免費諮詢一:這一家的諮詢速度特色就是快
免費諮詢二:申貸流程最快速,24小時內可撥款
一對一的快速立即免費諮詢、配對,十分鐘就能知道您適合的銀行申貸方案是什麼。
立即免費諮詢
免費諮詢三:這家貸款公司評價非常高
免費諮詢四:這一家的諮詢方案很多元,很推薦
免費諮詢五:一群對於專精貸款的專業人士提供相關諮詢
免費諮詢六:這家貸款公司可以承辦軍公教人士
軍公教朋友可以到這間貸款快速找到適合的貸款方案
立即免費諮詢
個人貸款 | 貸款 | 信用貸款 | 債務整合 | 負債整合 | 債務協商 | 個人信貸 | 小額借款 | 信貸 | 信貸利率 |
信貸代辦 | 創業貸款 | 銀行貸款 | 貸款投資 | 買車貸款 | 車貸 | 汽車貸款 | 債務協商 | 卡債處理 | 二胎房貸 |
信用不良信貸貸的下來嗎 該怎辦 | 信用貸款哪裡申請最快核貸 | 信用不良要如何申請信用貸款
個人信貸免費諮詢的網站 | 個人信貸條件,銀行個人信貸比較諮詢 | 小額信貸利率比較標準迷思
三面向分析最低信貸利率條件的迷惑陷阱 | 哪家銀行信貸利息最低 | 銀行個人信貸免費諮詢 | 小額信貸推薦幾家 | 個人信貸利率比較銀行條件如何談 |
RF4165456EDFECE15158DCE |
熱點新知搶先報
內容簡介
Multiobjective Resource Management Problems (m-RMP) involves deciding how to divide a resource of limited availability among multiple demands in a way that optimizes current objectives. RMP is widely used to plan the optimal allocating or management resources process among various projects or business units for the maximum product and the minimum cost. “Resources” might be manpower, assets, raw materials, capital or anything else in limited supply. The solution method of RMP, however, has its own problems; this book identifies four of them along with the proposed methods to solve them. Mathematical models combined with effective multistage Genetic Algorithm (GA) approach help to develop a method for handling the m-RMP. The proposed approach not only can solve relatively large size problems but also has better performance than the conventional GA. And the proposed method provides more flexibility to m-RMP model which is the key to survive under severely competitive environment. We also believe that the proposed method can be adapted to other production-distribution planning and all m-RAP models.
In this book, four problems with m-RMP models will be clearly outlined and a multistage hybridized GA method for finding the best solution is then implemented. Comparison results with the conventional GA methods are also presented. This book also mentions several useful combinatorial optimization models in process system and proposed effective solution methods by using multistage GA.
Note:Part of this book, once published in international journals SCI (Science Direct) inside, be accepted have five articles.
作者簡介:
林吉銘 (Chi-Ming Lin)
電子信箱:chiminglin.tw@gmail.com
學歷
日本國立兵庫教育大學 教育學碩士
日本早稻田大學資訊生產系統研究所5年研究
日本公立前橋工科大學工學研究所 工學博士
經歷
教育部 專員
國立台北教育大學 兼任講師
台北市立教育大學 兼任講師
中央警察大學 兼任講師
國立台南師範大學 兼任講師
美和技術學院 專任講師
長庚技術學院 專任講師
桃園縣公、私立托兒所 評鑑委員
開南大學 專任講師(現職)
目錄
Acknowledgements3
Absract of Chinese 4
Abstract8
Chapter 1 Introduction2
1.1 Background of the Study2
1.2 Related Work7
1.2.1 Genetic Algorithm7
1.2.2 Multiobjective Genetic Algorithm36
1.3 Resource Management Problems54
1.4 Problems in this Dissertation58
1.4.1 A Solution Method for Human RMP Optimization58
1.4.2 A Solution Method for Asset RMP Optimization58
1.4.3 A Solution Method for Capital RMP Optimization58
1.4.4 A Solution Method for Staff Training RMP Optimization59
1.5 Organization of the Dissertation59
Chapter 2 Multistage Genetic Algorithm in Resource Management System65
2.1 Introduction65
2.2 Basic Idea67
2.2.1 Basic Idea Description67
2.2.2 Structure of Resource Management Solution System71
2.2.3 Multistage Network Framework74
2.2.4 Linearization76
2.2.5 Local Search78
2.3 Mathematical Formulations78
2.4 Constructing Multistage Network Structure81
2.4.1 Example One82
2.4.2 Example Two84
2.5 Solving Method by Multistage Genetic Algorithm90
2.5.1 Example Three93
2.5.2 Example Four99
2.6 Experimental Results102
2.6.1 Facility Allocation Problem102
2.6.2 Problem Description of Multiobjective Human RMP104
2.6.3 Experimental Results of Multiobjective Human RMP105
2.7 Summary110
Chapter 3 Optimization for Multiobjective Assets RMP by Multistage GA112
3.1 Introduction112
3.2 Problem Description113
3.2.1 There is Assets Resources Now113
3.2.2 The Data in the Past113
3.2.3 The Problem of Enterprise Boss Expects to be Solved114
3.3 Mathematical Model of Multiobjective Assets RMP115
3.4 Experimental Results and Discussion in First Part122
3.4.1 Experiments Results in the First Part122
3.4.2 Discussion in First Part125
3.5 Experimental Results and Discussion in Second Part134
3.5.1 Experimental Results in Second Part134
3.5.2 Discussion in Second Part139
3.6 Summary144
Chapter 4 Multistage GA for Optimization of Multiobjective Capital RMP149
4.1 Introduction149
4.2 Mathematical Model of Multiobjective Capital RMP153
4.3 Solution Approaches for Multiobjective Capital RMP155
4.3.1 Candidate Mutual Funds Selection155
4.3.2 Multistage Hybrid GA of Multiobjective Capital RMP156
4.3.3 Pareto Optimal Solution159
4.3.4 Adaptive Weight GA161
4.4 Numerical Example of Multiobjective Capital RMP164
4.4.1 Problem Description164
4.4.2 The Goal of the Problem Reached in Research166
4.4.3 Numerical Example of Multiobjective Capital RMP167
4.5 Discussion of Multiobjective Capital RMP175
4.6 Summary178
Chapter 5 Optimization of Staff Training RMP by Multistage GA182
5.1 Introduction182
5.2 Concepts of Competence Set183
5.3 Mathematical Model187
5.4 Solution Approaches by Multistage Hybrid GA191
5.4.1 Genetic Representation191
5.4.2 Evaluation193
5.4.3Selection193
5.5 Numerical Examples195
5.5.1 Problem Description195
5.5.2 The Goal of the Problem Reached in Research196
5.6 Summary209
Chapter 6 Conclusions and Future Research 213
6.1 Conclusions213
6.2 Future Research219
Glossary220
Notations220
Abbreviations222
Bibliography223
List of Publications231
International Journal Papers231
International Conference Papers with Review232
Index235
List of Figure
Figure 1.1: The Flow Chart of Genetic Algorithm11
Figure 1.2: Procedure-code of Basic GA12
Figure 1.3: Coding Space and Solution Space17
Figure 1.4: Feasibility and Legality18
Figure 1.5: The Mapping from Chromosomes to Solutions21
Figure 1.6: An Example of One-cut Point Crossover Operation24
Figure 1.7: Procedure-code of One-cut Point Crossover Operation25
Figure 1.8: An Example of Mutation Operation by Random27
Figure 1.9: An Example of Mutation Operation by Random27
Figure 1.10: Procedure-code of Multiobjective GA54
Figure 2.1: Proposed Structure of Resource Management Solution System72
Figure 2.2: Proposed a Flowchart of Resource Management Solution System73
Figure 2.3: An Example of Complex Multistage Network Framework74
Figure 2.4: Representation of Multistage Network Approach for RMP75
Figure 2.5: Representation Process for RMP83
Figure 2.6: Representation Process for RMP84
Figure 2.7: A Multistage Network of Human RMP90
Figure 2.8: The Code of Random Key-based Encoding in Procedure 194
Figure 2.9: The Code of Weight Generating in Procedure 295
Figure 2.10: An Example of Weight Generating96
Figure 2.11: An Example of One-cut Point Crossover Operator96
Figure 2.12: The Example of Insertion Mutation98
Figure 2.13: Proposed Structure of a Chromosome100
Figure 2.14: An Example of Optimal Allocation Path101
Figure 2.15: Proposed Chromosome Structure for Four Stages Allocation Path101
Figure 2.16: The Pareto Optimal Solutions of Weighted-sum Method107
Figure 2.17: The Pareto Optimal Solutions of Proposed Method108
Figure 3.1: An Example of Complex Multistage Network Framework114
Figure 3.2: The Path Process of Two Objectives in Each Node119
Figure 3.3: Simulation Results for Multiobjective Assets RMP121
Figure 3.4: The Simulation Results of pri-GA124
Figure 3.5: The Simulation Results of msh-GA124
Figure 3.6: Preference Solutions with Pareto Optimal Solutions by pri-GA137
Figure 3.7: Preference Solutions with Pareto Optimal Solutions by msh-GA137
Figure 4.1: Simple Case with Two Objectives160
Figure 4.2: The Procedure of Pareto GA161
Figure 4.3: Adaptive Weights and Adaptive Hyperplane163
Figure 4.4: The Process Path of Two Objectives in Each Node168
Figure 4.5: An Example for Multiobjective Capital RMP169
Figure 4.6: Experiment Results by Two Methods172
Figure 5.1: The Cost Function of CSE184
Figure 5.2: CSE in Multistage Network Model186
Figure 5.3: An Example of State Permutation Encoding for CSE Operation.192
Figure 5.4: An Example of State Permutation Decoding for CSE Operation.192
Figure 5.5: An Example of Evaluation for CSE193
Figure 5.6: An Example of Selection for CSE193
Figure 5.7: The Procedure of msh-GA for Multistage CSE194
Figure 5.8: An Example of CSE for Staff Training RMP198
Figure 5.9: The Process Path of Two Objectives in Each Arc199
Figure 5.10: A Solution Example of Pareto Optimal Solutions for CSE200
Figure 5.11: Simulation Results of CSE for Staff Training RMP205
List of Table
Table 2.1: Transportation Costs102
Table 2.2: Maintenance Costs of Each Facility102
Table 2.3: The Parameters Setting of Experiment102
Table 2.4: Transportation Amounts from Each Facility to Each Consumer103
Table 2.5: Total Cost of Facility Allocate Transportation by Two Methods103
Table 2.6: An Example of Expected Wage of Programmer (Workers)106
Table 2.7: An Example of Expected Product Number of Task (Job)106
Table 2.8: The Parameter Settings of Experiment106
Table 2.9: Experiment Results of Two Methods108
Table 2.10: Experiment Results of Overall Average by Two Methods109
Table 3.1: The Data of the Company in the Past 4 Years117
Table 3.2: An Example of Expected Cost in 4 Districts 118
Table 3.3: An Example of Expected Selling Goods in 4 Districts118
Table 3.4: The Total Number of Feasible Solutions for Process Planning120
Table 3.5: The Parameter Settings of Experiment122
Table 3.6: Experiment Rs of the Pareto Optimal Solutions123
Table 3.7: Experiment Result of Two Methods125
Table 3.8: Same Preference Solution for Minimum Cost127
Table 3.9: Same Preference Solution for Maximum Selling Goods Number129
Table 3.10: Preference for Golden Mean within Pareto Optimal Solutions131
Table 3.11: The Parameter Settings of msh-GA136
Table 3.12: Experiment Results for Pareto Optimal Solutions138
Table 3.13: Preference for Golden Mean within Pareto Optimal Solutions141
Table 4.1: 3-months and 12-months Return Rates for 60 Sample Companies165
Table 4.2: Reordering Data Sets of Mutual Funds165
Table 4.3: The Total Number of Feasible Solutions for Process Planning169
Table 4.4: The Covariance Matrix170
Table 4.5: The Parameters Setting of Experiment170
Table 4.6: Experiment Results of Pareto Optimal Solutions by Two Methods171
Table 4.7: Experiment Results for the Optimal Portfolio174
Table 4.8: The Optimal Portfolio Solution of Sharpe Ratio174
Table 5.1: Total Numbers of Feasible Solutions for CSE200
Table 5.2: An Example of Data for CSE203
Table 5.3: Parameters Settings204
Table 5.4: Pareto Optimal Solutions for Multiobjective CSE204
Table 5.5: Experiment Results of the Pareto Optimal Solutions207
Table 5.6: Experiment Results of Pareto Optimal Solutions208
序
Abstract
Multiobjective Resource Management Problems (m-RMP) involves deciding how to divide a resource of limited availability among multiple demands in a way that optimizes current objectives. RMP is widely used to plan the optimal allocating or management resources process among various projects or business units for the maximum product and the minimum cost. “Resources” might be manpower, assets, raw materials, capital or anything else in limited supply.
The solution method of RMP, however, has its own problems; this thesis identifies four of them along with the proposed methods to solve them. Mathematical models combined with effective multistage Genetic Algorithm (GA) approach help to develop a method for handling the m-RMP. The proposed approach not only can solve relatively large size problems but also has better performance than the conventional GA. And the proposed method provides more flexibility to m-RMP model which is the key to survive under severely competitive environment. We also believe that the proposed method can be adapted to other production-distribution planning and all m-RAP models.
In this thesis, four problems with m-RMP models will be clearly outlined and a multistage hybridized GA method for finding the best solution is then implemented. Comparison results with the conventional GA methods are also presented. This study also mentions several useful combinatorial optimization models in process system and proposed effective solution methods by using multistage GA. In the areas of future research, the methods outlined in this study might be applied to combinatorial optimization of m-RMP involving areas of education, portfolio selection or areas of industrial engineering design, product process planning system amongst many others.
詳細資料
- ISBN:9789866231483
- 規格:平裝 / 258頁 / 16k菊 / 14.8 x 21 cm / 普通級 / 單色印刷 / 初版
- 出版地:台灣
- 本書分類:> >
... 秦始皇三十七年(前210年)七月,秦始皇駕崩於沙丘平臺。秦始皇死後,胡亥繼位,是為秦二世。長久以來,人們在稱讚秦始皇時,也將他視為暴君。這是最大的誤解,秦始皇非但不是暴君,而是公認的千古一帝。 魯迅先生就曾替秦始皇鳴不平,「秦始皇實在冤枉得很,他的吃虧是在二世而亡,一班幫閒們都替新主子去講他的壞話了。不錯,秦始皇燒過書,燒書是為了統一思想,但他沒有燒掉農書和醫書。」之所以有人稱秦始皇是暴君,很大程度上是受到胡亥的影響,因為他才是真正的暴君。 ... 胡亥的殘暴難以想像,司馬遷用「賦斂愈重,成器無已」、「法令誅罰日益刻深」來描述。然而,1976年人們終於見識了胡亥的殘暴。1976年10月,考古人員在發掘兵馬俑同時,在秦陵東側發現了17座陪葬墓群。 當考古人員發掘這些墓地後,倒吸了一口冷氣。據當時的考古日誌描述,「墓地非常寒酸,棺內屍骨非常凌亂,有的頭骨與軀幹相分離,有的頭骨上有箭頭,有的骨幹上有刀痕,這些現象都表明墓主是非正常死亡。」 ... 墓地儘管寒酸,可是棺內陪葬品卻比較豐富,包括金、銀、銅、鐵、陶、玉、蚌、貝、骨、漆器及絲綢殘片等200多件(套),這說明墓主人身份不凡。果不其然,考古人員在發現了兩枚私印,一枚「榮祿」出土於男性墓中,另一枚印文為「陽滋」出土於女性墓中。 專家考證,「陽滋」就是秦始皇的女兒。史書記載,秦始皇有子女至少33人,除了後世熟悉的長子扶蘇,少子胡亥、公子高、公子將閭四人外,其他子女史書上沒有過多描述。然而,胡亥繼位後,殘忍殺害兄弟姐妹的行為,史書上確實有記載。 ... 「六公子戳死於杜」、「十二公子戳死於咸陽」,這些都是胡亥殺害兄弟的證據。公子高看得長孫被迫自殺,深知自己在劫難逃,本來準備逃跑的他擔心家族被屠殺,只好上書稱願意給父皇殉葬,胡亥同意了。 如果說殺害兄弟是為了皇位穩固,可胡亥連自己的姐妹也不放過,比如十公主就被亂刀砍死。試想,胡亥已經殺了好幾個兄弟姐妹,他在乎多殺幾個嗎?於是,秦始皇的子女幾乎都被胡亥誅殺殆盡,並將他們的屍體草草下葬。 ... 對兄弟姐妹殘忍,對老百姓更殘忍,「盡徵其材士五萬人為屯衛咸陽,令教射狗馬禽獸。當食者多,度不足,下調郡縣轉輸菽粟芻稿,皆令自齎糧食,咸陽三百里內不得食其谷。」看到這裡,很多人認為胡亥毫無人性,簡直跟禽獸差不多。朱門酒肉臭,路有凍死骨,這是胡亥統治後期秦朝真實寫照。 參考資料:《始皇本紀》、《李斯列傳》、《趙正書》
文章來源取自於:
每日頭條 https://kknews.cc/history/254kbgz.html
博客來 https://www.books.com.tw/exep/assp.php/888words/products/0010562465
如有侵權,請來信告知,我們會立刻下架。
DMCA:dmca(at)kubonews.com
聯絡我們:contact(at)kubonews.com
花蓮個人信貸利率最低彰化退休生活費用花蓮快速借錢花蓮信用空白如何貸款
信用評分不足? 信貸條件若在信用條件評分不好的情況下也能申辦貸款嗎? 台南銀行房屋貸款利率 查封如何申辦合法代書貸款、房屋貸款、個人信貸怎麼找?4大特色告訴你! 信用不良 基隆信用不良如何貸款負債百萬怎麼辦…我該如何解決負債? 小額信貸10萬 彰化銀行信用貸款最常被詢問的個人信貸、車貸、二胎問題 幫你整理清楚! 苗栗快速借錢 南投貸款諮詢
留言列表