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«SHOP SCHEDULING IN THE PRESENCE OF BATCHING, SEQUENCE-DEPENDENT SETUPS AND INCOMPATIBLE JOB FAMILIES MINIMIZING EARLINESS AND TARDINESS PENALTIES by ...»

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SHOP SCHEDULING IN THE PRESENCE OF BATCHING, SEQUENCE-DEPENDENT

SETUPS AND INCOMPATIBLE JOB FAMILIES MINIMIZING EARLINESS AND

TARDINESS PENALTIES

by

PATRICIA C. BUCHANAN

B.S. Industrial Engineering, University of Florida, 2003

M.S. Industrial Engineering, University of Florida, 2006

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Industrial Engineering and Management Systems in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Spring Term Major Professor: Christopher D. Geiger ABSTRACT The motivation of this research investigation stems from a particular job shop production environment at a large international communications and information technology company in which electro-mechanical assemblies (EMAs) are produced. The production environment of the EMAs includes the continuous arrivals of the EMAs (generally called jobs), with distinct due dates, degrees of importance and routing sequences through the production workstations, to the job shop. Jobs are processed in batches at the workstations, and there are incompatible families of jobs, where jobs from different product families cannot be processed together in the same batch. In addition, there are sequence-dependent setups between batches at the workstations.

Most importantly, it is imperative that all product deliveries arrive on time to their customers (internal and external) within their respective delivery time windows. Delivery is allowed outside a time window, but at the expense of a penalty. Completing a job and delivering the job before the start of its respective time window results in a penalty, i.e., inventory holding cost.

Delivering a job after its respective time window also results in a penalty, i.e., delay cost or emergency shipping cost. This presents a unique scheduling problem where an earlinesstardiness composite objective is considered.

This research approaches this scheduling problem by decomposing this complex job shop scheduling environment into bottleneck and non-bottleneck resources, with the primary focus on effectively scheduling the bottleneck resource. Specifically, the problem of scheduling jobs with unique due dates on a single workstation under the conditions of batching, sequence-dependent ii setups, incompatible job families in order to minimize weighted earliness and tardiness is formulated as an integer linear program. This scheduling problem, even in its simplest form, is NP-Hard, where no polynomial-time algorithm exists to solve this problem to optimality, especially as the number of jobs increases. As a result, the computational time to arrive at optimal solutions is not of practical use in industrial settings, where production scheduling decisions need to be made quickly. Therefore, this research explores and proposes new heuristic algorithms to solve this unique scheduling problem. The heuristics use order review and release strategies in combination with priority dispatching rules, which is a popular and more commonly-used class of scheduling algorithms in real-world industrial settings. A computational study is conducted to assess the quality of the solutions generated by the proposed heuristics. The computational results show that, in general, the proposed heuristics produce solutions that are competitive to the optimal solutions, yet in a fraction of the time. The results also show that the proposed heuristics are superior in quality to a set of benchmark algorithms within this same class of heuristics.

–  –  –

I would first like to thank my advisor, Dr. Christopher D. Geiger, for his guidance and support during the development of my dissertation. He is a great teacher, mentor, and friend.

His knowledge, patience, and dedication to guiding students helped to make this possible.

I would also like to thank Dr. Dima Nazzal who helped me develop my idea and the beginning stages of my research and phases. It was a pleasure working with her. I would also like to thank Dr. Mansooreh Mollaghasemi, Dr. Jennifer Pazour and Dr. Cheryl Xu for their insights, advice, and for serving on my doctoral dissertation committee.

I would also like to thank my former boss Phillip Burroughs, who constantly supported my continued education and was instrumental in helping me balance my work/life schedule. His constant encouragement and advice was one of the drivers that helped me succeed.

I would like to express my gratitude to all my friends and family for all their constant and loving support. They offered words of encouragement, guidance, a shoulder to lean on, babysitting, and reassurance in my ability to successfully finish this journey.

I especially thank my three children. Their smiles, hugs, and love helped me through my rough times. They were truly patient and understanding when mom had to study. They were my constant reminder why I was working so hard.

Most importantly, I would like to say thank you to my best friend and husband. He was incredibly supportive, encouraging, and was my sounding board when I needed to talk about ideas. This journey was as much his as it was mine.





–  –  –

LIST OF FIGURES

LIST OF TABLES

CHAPTER 1 : INTRODUCTION

1.1. Background of the Industrial Setting Motivating This Research Investigation

1.1.1 The Importance of On-Time Delivery

1.1.2 The Coating Room

1.1.3 Process Characteristics within the Coating Room

1.2 Description of the General Problem

1.2.1 Job and Resource Characteristics

1.2.2 Processing Characteristics

1.2.3 The Performance Objective

Decomposition of the Problem – The Bottleneck

1.3 1.4 Objectives of This Research Investigation

1.5 Expected Contribution of This Research Investigation

1.6 Organization of This Document

CHAPTER 2 REVIEW OF THE EXISTING LITERATURE

2.1 Introduction

2.2 Job Shop Scheduling

2.2.1 Heuristic Approaches to the Job Shop Scheduling Problem

2.3 Batching and Sequence-Dependent Setups

–  –  –

2.3.3 Sequence-Dependent Setups

2.4 Unplanned Disruptions

2.5 Minimizing the Earliness and Tardiness Performance Objectives

2.5.1 Approaches for Minimizing Tardiness

2.5.2 Approaches for Minimizing Earliness-Tardiness

2.6 Summary

CHAPTER 3 : RESEARCH APPROACH

3.1 Introduction

3.2 Phase 1: Single Batch Workstation with Sequence-Independent Setups and Equal Job Weights

3.3 Phase 2: Single Batch Workstation with Sequence-Independent Setups and Unequal Weights

Phase 3: Single Batch Workstation with Sequence-Dependent Setups and Unequal Job 3.4 Weights

CHAPTER 4 : MATHEMATICAL MODEL FORMULATION AND COMPUTATIONAL

PERFORMANCE REQUIREMENTS

4.1 Phase 1: Single Batch Machine Scheduling with Sequence-Independent Setups and Equal Earliness and Tardiness Penalties

4.2 Computational Requirements of the Single Batch Machine Scheduling Problem with Sequence-Independent Setups and Equal Earliness and Tardiness Penalties

CHAPTER 5 : HEURISTIC SOLUTION PROCEDURES

5.1 Introduction

5.2 Proposed Solution Heuristics

5.2.1 Apparent Tardiness and Earliness Cost (ATEC) Heuristic

–  –  –

5.2.2.1 The Family Batch Constraint (FBC) Parameter

5.3 Computational Study

5.3.1 Benchmark Heuristics

5.3.2 Experimental Design

5.4 Discussion of Computational Results

CHAPTER 6 : THE SINGLE BATCH WORKSTATION WITH UNEQUALLY-WEIGHTED

PENALTIES AND VARYING BATCH SIZES AND INCOMPATIBLE FAMILIES AND

SEQUENCE-INDEPENDENT SETUPS

6.1 Introduction

6.2 Phase 2: Single Batch Workstation with Sequence-Independent Setups and Unequal Job Weights

6.2.1 Experimental Design

6.2.2 Performance Results of Varying Maximum Batch Sizes

6.2.3 Unequal Weights

6.3 Integrating Order Review and Release Strategies

6.3.1 Experimental Design with ORR Strategies

6.4 Results with ORR Strategies

CHAPTER 7 : THE SINGLE BATCH WORKSTATION WITH UNEQUALLY-WEIGHTED

PENALTIES AND VARYING BATCH SIZES AND INCOMPATIBLE FAMILIES AND

SEQUENCE-DEPENDENT SETUPS

Introduction

7.1 7.2 Mathematical Model

7.2.1 Computational Time of the Mathematical Model vs the Proposed and Benchmark Heuristics

–  –  –

7.3 Discussion of the Performance Results

7.3.1 Batch Size Impact

7.3.2 Effects of Weighted the Earliness and Tardiness Penalties

7.3.3 Overall Performance of Dispatching Rules and ORR versus Proposed Heuristics 109 CHAPTER 8 : SUMMARY AND FUTURE RESEARCH DIRECTIONS

8.1 Summary

8.2 Future Research Directions

8.2.1 Modeling Multiple Serial Machines in a Flow shop

8.2.2 Modeling Multiple Parallel Machines

8.2.3 Machine scheduling with Job Preemption

8.2.4 Due Date Time Windows

APPENDIX A: EQUAL WEIGHTS

APPENDIX B: EARLY PENALY =10/TARDY PENALTY =1

APPENDIX C: TARDY PENALTY=10/EARLY PENALTY = 1

LIST OF REFERENCES

–  –  –

Figure 1.1.

General product flow through the Coating Room.

Figure 2.1.

Summary of existing solution methodologies (modified from Mathirajan et al. 2006).

Figure 5.1.

95% confidence intervals on the performance results for 1 family and 5 families, tight due dates, and high traffic intensity.

Figure 5.2.

95% confidence intervals on the performance results for 1 family and 5 families, tight due dates, and medium traffic intensity.

Figure 5.3.

95% confidence intervals on the performance results for 1 family and 5 families, tight due dates, low traffic intensity.

Figure 5.4.

95% confidence intervals on the performance results for 1 family and 5 families, loose due dates, and low traffic intensity.

Figure 5.5.

95% confidence intervals on the performance results for 1 family and 5 families, tight due dates, and low traffic intensity.

Figure 5.6.

95% confidence intervals on the performance results for 1 family and 5 families, loose due dates, and high traffic intensity.

Figure 5.7.

95% confidence intervals on the performance results for 1 family and 5 families, tight due dates, and all jobs arriving simultaneously.

Figure 5.8.

95% confidence intervals on the performance results for 1 family and 5 families, loose due dates, and all jobs arriving simultaneously.

Figure 5.9.

95% confidence intervals on the performance results for 1 family and 5 families, tight due dates, and medium traffic intensity.

Figure 5.10.

95% confidence intervals on the performance results for 1 family and 5 families, loose due dates, and medium traffic intensity.

Figure 6.1.

95% confidence interval on the performance results with varying batch sizes for tight due dates, all jobs arrive simultaneously, 5 families, and earliness penalty = 10 and tardiness penalty = 1.

–  –  –

Figure 6.3.

95% confidence interval on the performance results with varying batch sizes for tight due dates, medium traffic intensity, 5 families, earliness penalty = 10 and tardiness penalty = 1.

Figure 6.4.

95% confidence interval on the performance results for tight due dates, low traffic intensity, 5 families, and maximum batch size 10.

Figure 6.5.

95% confidence interval on the performance results for tight due dates, jobs arrive simultaneously, 1 family, and maximum batch 3.

Figure 6.6.

95% confidence interval on the performance results for Max batch size 1, tight due dates, medium traffic intensity, and 1 family.

Figure 6.7.

95% confidence interval on the performance results for equal earliness and tardiness penalties, tight due dates, all jobs arriving simultaneously, and 5 families.

Figure 6.8.

95% confidence interval on the performance results for equal earliness and tardiness penalties, tight due dates, all jobs arriving simultaneously, and 5 families.

Figure 6.9.

95% confidence interval on the performance results for equal earliness and tardiness penalties, tight due dates, high traffic intensity, 1 family, dispatching rules compared with ORR strategies combined with dispatching rules.

Figure 6.10.

95% confidence interval on the performance results for equal earliness and tardiness penalties, tight due dates, low traffic, 1 family, dispatching rules compared with ORR strategies combined with dispatching rules.

Figure 6.11.

95% confidence interval on the performance results for earliness penalty=10 and tardiness penalty=1, for tight due dates, high traffic intensity, 1 family, dispatching rules compared with ORR strategies combined with dispatching rules.

Figure 6.12.

95% confidence interval on the performance results for earliness penalty=1 and tardiness penalty=10, loose due dates, high traffic, 5 families, dispatching rules compared with ORR strategies combined with dispatching rules.

Figure 7.1.



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