Large-scale timetabling problems with adaptive tabu search

Abstract Timetabling problems are specific types of scheduling that deal with assigning certain events to the timeslots. This is subject hard constraints should be achieved get a feasible timetable and soft must meet as many possible during forming schedule. paper introduces an adaptive tabu search. Eleven benchmark datasets year 2002 applied show effectiveness introduced algorithm. These consist 5-small, 5-medium, 1-large dataset. As compared other methods from previous works, proposed algorithm produces excellent timetables, in comparison algorithms well current results, mathematical results showed high suggested It has minor deficit on medium or small problem Tabu, search relies list penalty cost when change checked; if it still unchanged for period iterations (1,000 iterations), reduces automatically by (−2); furthermore, remains constant.