Performance improvement strategies on Cuckoo Search algorithms for solving the university course timetabling problem

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

“…Scheduling or timetabling is an important problem and active area of research with applications in many fields [ 19 ], e.g., transportation [ 20 ], sport [ 21 ], healthcare (e.g., nurse rostering [ 22 ], surgery [ 23 ]), industrial production [ 8 ], employee [ 24 ], educational institutes (e.g., secondary school [ 25 ], university [ 18 ]). The university course timetabling problem (UCTP) is a classic and famous optimisation problem in the field of computer science and management science [ 26 ].…”

“…The characteristics of all hard constraints ( H ) considered in this work can be explained as follows [ 18 ]: ( H 1 ) all given courses must be scheduled, in which all lectures or laboratories for each course must be assigned to different periods; ( H 2 ) lecturers and students can only attend one lecture or event at a time; ( H 3 ) each timeslot for the given classrooms cannot assign a lecture or event more than one; ( H 4 ) all courses required by lecturers and students must not be scheduled in their unavailable periods; ( H 5 ) each course must be scheduled in the specific classrooms according to its given requirements (e.g., types of classroom, room facilities, and building location); and ( H 6 ) all events or lectures for a course required consecutive timeslots must be satisfied.…”

“…H 4 – H 6 are particular constraints for each lecturer, curriculum, and courses found in most of the universities in Thailand. In addition, Soft Constraints ( S ) can be described as follows [ 18 ]: ( S 1 ) all courses should be scheduled in the proper types of the classroom to keep away from unnecessary renting or operating costs (currency unit per period); ( S 2 ) each course taught by the given lecturer(s) should be assigned according to their preferred working days and periods for saving the hiring or employing costs (currency unit per period); and ( S 3 ) each classroom should be used in consecutive periods of a day for reducing the number of times to prepare or clear that classroom after usability (per time).…”

“…After the movement process, a new solution or timetable x i t +1 may be either feasible or infeasible timetable. In the case of an infeasible solution, the repair process was adopted from previous research [ 18 ] to rectify all infeasible timetables to obtain feasible timetables. The main rectifying steps can be described as follows: (i) find the encoded events having some violations of hard constraints ( H 1 – H 6 ); (ii) for each violated event, seek the possible idle or empty timeslots without H 1 – H 6 violations (called the feasible timeslots) for that violated event before swapping randomly; (iii) if the feasible timeslots from the previous step cannot found, search the possible non-empty timeslots (events) without any violation of H 1 – H 6 for that violated event and vice versa before random swapping; and (iv) repeat the steps (ii) and (iii) until all violated events in a candidate solution are rectified.…”

“…Many types of Local Search (LS) strategies, e.g., Neighbourhood Search [ 14 ], Hill Climbing [ 15 ], Exchange Operator (EO) [ 2 , 16 ], Local Beam Search [ 17 ], Insertion Operator (IO) [ 2 ], have been found to hybrid with PSO for dealing with the CTP. Besides avoiding traps in a local optimum solution, hybrid approaches can increase the opportunity to discover the global best solution quickly [ 18 ]. According to a literature survey above, only one article has considered more than one type of LS strategies (IO and EO) embedded in MCPSO for solving the CTP [ 2 ].…”