MINMAX的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列免費下載的地點或者是各式教學

Pseudolinear Functions and Optimization

為了解決MINMAX的問題，作者Mishra, Shashi Kant,Upadhyay, Balendu Bhooshan 這樣論述：

Pseudolinear Functions and Optimization is the first book to focus exclusively on pseudolinear functions, a class of generalized convex functions. It discusses the properties, characterizations, and applications of pseudolinear functions in nonlinear optimization problems.The book describes the char

acterizations of solution sets of various optimization problems. It examines multiobjective pseudolinear, multiobjective fractional pseudolinear, static minmax pseudolinear, and static minmax fractional pseudolinear optimization problems and their results. The authors extend these results to locally

Lipschitz functions using Clarke subdifferentials. They also present optimality and duality results for h-pseudolinear and semi-infinite pseudolinear optimization problems.The authors go on to explore the relationships between vector variational inequalities and vector optimization problems involvi

ng pseudolinear functions. They present characterizations of solution sets of pseudolinear optimization problems on Riemannian manifolds as well as results on pseudolinearity of quadratic fractional functions. The book also extends n-pseudolinear functions to pseudolinear and n-pseudolinear fuzzy ma

ppings and characterizations of solution sets of pseudolinear fuzzy optimization problems and n-pseudolinear fuzzy optimization problems. The text concludes with some applications of pseudolinear optimization problems to hospital management and economics.This book encompasses nearly all the publishe

d literature on the subject along with new results on semi-infinite nonlinear programming problems. It will be useful to readers from mathematical programming, industrial engineering, and operations management. Mishra, Shashi Kant; Upadhyay, Balendu Bhooshan

MINMAX進入發燒排行的影片

可適應短時間基本單位的多目標排班之離散最佳化決策模型

為了解決MINMAX的問題，作者鄭鈺晴 這樣論述：

護理師人力缺乏及其妥善排班調度之問題一直是長年未解決的問題，例如世界衛生組織 (WHO) 在 2020 年的一份報告中指出，目前全球仍有將近 600 萬名護理師的短缺，而除了人口老化、慢性病普遍化等常態性之因素，近年的 COVID-19 疫情也是主要推手。本研究沿用先期研究所整理的相關政策及規定，使用 LINGO軟體 建立並提出二元目標規劃 (BGP，Binary Goal Programming) 模型，之後將提出的數學模型應用於一個實際案例上，該問題為我國中部某家醫院的非循環調度、並且是以半小時為時間單位的科門診護理排班問題。研究結果顯示，所提出的模型在符合相關規定之下，可以適應半小時的

專科門診護理排班規則。另外，進一步的研究是，在護理師數量不變的情況下，如同以往類似模型一般，所提出的模型也可以同時處理具有全職護理師和兼職護理師的不同要求情況之下的非循環排班問題。並且在研究最後，利用實際案例醫院所給付之薪水代入模型，以了解實際所需支付的人力成本費用並對其最小化。由於工程及營建管理上的排班問題，例如：機具、施工人員等的排班，在日益競爭的環境下，目前也傾向於將排班基本時間單位縮小為一小時或半小時的狀況，再加上 COVID-19 疫情也導致必須有效應用現場有限的人力資源，所以本研究的成果除了醫療領域之外，也可以適用在土木工程領域，以達到排班最佳化效果。

Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

為了解決MINMAX的問題，作者Masters, Timothy 這樣論述：

Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many imp

ortant techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application.Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. T

his presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics.All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and comment

ed C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You

'll LearnCompute entropy to detect problematic predictorsImprove numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothingCarry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersectio

n rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise couplingHarness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promisingUse Monte-Carlo permutation methods to

assess the role of good luck in performance resultsCompute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisionsWho This Book is ForAnyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Alt

hough all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language. Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an inde

pendent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he work

ed with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored four books on pract

ical applications of neural networks: Practical Neural Network Recipes in C++ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995) Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995).

應用多目標基因演算法求解競爭型加水站選址問題

為了解決MINMAX的問題，作者翁姿婷 這樣論述：

摘　要 iABSTRACT ii誌 謝 iv目　錄 v表目錄 vii圖目錄 viii第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 研究流程 3第二章 文獻探討 6 2.1 水資源 6 2.1.1 水資源的重要性 7 2.1.2 全球水資源分佈現況 9 2.1.3 水污染 11 2.1.4 臺灣水資源現況 15 2.1.5 小結 18 2.2 加水站的興起和臺灣設置現況 19 2.3 設施選址問題 21 2.3.1 競爭型設施選址問題 21 2.3.2 客戶行為不確定性問題 21 2.4 覆蓋問題

23 2.4.1 最大覆蓋位置問題 24 2.4.2 漸進式覆蓋範圍 25 2.5 啟發式演算法 27第三章 研究方法 32 3.1 問題定義與假設 32 3.2 研究架構 33 3.3 數學模型 35 3.4 NSGAIII求解步驟 38第四章 實驗結果與分析 45 4.1 初始建構 45 4.2 績效評量指標 47 4.3 參數設計 48 4.4 結果分析 56第五章 案例分析 65 5.1 不同情境之案例分析 65 5.1.1 情境一與情境二 66 5.1.2 情境一與情境三 70第六章 結論及建議 74 6.

1 結論 74 6.2 未來建議 75參考文獻 76