Introduction to Mathematical Models in Operations Planning

by Halit Alper Tayali

Overview

Discover the intricate nature of a company's production function and the comprehensive principles of planning operations in this book. Through practical applications and enriched by numerical examples, readers gain essential knowledge of elementary mathematical methods in operations planning. The inclusion of the powerful R programming language, accompanied by code scripts and real-world examples, enhances the learning experience. Blending theory with practice, this resource equips readers with the tools necessary to optimize production systems, make informed decisions, and gain a competitive edge in today's dynamic business landscape.

Product Details

ISBN-13:	9781032192017
Publisher:	CRC Press
Publication date:	01/30/2025
Pages:	122
Product dimensions:	5.44(w) x 8.50(h) x (d)

About the Author

Halit Alper Tayalı is an assistant professor of business with expertise in enhancing productivity and efficiency. As a co-editor of Istanbul Business Research, an ESCI-indexed journal, his commitment to advancing research and knowledge in the field is evident. With a solid academic background, including studies at Istanbul American Robert College, Koç University, and ESC Rennes, he has practical experience through involvement in his family business. His entrepreneurial spirit led him to co-found a software development startup with support from the Scientific and Technological Research Council of Türkiye, highlighting his dedication to innovation and practical applications. As his academic journey continues at Istanbul University, he empowers individuals in the realm of production planning and beyond, driving progress and excellence.

Table of Contents

Preface

CHAPTER 1: Introduction

CHAPTER 2: Operations planning

CHAPTER 3: Manufacturing processes

CHAPTER 4: Facility layout and line balancing

CHAPTER 5: Project scheduling

CHAPTER 6: Work center scheduling

CHAPTER 7: Linear programming in operations planning

CHAPTER 8: Aggregate operations planning

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