Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications (Foundations and Trends? in Technology, Information and Operations Management)

Munson, Charles L; Luo, Lan; Huang, Xiaohui

ISBN 10: 1638283389 / ISBN 13: 9781638283386

Published by Now Publishers, 2024

About this Item

Print on Demand.

Seller Inventory # 395452766

Bibliographic Details

• Title: Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications (Foundations and Trends? in Technology, Information and Operations Management)
• Publisher: Now Publishers
• Publication Date: 2024
• Language: English
• ISBN 10: 1638283389
• ISBN 13: 9781638283386
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications provides a detailed tutorial as a guide for both researchers and practitioners to understand how and when to implement PGSA. While the technique involves a fair amount of "number crunching," it also requires a significant subjective cognitive component. The researcher must consider how to define potential summary variables and subsequently use judgement to determine which to keep and which interaction terms to include. If initial results underperform, the researcher must rethink initial approaches and try again. The tutorial section follows two examples through each step of the process. The monograph is organized as follows. Section 2 describes the PGSA applications that appear in the literature. Section 3 represents the tutorial section, which describes each step in the process and illustrates how each step is applied to two examples: (1) a safety stock model using the fill rate criterion, and (2) a classic linear programming transportation problem. Section 4 presents a full PGSA application for a model used by firms with multiple facilities purchasing many different component parts. The model determines which parts should be purchased locally, which should be purchased centrally, and which should be partially centralized. The PGSA predictive equations do an excellent job at placing parts into the three categories. Section 5 concludes by describing the challenges and limitations of PGSA, along with providing several recommendations for future research.