الفهرس 
Theoretical Framework and Literature ReviewOnly 14 pages are availabe for public view
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Abstract
During the last few decades, the automotive assembly business in Egypt witnessed a remarkable growth rates to reach about 20 major vehicle assemblers and more than 375 companies registered in the General Authority for Industrial Development with a capital of 15 billion Egyptian Pounds nearly. No matter the manufacturing sector is, the assembly process of mixed-car model lines flows according to a standard assumption that implies the existence of different products with different models or shapes and certain common manufacturing characteristics in a certain number of workstations.
The assembling approach commonly adopted in mixed-car model assembly lines is the manual operation that lets different types of workers to work on assembling different product models simultaneously and continuously.
For a given assembly line, managers must find the optimal assignments of workers to sequences of tasks which will help find a schedule of their moves along workstations; in order to minimize the overall makespan/time required for assembling more than one model simultaneously.
Commonly, when managers are scheduling different jobs, the number of workers is not generally taken into account; however, if the number of workers increases or decreases the overall makespan will increase or decrease as well. Therefore, the mathematical relationship can address two critical issues: 1) Screening jobs at different workstations (closed or open), 2) Sequencing these jobs properly to minimize the overall makespan.
Research Problem:
The research problem states that workforce scheduling in automotive industry is affected by multiple factors, one of the main factors is the overall makespan of assembly lines which needs to be minimized.
Time minimization is affected by some constraints, such as workers number, workforce type, workstation type, cycle time of model, number of station, total number of jobs in the assembly line, and number of models.
Research Objectives
The current research targets to achieve the following objectives:
10. Help vehicle manufacturers in Egypt optimize their production costs and faces the forces of the current highly competitive marketplace
11. Arrange sophisticated workforce schedules that can help workers to deal with flexible work schedules with the least amount of upper management directions regarding workload distribution
12. Help managers prepare different versions of assignment schedules with different production cycles (work shifts for workers) to facilitate worker shift from one workstation to another
13. For every workstation, with a number of jobs, the research helps find an optimal permutation of the manpower requirements of all jobs that minimize the makespan of every production cycle and hence, reduce the overall time needed for the mixed-car model assembly line in a given vehicle plant in Egypt
14. Achieve higher profitability both on the short-term and long-term by reducing costs of tardiness and fulfill demand for different product models simultaneously
15. Enhance company competitive advantage by supporting on-time delivery of demanded products
16. Help vehicle manufacturers to dimension and schedule workforce resources on mass production for both robotic and manual assembly process
17. Find the optimal assignment of workers to sequence of jobs to find the best schedule of their movement along different workstations, by applying linear programming minimization function
18. Find the best possible line parameters such as launch interval, sequence of work-pieces, station length in order to minimize utility time cost in the assembly plant
Research Importance
The current research is important for the automotive industry sector because of the following points:
7. Mass production in manufacturing sector requires repetitive production work patterns which implies the necessity of arranging for flexible workers schedules
8. In automotive industry sector, there is a need for an integrated scheduling model that is capable of fulfilling customers needs and achieve cost minimization for the company at the same time
9. Cost minimization is dependent on different factors, and time minimization is one of the critical factors that enhances cost minimization and raises profitability
10. Based on previous study, it appears that using linear programming for time management is a successful tool
11. Workforce scheduling in assembling plants is a key factor to the success of the business as it is reflected on the company capacity to produce without tardiness and hence, achieve on-time delivery purpose
12. Company commitment to delivery schedules enhances its competitive advantage among other competitors in the market, which can be achieved through better workforce scheduling and time minimization
Research Hypotheses
The research attempts to prove the validity of the following hypotheses:
First Hypothesis:
”The number of working shifts allowed in production schedule increases the possibility to minimize the overall makespan in mixed-car model assembly line of automotive industry plants in Egypt”.
The first hypothesis validity testing will based on the final results regarding constraint number (1) and how far it affects time minimization.
Second Hypothesis:
”The number of available workstations for assembly increases the possibility to minimize the overall makespan in mixed-car model assembly line of automotive industry plants in Egypt”.
The second hypothesis validity testing will based on the final results regarding constraint number (1) and how far it affects time minimization.
Third Hypothesis:
”The number of available suppliers increases the possibility to minimize the overall makespan in mixed-car model assembly line of automotive industry plants in Egypt”.
The third hypothesis validity testing will based on the final results regarding constraint number (2) and how far it affects time minimization.
Fourth Hypothesis:
”The number of workers hired in general increases the possibility to minimize the overall makespan in mixed-car model assembly line of automotive industry plants in Egypt”.
The fourth hypothesis validity testing will based on the final results regarding constraints number (3,4,5) and how far they affect time minimization.
Fifth Hypothesis:
”The number of workers hired in every workstation increases the possibility to minimize the overall makespan in mixed-car model assembly line of automotive industry plants in Egypt”.
The fifth hypothesis validity testing will based on the final results regarding constraints number (3,4,5) and how far they affect time minimization.
Sixth Hypothesis:
”The lack of proper distribution of workers on different workstations decreases the possibility to minimize the overall makespan in mixed-car model assembly line of automotive industry plants in Egypt and increases idle time”.
The sixth hypothesis validity testing will be based on the final results regarding constraints number (3,4,5) and how far they affect time minimization.
Seventh Hypothesis:
”The demand level for different models increases the possibility to minimize the overall makespan in mixed-car model assembly line of automotive industry plants in Egypt”.
The seventh hypothesis validity testing will based on the final results regarding constraints number (6,7,8), and how far they affect time minimization.
Research Methodology
Research Tools
The current study makes use of the following tools:
3. Theoretical study: which is drawn from previous studies and the understanding of the nature and environment of the automotive industry sector in Egypt.
4. Linear Programming: the study makes use of linear programming in order to create a linear relationship between different variables, as illustrated in research problem, in order to create a minimization objective function of the overall time needed for assembly in automotive industry plants
Research Sample
The research sample includes Egyptian companies registered in the automotive industry sectors.
The following are a sample of these companies:
3) Prima Assembly Plant
4) Hyundai Motor Company
Data Sources
The study will depend on two types of data:
Primary Data:
Collected using both:
3) In-depth interviews with production managers in the companies enlisted in the research sample
4) Close observation (both images and text) of the production process in the companies enlisted in the research sample
Secondary Data:
Collected using different kind of available secondary resources in the libraries and on the Web, such as:
Books
Reports (academic and commercial from the manufacturing sector entities)
Previous studies
Journal articles
Authenticated Online Content
Thesis Outline:
In chapter 1 we have introduced the problem in an informal setting and provide some examples that will motivate the remaining chapters. We have offered a few examples to illustrate the difficulties involved in making scheduling decisions in car assembling plants and how certain approaches taken by some manufacturing firms can be vastly improved. A brief literature review was provide in the last section.
In chapter 2 we formally introduce the problem we wish to consider in a theoretical form.
In chapter 3 we look at the case in which the mathematical model is created. In different sections the mathematical model is built in three phases and solved in phase III using GAMS Ide software and linear programming equations.
In chapter 4. Results are analyzed for every equation of the model.
Theoretical Part:
Assembly Organization: In defining the organization of the assembly system, the assembly object is the most important criterion; it is necessary, hence, to differentiate between objects with fixed location and objects with variable location.
Objects with fixed location are assembled without transport to another station; its organization is divided into site assembly work, and moving assembly work.
On the other hand, objects with variable location are organized based on workshop assembly and line assembly work.
Assembly Line Balancing: Assembly line balancing starts with product assembly analysis and the identification of elementary tasks required to complete assembly and the time needed to complete each. Assembly line balancing is a procedure, in which tasks are to be distributed evenly to each assembly station in the line which can evenly dedicate the same amount of work to each workstation.
Manpower Planning: The manpower requirement for production can be estimated by considering local legal constraints for working times, which is depending on agreements with local units, as well as statistical records on manpower present on the job. The input data includes:
Working days available for each worker
Duration of daily working shifts
Shift organization and rotation
Sick leave, accidents and incidents of permission for individual leave
Car Sequencing Problem: Production scheduling problems can be expressed as a collection of linear constraints with one basic objective function. This allows handling such industrial problems to be treated as linear optimization problems in which we have a group of variables that can take real values. There are several measures to be used with models to assess the performance and cost effectiveness such as assembly throughput, assembly capacity, assembly lead-time, in-process inventory, availability, flexibility, quality and cost per assembly. In different assembly systems, assembly line balancing starts with product assembly analysis and the identification of demand tasks. In this regard, makespan analysis technique may depend on stop-watch study analysis, time studying with modern technologies, the analysis with the predetermined standard times and the instantaneous observation analysis.
Mathematical Model:
The model application will go through three phases:
Phase One: The mathematical model is modeled using Mixed Integer Programming to maximize the number of workers needed to assemble the three models of cars, and runs are performed on the software application “GAMS Ide”. This summarize phase one of the model.
Phase Two: second phase objective is to focus on workstations number needed to assemble the three models.
Phase Three: third phase objective is to minimize time needed per model; which is the objective of the thesis, and runs are performed on the software application “GAMS Ide”. This summarize phase three of the model.
Sensitivity Analysis: After model application, a sensitivity analysis is required to ensure its validity and if it can works in case parameters are changed by +/- 10% (increase/decrease). By reviewing the sensitivity analysis iterations, we find out that iteration #1 is the only optimal solution, and iterations # 2, 6 and 8 provide non-feasible solutions, while all other iterations give the same results as initial solution.
Results Analysis:
The results of the proposed mathematical model presented in details in chapter three revealed the validity of the hypotheses four and five specifically, while all other factors remain the same. This means that the higher the number of workers hired the better the chance will be to minimize total assembly time, which is reflected in the first phase of the model. Note that testing will handle the vector versions of constraints and objective functions.