Setting wi and w% if Sj has the same distance to the lower
bound and to the upper bound.................124
5.3 Setting w and w2 if w does not have the same distance to the
lower bound and to the upper bound.............. 124
XIV
LIST OF FIGURES
5.4 Histograms of the percentage deviations AFGFS~opt of the op-
timal policy compared to the FCFS policy...........130
5.5 Histograms of the percentage deviations AFCFS~~opt with low
and high setup times.......................132
5.6 Histograms of the percentage deviations AFCFS opt at varying
approximate traffic intensities p.................133
5.7 Histograms of the percentage deviations AH~opt of the optimal
policy compared to the heuristic policy.............135
5.8 Histograms of the percentage deviations AH~opt with low and
high setup times..........................13
5.9 Histograms of the percentage deviations AH~opt at varying
traffic intensities .........................13°
5.10 Histograms of the percentage deviations AFGFS H of the heuris-
tic policy compared to the FCFS policy.............140
5.11 Histograms of the percentage deviations AFCFS~H with low
and high setup times.......................142
5.12 Histograms of the percentage deviations AFCFS~H at varying
traffic intensities .............,............143
6.1 Stochastic process which results in dynamic pricing......148
List of Tables
2.1 Contingency table for the influence of the size of a company
on whether it uses revenue management or not......... 19
2.2 Contingency table for the influence of the size of a company
on its potential to use revenue management ..........21
3.1 Mean and maximum of absolute percentage deviations of the
average rewards compared to the average rewards obtained by
solving the evaluation equations with CPLEX......... 37
3.2 Mean and standard deviation of running times in seconds ... 38
3.3 Wilk-Shapiro test for normality of the running times.....41
3.4 Ranking the solution procedures with regards to the running
times for small problem instances................42
3.5 Memory requirements for solution procedures .........43
3.6 Mean and maximum of absolute percentage deviations of the
average rewards obtained by two solution procedures com-
pared to the average rewards obtained by solving the linear
program with CPLEX...................... 48
3.7 Mean and standard deviation of running times.........48
3.8 Wilk-Shapiro test for normality of the running times.....50
3.9 Problem classes for comparing the optimal policy to a FCFS
policy ............................... 56
3.10 Percentage deviations AFCFS~ pt of average rewards of the op-
timal policy compared to the PCFS policy...........57
3.11 AFCFS~opt with different traffic intensities............ 59
3.12 Avaps -0^ for tight and normal lead times........... 63
3.13 Wilk-Shapiro test for normality ................. 65
3.14 Mann-Whitney test........................65
LIST OF TABLES
3.15 Percentage deviations AH~opt of average rewards obtained by
the heuristic policy compared to the average rewards obtained
by the optimal policy.......................°°
3.16 Percentage deviations AH~opt of average rewards obtained by
the heuristic policy compared to average rewards obtained by
the optimal policy at p = 1.................... °
3.17 Percentage deviations AH-°pt at p = 2.5 ............ 73
3.18 Problem classes for comparing the heuristic procedure to a
FCFS policy............................ 75
3.19 Percentage deviations AFGFS ~H of average rewards of the op-
timal policy compared to the FCFS policy........... °
3.20 AFGFS H with different traffic intensities............76
4.1 Problem classes for comparing the e-optimal policy to a FCFS
policy ...............................99
4.2 Percentage deviations AFGFS~opt of average rewards of the e-
optimal policy compared to the FCFS policy..........1™
4.3 AFCFS~opt with a low and a high inventory holding cost h . ¦ ¦ 1°2
4.4 AFGFS-°pt with different traffic intensities............104
4.5 Percentage deviations AH~opt of average rewards of the e-
optimal policy compared to the heuristic policy.......• ^
4.6 AH~opt with varying inventory holding costs h.........^
4.7 AH opt with varying traffic intensities p.............108
4.8 Problem classes for comparing a FCFS policy to the heuristic
policy......,........................^
4.9 Percentage deviations AFCFS~H of average rewards of the op-
timal policy compared to the FCFS policy...........1
4.10 AFCFS-H with a low and a high inventory holding cost h . ¦ • I13
4.11 AFCFS~H with varying traffic intensities p............ll4
5.1 Problem classes for comparing FCFS policies to e-optimal poli-
cies .................................l*o
5.2 Percentage deviations AFGFS~opt of average rewards of the e-
optimal policy compared to the FCFS policy..........129
5.3 AFCFS~opt with low (r = 0.9) and high (r = 0.5} setup times . 130
5.4 AFGFS~opt with different approximate traffic intensities p ¦ ¦ ¦ 131
5.5 Percentage deviations AH~opt of average rewards of the opti-
mal policy compared to the heuristic policy...........^
LIST OF TABLES xvii
5.6 AH~opt with low (r = 0.9) and high (r = 0.5) setup times ... 136
5.7 AH~opt with different approximate traffic intensities p.....136
5.8 Problem classes for comparing a FCFS policy to the heuristic
policy ...............................139
5.9 Percentage deviations AFCFS ~H of average rewards of the op-
timal policy compared to the heuristic policy..........140
5.10 AFCFS H with low (r = 0.9) and high (r = 0.5) setup times . . 141
5.11 AFCFS~H with different approximate traffic intensities p . . . . 141
|
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| oclc_num | 645443879 |
| open_access_boolean | 1 |
| owner | DE-824 DE-384 DE-473 DE-BY-UBG DE-703 DE-1051 DE-29 DE-12 DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-1049 DE-92 DE-739 DE-898 DE-BY-UBR DE-355 DE-BY-UBR DE-706 DE-20 DE-1102 DE-945 |
| owner_facet | DE-824 DE-384 DE-473 DE-BY-UBG DE-703 DE-1051 DE-29 DE-12 DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-1049 DE-92 DE-739 DE-898 DE-BY-UBR DE-355 DE-BY-UBR DE-706 DE-20 DE-1102 DE-945 |
| physical | XVIII, 156 Bl. graph. Darst. |
| psigel | ebook |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| record_format | marc |
| spellingShingle | Defregger, Florian Revenue management for manufacturing companies Industriebetrieb (DE-588)4026813-5 gnd Revenue Management (DE-588)4444408-4 gnd Verarbeitendes Gewerbe (DE-588)4127067-8 gnd Stochastisches Entscheidungsmodell (DE-588)4508360-5 gnd |
| subject_GND | (DE-588)4026813-5 (DE-588)4444408-4 (DE-588)4127067-8 (DE-588)4508360-5 (DE-588)4113937-9 |
| title | Revenue management for manufacturing companies |
| title_auth | Revenue management for manufacturing companies |
| title_exact_search | Revenue management for manufacturing companies |
| title_full | Revenue management for manufacturing companies by Florian Defregger |
| title_fullStr | Revenue management for manufacturing companies by Florian Defregger |
| title_full_unstemmed | Revenue management for manufacturing companies by Florian Defregger |
| title_short | Revenue management for manufacturing companies |
| title_sort | revenue management for manufacturing companies |
| topic | Industriebetrieb (DE-588)4026813-5 gnd Revenue Management (DE-588)4444408-4 gnd Verarbeitendes Gewerbe (DE-588)4127067-8 gnd Stochastisches Entscheidungsmodell (DE-588)4508360-5 gnd |
| topic_facet | Industriebetrieb Revenue Management Verarbeitendes Gewerbe Stochastisches Entscheidungsmodell Hochschulschrift |
| url | http://www.opus-bayern.de/ku-eichstaett/volltexte/2009/55/ http://d-nb.info/997408154/34 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017685662&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT defreggerflorian revenuemanagementformanufacturingcompanies |