Essay Example on Timetabling is a way of assigning Events

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Background of the study Timetabling is a way of assigning events into a limited number of time periods in such a way as to satisfy as many desirable objectives Burke Petrovic 2002 Various fields require scheduling and hence timetabling is applied These fields include job shop scheduling airline crew scheduling sports or tournament timetabling transportation timetabling and education timetabling University course timetabling is one of the subclasses of education timetabling and an NP complete problem Babaei Karimpour Hadidi 2015 Due to the variations in policies constraints hard and soft and objectives across the different institutions Gonsalves Oishi 2015 Jula Naseri 2011 it is very difficult to find exact or a perfect solution using a conventional optimization techniques Kanoh Chen 2013 The timetabling problem has been formulated in many and very different ways and has been addressed using several analytical or heuristic approaches Babaei et al 2015 Pillay 2014 Rossi Doria et al 2002 Numerous methods and approaches are proposed to solve the educational timetabling problem by using operational research based techniques Bakir Aksop 2008 Dandashi Al Mouhamed 2010 Razak Ibrahim Hussin 2010 meta heuristic approaches Alves Oliveira Neto 2015 Bellio Ceschia Di Gaspero Schaerf Urli 2016 Fonseca Santos 2014 Sani Yabo 2016 Yigit 2007 multi criteria and multi objective approaches Shahnazari Shahrezaei 2013 Smutnicki Pempera Rudy Zelazny 2015 intelligent novel approaches Alzaqebah Abdullah 2015 Jain Chawla Soares 2014 Oner Ozcan Dengi 2011 Shahnazari Shahrezaei 2013 Tarawneh Ayob Ahmad 2013 and distributed multi agent systems approach Feizi Derakhshi Babaei 2012 Lewis Paechter Rossi Doria 2007 



These methods are capable of generating feasible solutions with certain characteristics and tradeoffs Teoh Wibowo Mohd Ngadiman 2015 Literature revealed that the hybrid of meta heuristics methods are given more interest by the current researches Feizi Derakhshi Babaei 2012 Meta heuristics methods were used and its attention have grown over the years due to its capability in addressing various real world problems Alzaqebah Abdullah 2015 Deng Lin 2011 Fong Asmuni McCollum McMullan Omatu 2014 Oner et al 2011 Smutnicki et al 2015 Tarawneh et al 2013 Tein Ramli 2010 Yang Jat 2011 Different metaheuristic approach such as evolutionary algorithms were also developed aiming at reducing the computational time required to solve these problems without considering much of the real world constraints It was also reported that evolutionary genetic algorithms had proven success in solving many timetabling problems Additionally with regards to search space exploration it was observed that population based meta heuristic approaches appear to perform better compared to single solution approaches Teoh et al 2015 Moreover a population based genetic algorithms have been proven successful in solving many timetabling problems Abdelhalim El Khayat 2016 Introduced by Holland 1992 GA is a general purpose search algorithms that use principles inspired by natural genetics to evolve solutions to problems GAs are known for their robustness in solving complex combinatorial problems Sani Yabo 2016 Genetic algorithms have the advantage of being adaptive search algorithms and characterized by their flexibility and ability to search for complex large spaces Juang Lin Kao 2007 However genetic algorithms takes large computational time to produce an optimal or near optimal solution Juang et al 2007 Pillay 2014 This is due to stochastic nature of its operators that can lead to offspring not meeting the requirements of the problem being produced Mirhassani Habibi 2013 Pillay 2014 Teoh et al 2015 To effectively exploit the strength of this algorithm a hybridization techniques were employed to enhance the primitive decision making skills of the genetic algorithm operators Feizi Derakhshi Babaei 2012 Teoh et al 2015 



At present some universities are still constructing timetables by hand Soria Alcaraz Özcan Swan Kendall 2016 with the aid of simple office applications such as word processor and spreadsheets Every educational institution has a lot of things to consider when generating schedules and with the difficulty of generating timetables it is proper to generate these timetables from the methods or solutions obtained from experts automatically However it is difficult to implement the same method to a problem in different types different characteristics and different constraints or limitations In Kaewchanid Wangmaeteekul 2016 it was argued that some of these recent methods and implementations were focusing on evaluating its performance on a particular data sets and have ignored many real world constraints Thus these recent solutions may not applicable to all educational institutions Normally the goal of the algorithm is to create a feasible timetable that satisfies all hard constraints and as much as possible to minimize soft constraints penalties Consequently the efficient timetable is the solution having the minimal cost Though complications may still arise if a large part of the total penalty is assigned only to a small number of stakeholders such as faculty and student sections that receive poor timetable compared to others Mühlenthaler Wanka 2016

Another example is when a faculty receive four 4 classes to teach with 4 preparations compared to a faculty having the same numbers of classes but with only two 2 preparations Thus a timetable may be unfair due to an unequal distribution of penalty workloads or utilization In this work we address the university course timetabling problem that is suitable for Philippine higher educational institutions based on hybrid genetic algorithm with a guided search technique In the proposed method the least selected and unused resources are keep tracked and stored in a data structures As a guided or directed strategy these data structures are then is use to improve the previously generated individuals by the GA operators Further a guided repair strategies will be employed using the data structures on invalid genes in every infeasible individual Thus these propose methods will give higher chance of creating feasible solution in every generation from genetic operators and the probability of getting optimal or near optimal solution will also increase Lastly a unique penalty assignment is also proposed to achieve fair and balance timetable


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