A Production Scheduling Model for Plastic Injection Molding Process/ George Ayad Farhat

Supervisor: Irene Samy

Thesis (M.A.)—Nile University, Egypt, 2022 .

"Includes bibliographical references"

Contents:
ACKNOWLEDGEMENT ..................................................................................................................... II
ABSTRACT ...................................................................................................................................... IV
NOMENCLATURE ........................................................................................................................... VII
LIST OF TABLES ............................................................................................................................. VIII
LIST OF FIGURES ............................................................................................................................. IX
INTRODUCTION ............................................................................................................................... 1 1.1 Scheduling of Plastic Injection Molding .......................................................................... 3
1.2 Universal for Metal and Supporting Industries ................................................................ 5
1.3 The impact of scheduling on company performance ....................................................... 8
LITERATURE REVIEW ..................................................................................................................... 10
2.1 Classifications of machine scheduling problems ........................................................... 11
2.2 Scheduling criteria and mathematical modeling ............................................................ 11
2.3 Solution approaches for production scheduling optimization problems ........................ 13
PROBLEM DESCRIPTION AND OBJECTIVES ................................................................................... 17
3.1 Introduction .................................................................................................................... 17
3.2 Problem Statement ......................................................................................................... 17
3.3 Objective function of the optimization model................................................................ 18
3.4 Constraints of the optimization problem ........................................................................ 19
3.5 Formulation of the optimization problem ...................................................................... 21
3.6 Assumptions and limitations .......................................................................................... 23
3.7 Research objectives ........................................................................................................ 24
VI
METHODOLOGY ............................................................................................................................ 26
4.1. Data collection................................................................................................................ 26
4.2 Model development ........................................................................................................ 26
4.3 Model verification .......................................................................................................... 29
4.4 Metaheuristics ................................................................................................................ 30
4.5 Problem division solution approach ............................................................................... 31
4.6 Evaluation of solution approaches ................................................................................. 32
SCHEDULING RESULTS AND ANALYSIS .......................................................................................... 33
5.1 Numerical results............................................................................................................ 33
5.2 A User-friendly schedule generator ............................................................................... 36
5.3 A use case for scheduling framework in Universal for Metal and Supporting Industries 37
4.5 Dynamic framework for decision making in manufacturing organizations. .................. 39
CONCLUSION AND FUTURE WORK ............................................................................................... 41
6.1. Research Limitations ......................................................................................................... 42
6.2. Future Work ....................................................................................................................... 43
REFERENCES ................................................................................................................................. 44
Appendix ...................................................................................................................................... 47

Abstract:
This thesis is an outcome of research collaboration between Nile University and Universal for Metal and Supporting Industries aiming at eliminating the gap between research and industry in Egypt. Being one of the most important manufacturers in the middle east, Universal for Metal and Supporting Industry always tries to increase its operations efficiency. This research focused on production scheduling, specifically in a large plastic injection molding facility. The author addressed a production scheduling problem faced by the company from a practical and a theoretical context. The problem is classified as a parallel machine scheduling and lot sizing problem. In order to identify the problem's nature and boundaries, numerous interviews, analyses, and literature surveys are conducted to produce a suitable mathematical optimization model for the problem. The scheduling optimization model is chosen from literature and various solution approaches are developed for the model. Many experiments are conducted to test the efficiency for each solution approach. After comparing these approaches, and because of the computational complexity of the problem, an improved solution approach based on mixed integer linear programming (MILP) is proposed. The approach proved its efficiency in both solution time and accuracy, compared to other solution approaches. Based on the previous work, a user-friendly scheduling tool is generated using MS-Excel and MATLAB to help production planners develop optimal schedules in a reasonable time with no need to write a code.
From a managerial perspective, a dynamic decision-making framework is proposed to allow efficient data sharing through the organization which enhances the performance of all the organization departments, specifically, the production department. The outcomes of this research allow the company to measure and analyze the dynamics of its production operations and minimize the associated costs. They also allow for continuous improvement for all the organization departments through dynamic feedback and data sharing system. Last of all, this research work is a managerial approach to make the best use of technology and have it impact local industry.

Text in English, abstracts in English .