System, method and product for rostering using dynamic gene modelling based upon genetic algorithms

In a dynamic gene modeling rostering computing system based upon genetic algorithms, a method for processing user input information to derive a roster, said method comprising the steps of:

• constructing an initial shift list matrix, wherein the shift list matrix has m rows and n columns, wherein m and n are integral numbers, and wherein when there are a plurality of staff and a plurality of shifts during a rostering period, m represents the plurality of staff, n represents the plurality of shifts;
• constructing an initial generation of chromosomes by dynamic gene modeling, wherein the length of each chromosome is automatically calculated based on total number of staff, shift lists assignment and fixed schedule information, and each of the shift lists is associated with a dynamic adjusting factors, and
• performing genetic algorithm evolutions on the initial generation of chromosomes based on the dynamic adjusting factors and then manipulating of said initial shift list matrix-according to the evolved dynamic adjusting factors to firstly derive a roster and then further fine-tune said roster to provide an optimal roster;
• wherein the optimal rosier is achieved when the total number of available staff and work shifts are evenly distributed daily within the roster period at every organizational hierarchical level;
• whereby the optimal roster is output to a user.

A product comprising computer storage medium having a genetic algorithm-based dynamic gene modeling computer program recorded thereon for processing user input information to derive a roster, wherein said genetic algorithm-based dynamic gene modeling computer program comprises:

• a workload and shift setting optimizer configured to construct an initial shift list matrix, wherein the shift list matrix has m rows and n columns, wherein m and n are integral numbers, and wherein when there are a plurality of staff and a plurality of shifts during a rostering period, m represents the plurality of staff, n represents the plurality of shifts;
• and
• a roster engine configured to
• construct an initial generation of chromosomes by dynamic gene modeling, wherein the length of each chromosome is automatically calculated based on total number of staff, shift lists assignment and fixed schedule information, and each of the shift lists is associated with a dynamic adjusting factors, and
• perform genetic algorithm evolutions on the initial generation of chromosomes based on the dynamic adjusting factors and then to manipulate said initial shift list matrix according to the evolved dynamic adjusting factors to firstly derive a roster and then further fine-tune the roster to provide an optimal roster;
• wherein the optimal roster is achieved when the total number of available staff and work shifts are evenly distributed daily within the roster period at every organizational hierarchical level:
• whereby the optimal roster is output to a user.