This document summarizes an optimization model developed for the proposed assembly line for Kiira EV electric vehicles in Uganda. It describes the background and objectives of developing an optimized assembly line layout. The methodology included developing a detailed component dictionary, preliminary task sequencing, and using simulation and mathematical modeling to develop an optimal layout. The results of the simulation showed a throughput of 5 cars per 9-hour shift was possible with a 30-minute cycle time. The mathematical model can help determine the optimal number of stations and cycle times required. Recommendations include considering automation and using the model to inform expansion decisions.

1. OPTIMIZATION MODEL OF THE PROPOSED KIIRA
EV ASSEMBLY LINE
Main supervisor
Dr. Bernard Kariko Buhwezi
Co- supervisor
Dr. J.K Byaruhanga
By
Ronald Kayiwa

2. Presentation Contents
 Background
 Problem statement
 Objectives
 Conceptual framework
 Methodology
 Design
 Optimization
 Mathematical model
 Results
 Recommendations

3. From the experience fetched from the construction of the prototype of the KIIRA EV, tasks had
been identified that were executed in the process.
From the experience fetched from the construction of the prototype of the KIIRA EV, tasks had
been identified that were executed in the process.
Initially architectural impressions had been developed for the establishment of the assembly
line. This has to be incorporated with well engineering designs to develop an optimally balanced
line.
Initially architectural impressions had been developed for the establishment of the assembly
line. This has to be incorporated with well engineering designs to develop an optimally balanced
line.
Facility planning is concerned with the design, layout, and accommodation of people,
machines and activities of a system or enterprise within a physical spatial environment.
Facility planning is concerned with the design, layout, and accommodation of people,
machines and activities of a system or enterprise within a physical spatial environment.
Background

4. Problem statement
After the design and construction of the first electric car in Uganda; the KIIRA EV, there has
been a concern on how this vision is to be sustained to a commercialization level.
After the design and construction of the first electric car in Uganda; the KIIRA EV, there has
been a concern on how this vision is to be sustained to a commercialization level.
This calls for establishment of an assembly line that can serve to satisfy the demands of the
proposed startup production level of the car.
This calls for establishment of an assembly line that can serve to satisfy the demands of the
proposed startup production level of the car.
There is need therefore to undertake Heuristic studies and benchmarks to design an optimized
assembly line while not compromising the stakeholders’ requirements.
There is need therefore to undertake Heuristic studies and benchmarks to design an optimized
assembly line while not compromising the stakeholders’ requirements.
What
next ?
???
What
next ?
???

5. MINIMUM
WASTES
MINIMUM
IDLE TIMES
MINIMUM
COSTS
BALANCED
OPERATOR
WORK LOAD
OPTIMALLY
BALANCED
LINE
CRTT
REQUIREMENT
S
INITIAL
ARCHITECTURAL
DESIGNS
SIMULATIONMODELING
RELATED
LITERATURE
BEST
BENCHMARK
PRACTICES
STATION CYCLE
TIMES
WORKER
UTILIZATION
LOGISTICS
(PARTS) FLOW
TOTAL TAKT
TIME
SPACE
UTILIZATION
CONCEPTUAL FRAMEWORK

6. Objectives
 General objective
To develop an optimally balanced KIIRA EV assembly line.
 Specific objectives
 To develop a detailed component dictionary for the KIIRA EV.
 To develop the plant layout and evaluation for optimality.
 To develop mathematical and graphical models of the optimized
assembly line.

7. Methodology
Specific objective How Work package Deliverable
To develop a detailed component
dictionary for the car.
• Use of the CAD
assembly model of the
production concept of
the KIIRA EV
• Use a CKD Strategy to
break down all the car
components to the last
detail
WP 1.1 Developing a component list Component list
WP1.2 Identifying part numbers per
component.
Component specification sheet
WP1.3 Modularizing components Modules document
To develop a plant layout and
evaluation for optimality
.
• Best practices
benchmarking
• Heuristic sequencing
techniques mainly
Kilbridge and Wester's
Method (KWM)
• Production
Engineering principal
text books
WP2.1 Identifying tasks required to
integrate each component
Work break down structure
WP2.2 Sequencing tasks based on real
life studies
Task schedule
WP2.3 Allocating task times Station cycle time table
WP2.4 Develop manning levels per
station
Worker allocation document
WP2.5Laying all parameters in the
simulation environment and running the
simulation
Preliminary performance results.

8. Specific objective How Work package Deliverable
To develop mathematical and
graphical models of the optimized
assembly line
•Operations Research
tools/techniques shall be
used to formulate the
model and finding the
most optimal solution
•In case the model is
complex the MATLAB
software package shall be
employed in solution
finding.
•Graphical models shall be
generated using
TechnomatixR
plant
simulation package
WP3.1 Setting the core objectives
based on the performance results
Model objectives
WP3.2 Setting constraints and
solving the model
Optimal solution
WP3.3 Bottleneck identification Final line design specifications and
Recommendations
WP3.4 Simulating the models with
Technomatix
Video motion of he assembly line
model.

9. DESIGN
 Initial state analysis
The actual production rate, pieces per hour Rp = ; where Da = annual
demand for the cars; pieces per year (Groover)
Sw = number of shifts per week and Hsh = hours per shift
N = number of weeks per year
Rp = = 0.53………………..
Hence per hour …0.53 of the car should be assembled to satisfy the above
demand
 Preliminary task precedence
 Number of work stations
 Production rate

10. Design cont’d
Simulation1........
Parameters
Test cycle time of 30 minutes .//
 Continuity of a 14.5 hour shift
The availability per station was set at 80% as per
plant Sim for manual stations.
Set-up
Results
Parts dictionary

11. RESULTS

12. OPTIMIZATION
 Modularization
 CKD strategy
 parameters
 Simulation 2
Cycle time = 30 minutes
Run = single shift
Station separation = 8 m
The FIFO (First In First Out) is used in all stations, and each station
has the same number of operating workers.
All sub assembly stations are no shortage of material.
 Results Set-up
A throughput of 5 cars was possible at 9 hours
at a generic cycle time of 30 minutes

13. Mathematical model
 Brief:
This model was developed to help assign optimally jobs to stations while
ensuring that the cycle time is not exceeded.
It outputs all feasible combinations of cycle time and the number of
stations. In the second algorithm all the feasible combinations are
checked and the most optimal is chosen. This is based on the efficiency
of the combination .
It also incorporates the required output and the time frame of
production which can be based on which ever time units are chosen.
Basing on this model several results were generated from various
tasks of already standardized lines and it was proved to be
efficient.

14. Mathematical model (cont’d…....
Parameters
Number of jobs: n
Index of the job: i, i=1...n
Processing time of job i: pi
Unknown
Number of stations: k, with index of the station j
Processing time of station j: sj
Cycle time: C

15. MATLAB
CODE.

16. CONCLUSIONS
 A fully detailed component dictionary was developed from which
modules (kits) were extracted. This will be key in making informed
supplier decisions and also
A layout of the assembly line was developed and basing on a cycle
time of 30 minutes, a through put of 5 cars in a 9 hr shift is possible.
 A mathematical model which can handle task time categorizations
into the possible number of stations and viable cycle times was
developed can be vital at implementation stage and in cases of
expansions in future

17. Recommendations
 The design of this assembly line is based on the assumption that all
the processes are manually executed. However in implementing this line
there could be sophisticated processes that require automated systems.
This can be handled by utilizing the model developed to ascertain which
parameters need to be adjusted.
 In case of an expansion in future technical decisions can be informed
by the mathematical model so that optimality is not lost.