2000 words for the proposal (very fine if less - I am expecting no more than one or two pages)

explain the context in which the problem occurs, the data available and, mostly, what is the set of decisions to be made

Introduction

Background

Current challenges and limitations

Statement of the problem

Data

Problem Statement

aim to optimize

Proposed OR Approach

according to Decisions to be Made

Expected Benefits

Reduced cost etc.

Conclusion

Recap of the problem and solution

Potential for further optimization, improvement of the approach

Recommendation

Implementation plan for the proposed solution

Monitoring and evaluation strategy to measure the impact of the optimization efforts

Production scheduling:

https://github.com/jmyrberg/production-scheduling

MAST90014 Group Project

Melbourne Tour Day Plan Proposal

Group 2

Huixin ChenZhaonan GaoZixuan LinJinxian Lyu, Jingchen Shi, Zixuan Xiao

Introduction

Melbourne is well known for its multi-dimensional culture, stunning architecture and diverse cuisine, with an increasing number of visitors seeking unforgettable experiences in the city. However, tourists often struggle to plan an efficient and enjoyable day due to the wide variety of services offered by Melbourne but there is a lack of well-curated comprehensive visiting resources. Nowadays, travel agencies may face the challenge to provide tourists a travel plan fully experiencing the city's rich culture and attractions in a limited time. In order to address the current lack of efficient, enjoyable and flexible travel plans in Melbourne, and also in order to satisfy visitors' experience, this proposal aims to optimize Melbourne tour day plan.

Problem Statement

The general goal of this proposal is to optimize the Melbourne tour day plan that maximizes visitor satisfaction by leveraging data analytics and optimization techniques. By analyzing the available datasets, we aim to create a tailored day plan that maximizes tourist satisfaction and enjoyment. Specifically, the time constraints, budget limitations, and route concerns will be concerned. These itineraries must not only cover popular points of interest but also include dining options that offer exceptional culinary experiences, as well as some entertainment places.

Data Collection

Data collection mainly includes ratings, favorability, user feedback, etc. obtained from some popular travel apps such as Tourist Guide,Melbourne Guide, and Google Maps, etc.

The available datasets includes:

Melbourne_POIs_Final.csv: This dataset contains information about the names of attractions, their ratings, longitude, latitude, the type of attraction, ticket prices, and opening hours.

melbourne_restaurants.csv: This dataset contains names, longitude, latitude,rating,food type,price level of the restaurants.

parks-and-sports-facilities.csv: This dataset contains information about longitude, latitude, the type of facility, ticket prices, and the name of the facility.

entertainment-and-theatre.csv: This dataset contains information about longitude, latitude, the type of the entertainment, and the name of the facility.

clubs.csv: This dataset gives the club’s name, longitude, latitude as well as their official website.

hotel_poi.csv: This contains the names, ratings, type, Address, longitude and latitude of the hotel.

Decision to be made

Our research focuses on utilizing mixed-integer programming techniques to address the one-day tour guide optimization problem for visitors in Melbourne. Specifically, a detailed schedule will be established, including each location’s opening hours, ticket prices and estimated visiting duration. At the same time, the most popular, highest-rated attractions and restaurants that match tourists' interests will be identified. Also, determining the best order of visits to attractions, restaurants, parks, entertainment venues and so on is expected to be achieved. These locations will be filtered by their price level and proximity to attractions.

Conclusion

In conclusion, this proposal aims to enhance the tourist experience in Melbourne through a well-thought-out, optimized tour day plan. Using data analytics, we plan to create personalized itineraries that align with visitors' interests and constraints, covering the city's key attractions, dining, and entertainment options. This approach ensures a balanced and enjoyable experience, efficiently navigating through Melbourne's highlights based on ratings, location, and visitor preferences. Ultimately, this strategy seeks to make visits more memorable, support local businesses, and boost the tourism sector, making Melbourne an even more attractive destination for tourists.

Goal: Maximize the sum of ratings of every tourist for both attractions and restaurants.

Data:

set of hotel

set of attractions

set of restaurants

a_ij: binary variable equal to 1 if we decide to go to attraction i on day j (attractions)

r_ij: binary variable equal to 1 if we decide to go to restaurant i on day j (restaurant)

h_i: binary variable equal to 1 if we go to hotel i

t :

Constraints:

Arriving time of restaurants for lunch before 12:00 pm and dinner before 18:00 pm, respectively.

Cannot visit the same type of attraction more than twice.

Cost less than a specific number.

Attractions have their own opening hours; visitors must arrive after the opening hour and leave before the closing hour.

Selection of transportation options (e.g., taxi with an initial fee, number of students).

Tour Start and End: Each day's tour starts and ends at the hotel, ensuring all daily tours are loops that begin and conclude at the same point.

Walking- if the distance between 2 POIs is less than 400m

hours,days

Data:1.5

Latex :4

Slides/Presentation:1