ICAR Research Data Repository for Knowledge Management

Mandarine Academy Professional Timetabling (MAPT) is a real-world dataset suggested to solve Professional Timetabling Problems (PTPs). A rather under-exploited category of the overall Timetabling Problems. However, we believe it can still be applied to traditional problems (education, health, etc.) as a helpful benchmark dataset to assist researchers in comparing different methods. Compared to conventional educational datasets such as (ITC2007), MAPT proposes richer features inspired by real-world data to provide insight into corporate training logistics and timetabling complexities.

1. Input files that are used as testing the approach. Having 3 other groups (Small, Medium, and Large) each in 20 different instances, totaling 60 test sets, to simulate different real-world scenarios.
   
   


2. Second records are training files that include the information of each entity involved in the scheduling process. Such as training, teachers, rooms, locations, etc. Note that both records have no redundant values.

We provide test files used in our experiments. To simulate different real-world scenarios, we created varying complexities (number of events and available time window).

• Folder mapt_sm: The full MAPT (Small) test instances (20).


• Folder mapt_md: The full MAPT (Medium) test instances (20).


• Folder mapt_md_2: The full MAPT (Medium) with limited time window test instances (20).


• Folder mapt_lg: The full MAPT (Large) test instances (20).


• Folder mapt_lg_2: The full MAPT (large) with limited time window test instances (20).

These files give information about each entity found in the planning process. They are crucial to validate solutions with defined constraints.

• trainings.csv: This file has essential information about all training used in the planning process (ID, Type, Duration, etc.).


• training_skills.csv: This file provides a list of required skills for each training.


• training_rooms.csv: Associated rooms to training.


• training_devices.csv: Associated devices to training.


• training_animators.csv: Associated teachers to training.


• training_skilled_animators.csv: Associated skilled teachers to training


• sequences.csv: This file has essential information about all sequences used in the planning process (ID, Name, Duration, etc.).


• sequence_rooms.csv: Associated rooms to sequences.


• sequence_devices.csv: Associated devices to sequences.


• sequence_animators.csv: Associated teachers to sequences.


• sequence _skills.csv: This file provides a list of required skills for each sequence.


• resource_rooms.csv: This file has essential information about all rooms (ID, capacity, location, etc.).


• resource_devices.csv: This file has essential information about all equipment (ID, type, etc.).


• resource_animator.csv: This file has essential information about all teachers (ID, type, etc.).


• animator_skills.csv: This file provides a list of skills associated with each teacher.


• rooms_unavailability.csv = This file provides reservation information (dates, times, location, etc.) made for each room. 


• device_unavailability.csv = This file provides reservation information (dates, times, location, etc.) made for each piece of equipment. 


• animator_unavailability.csv: This file provides reservation information (dates, times, location, etc.) made for each teacher. 


• localization.csv: these files have information (id, city, country, etc.) about where events can take place.

Initial solutions were made available using our proposed constructive heuristic found in our work. These can be used directly as input to benchmark approaches.

• Folder mapt_sm: Initial solutions using the first input file sm_0.csv.


• Folder mapt_md: Initial solutions using the first input file md_0.csv.


• Folder mapt_md_2: Initial solutions using the first input file md_2_0.csv.


• Folder mapt_lg: Initial solutions using the first input file bg_0.csv.


• Folder mapt_lg_2: Initial solutions using the first input file bg_2_0.csv.

We provide final objectives and directions. We included NSGAII and NSGAIII results using only 3 Objectives.

• Folder mapt_sm: Final objectives values using the first input file sm_0.csv.


• Folder mapt_md: Final objectives values using the first input file md_0.csv.


• Folder mapt_lg: Final objectives values using the first input file bg_0.csv.