Synthesis-Algo: An Inclusive Timetable Generating Algorithm

Timetabling manually is a complex activity that is time-consuming and stressful because a high number of high and soft constraints need to be satisfied; there is therefore the need to automate the timetabling process. Timetabling automating involves combinatorial optimization, time-based planning, realizing highly constrained problems and addressing multi-dimensional complexities. Researchers have addressed the timetabling problem using approaches within the domain of Local Search Procedures, Population Based Algorithms, Artificial Immune Algorithms, Hyper-heuristics, Operations Research, Graph Based Sequential Techniques, Decomposition/Clustering Techniques and Artificial Intelligence. However, these approaches and technique have their setbacks that affect deriving the optimum solution within the shortest possible time. Some techniques are mainly concerned with one problem instance or class and are generally not capable of dealing with other problem domains [59]. Any parameter or constraint adjustment in a new problem domain results in implementation difficulties that is as complex as designing a new model using a new technique. This paper describes a universal algorithm called Synthesis-Algo for solving complex and highly constrained timetabling problems by hybridizing concepts from evolutionary algorithms and tabu searching technique. We programmed Synthesis-Algo using Matlab and simulations were performed using simulated and real data. Experimental findings show that Synthesis-Algo produced better timetabling results.