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Bakers Fresh, a local bakery in Brisbane, prides itself on delivering freshly baked products to its loyal  customers. As the customer base grows, planning optimal delivery routes for its trucks has become

Written and Programming Assessment Due date: 11:55 pm AEST, Friday of Week 8 (03 May 2024)  

Weighting: 25%

Mode: Individual 

Length: Maximum 2,000 words (Assessment Report – Max. 1,500  

words, and Reflection – Max. 500 words) 

Full Mark: 100 

Objectives 

This assignment is designed to reinforce the knowledge and skills acquired in Week 5 to Week 7. It is an  individual assessment to be submitted in Week 8. The assessment task relates to Unit Learning Outcomes  1, 2 and 4, and must be done and submit individually.  

Problem Description 

Bakers Fresh, a local bakery in Brisbane, prides itself on delivering freshly baked products to its loyal  customers. As the customer base grows, planning optimal delivery routes for its trucks has become  increasingly challenging. Currently, the bakery relies on a manual process for assigning deliveries to its  drivers. This often leads to sub-optimal routes, resulting in longer delivery times that has negatively  impacted customer satisfaction. 

To address this problem, Bakers Fresh is hiring you to develop a more efficient method for planning the  delivery routes. As an example, figure 1 shows how delivery routes may be seen as paths in a graph. The  numbers on the edges denote the distances between pairs of locations.  

Figure 1. This figure is by an Unknown Author licensed under CC BY-SA-NC

  

Tasks 

Part 1: Modelling and Algorithm Design (25 marks) 

1. Problem Modelling (10 marks):  

o Explain how the delivery scenario can be modelled as a graph. Identify nodes and edges  and their meaning in the context of the problem. 

o Consider the choice of data structures for representing nodes and edges. Explain your  choice. If the choice of data structures is different for both uninformed and informed search  algorithms, explain the difference.  

2. Algorithm Design (15 marks):  

o Design an algorithm based on A* search to find the shortest delivery route. Include pseudo  code with comments explaining the major steps of the algorithm. 

o Briefly discuss the heuristic function that you are using in the A* search. 

o Choose an uninformed search algorithm you have come across as the candidate to compare with the A* search. Explain this uninformed search algorithm. 

Part 2: Implementation (40 marks) 

1. Python Implementation (30 marks):  

o Implement the A* search algorithm in Python using your chosen data structures.

o Implement the uninformed search algorithm in Python for comparison. 

o Ensure your code is well-documented and includes comments explaining your logic. 

2. Test Data Design (10 marks):  

o Design the test data, including at least 20 delivery locations (discounting the location of  Bakers Fresh), for the comparison.  

o Include the test data in your Python code.  

Part 3: Testing and Analysis (20 marks) 

1. Testing (10 marks):  

o Test your implemented algorithms (A* and chosen uninformed) with the designed test data.

o Capture screenshots of the test output from your Python program for both algorithms. 

2. Analysis (10 marks):  

o Compare and analyse the performance of both the A* and the uninformed search algorithm  in terms of: 

Efficiency (number of nodes explored) 

Optimality (shortest route found) 

Other relevant performance metrics you can think of 

o Discuss which of A* search and the uninformed search performs better in the specific  problem of this assignment. Why? 

Part 4: Reflection (15 marks) 

1. Reflection (15 marks):  

o Summarise your learnings from this assignment. 

o Reflect on the strengths and limitations of the implemented algorithms. 

o Propose potential improvements on either or both algorithms.  

Submission 

Each student must upload these two files via the Assignment 2 submission link on the COIT20277 HT1, 

2  

2024 Moodle assessment block by the specified due date. Late submission will incur a penalty as per the  university’s Assessment Policy and Procedure. 

1. Submit a Jupyter notebook containing your Python code, test data, and analysis with screenshots. 2. Include a separate Word document containing your written report, covering problem modelling,  algorithm design, and reflection. 

Marking Rubric (maximum 100 marks) 

Part 1: Modelling and Algorithm Design (25 marks)  

Criteria 

Excellent  

(100%)

Good (70%) 

Satisfactory  

(40%)

Unsatisfactory  

(10%)

Problem  

Modelling (5  

marks)

Clear and  

accurate

Mostly clear 

Some clarity  

issues

Unclear or  

inaccurate

Data Structure  

Justification (5  

marks)

Well-explained  

and appropriate

Explained, but  

minor issues

Briefly  

explained

Not explained

A* Search  

Algorithm  

Design (5  

marks)

Clear, well 

commented, and  

correct

Mostly clear  

and correct

Some errors  

or missing  

information

Unclear or  

incorrect

Heuristic  

Function  

Explanation (5  

marks)

Clear  

explanation of  

purpose and  

choice

Basic  

understanding

Limited  

understanding

No explanation

Uninformed  

Search  

Algorithm  

Choice (5  

marks)

Justified and  

relevant to  

comparison

Mentioned but  

not justified

Not chosen or  

irrelevant

Not mentioned

 

Part 2: Implementation (40 marks) 

Criteria 

Excellent (100%) 

Good (70%) 

Satisfactory  

(40%)

Unsatisfactory  (10%)

A* Search  

Implementation (20  marks)

Correct, well 

documented, and  

efficient

Mostly correct and  documented

Some errors or  inefficiency

Incorrect or  

incomplete

Uninformed Search  Implementation (10  marks)

Correct and  

documented

Mostly correct 

Some errors 

Incorrect or  

incomplete

Test Data Design 

(10 marks)

Clear, well-structured,  and relevant

Mostly clear and  relevant

Missing clarity  or relevance

Unclear or  

irrelevant

 

Part 3: Testing and Analysis (20 marks)

Criteria 

Excellent (100%) 

Good (70%) 

Satisfactory  

(40%)

Unsatisfactory  (10%)

Testing Completion (10 marks)

Both A* and  

uninformed search  

algorithms are tested  with the designed test  data, and screenshots of 

Both algorithms  are tested, but  

only screenshots  for one algorithm  are provided, or 

Only one  

algorithm is  

tested, or the  

provided  

screenshots are 

No testing is  

conducted, or the  provided  

information is  

not related to 

 

3  


 

final output (e.g.,  

console output or  

visualizations) are  

captured for both  

algorithms.

the provided  

screenshots are  

incomplete.

irrelevant to the  task.

testing.

Analysis (10 marks) 

The analysis clearly  compares and contrasts the performance of A*  and the uninformed  search algorithm in  

terms of efficiency  

(number of nodes  

explored) and  

optimality (shortest  route found).

The analysis  

attempts to  

compare the  

performance of  

both algorithms,  but it might lack  detailed  

explanation or  

may contain minor  errors.

The analysis  

attempts to  

compare the  

performance,  but it lacks  

crucial aspects  like efficiency  or optimality  

comparison.

The analysis is  missing or  

irrelevant to  

comparing the  

algorithms or  

explaining A*’s  advantage.

 

Part 4: Reflection (15 marks) 

Criteria 

Excellent (100%) 

Good (70%) 

Satisfactory  

(40%)

Unsatisfactory  (10%)

Reflection 

The reflection paper  effectively summarizes  the student’s learnings  from the assignment.

The reflection  

paper covers the  key aspects  

mentioned above,  but it might lack  depth or detail in  some areas.

The reflection  paper attempts  to address the  required points  but might be  

incomplete or  lack clarity.

The reflection  

paper is missing  crucial aspects or  is not relevant to  the task.

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