A New Initialisation Method for Examination Timetabling Heuristics

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Timetabling problems are widespread, but are particularly prevalent in the educational domain. When sufficiently large, these are often only effectively tackled by timetabling meta-heuristics. The effectiveness of these in turn are often largely dependant on their initialisation protocols. There are a number of different initialisation approaches used in the literature for starting examination timetabling heuristics. We present a new iterative initialisation algorithm here --- which attempts to generate high-quality and legal solutions, to feed into a heuristic optimiser. The proposed approach is empirically verified on the ITC 2007 and Yeditepe benchmark sets. It is compared to popular initialisation approaches commonly employed in exam timetabling heuristics: the largest degree, largest weighted degree, largest enrollment, and saturation degree graph-colouring approaches, and random schedule allocation. The effectiveness of these approaches are also compared via incorporation in an exemplar evolutionary algorithm. The results show that the proposed method is capable of producing feasible solutions for all instances, with better quality and diversity compared to the alternative methods. It also leads to improved optimiser performance.Saudi Arabia Cultural Burea

Case Based Heuristic Selection for Timetabling Problems

This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstly, we model the problem and the problem solving situations along with specific heuristics for those problems. Secondly, we refine the case base and discard cases which prove to be non-useful in solving new problems. Experimental results are presented and analyzed. It is shown that case based reasoning can act effectively as an intelligent approach to learn which heuristics work well for particular timetabling situations. We conclude by outlining and discussing potential research issues in this critical area of knowledge discovery for different difficult timetabling problems

Structure based partial solution search for the examination timetabling problem.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The aim of this work is to present a new approach, namely, Structure Based Partial Solution Search (SBPSS) to solve the Examination Timetabling Problem. The success of the Developmental Approach in this problem domain suggested that the strategy of searching the spaces of partial timetables whilst constructing them is promising and worth pursuing. This work adopts a similar strategy. Multiple timetables are incrementally constructed at the same time. The quality of the partial timetables is improved upon by searching their partial solution spaces at every iteration during construction. Another key finding from the literature survey revealed that although timetables may exhibit the same behaviour in terms of their objective values, their structures or exam schedules may be different. The challenge with this finding is to decide on which regions to pursue because some regions may not be worth investigating due to the difficulty in searching them. These problematic areas may have solutions that are not amenable to change which makes it difficult to improve them. Another reason is that the neighbourhoods of solutions in these areas may be less connected than others which may restrict the ability of the search to move to a better solution in that neighbourhood. By moving to these problematic areas of the search space the search may stagnate and waste expensive computational resources. One way to overcome this challenge is to use both structure and behaviour in the search and not only behaviour alone to guide the search. A search that is guided by structure is able to find new regions by considering the structural components of the candidate solutions which indicate which part of the search space the same candidates occupy. Another benefit to making use of a structure-based search is that it has no objective value bias because it is not guided by only the objective value. This statement is consistent with the literature survey where it is suggested that in order to achieve good performance the search should not be guided by only the objective value. The proposed method has been tested on three popular benchmark sets for examination timetabling, namely, the Carter benchmark set; the benchmark set from the International Timetabling competition in 2007 and the Yeditepe benchmark set. The SBPSS found the best solutions for two of the Carter problem instances. The SBPSS found the best solutions for four of the competition problem instances. Lastly, the SBPSS improved on the best results for all the Yeditepe problem instances

Optimasi Penjadwalan Staf Rumah Sakit Dengan Menggunakan Algoritma Tabu Search Based Hyper-Heuristics (Studi Kasus: Rumah Sakit Ibu Dan Anak Kendangsari)

Penjadwalan staf rumah sakit atau dikenal sebagai staff healthcare rostering merupakan permasalahan kompleks yang harus dihadapi setiap rumah sakit. Rumah sakit harus mempertimbangkan banyak aspek seperti jumlah perawat, pembagian shift, cost, kesempatan libur atau cuti dan constraint yang lain. Karena banyak pertimbangan tersebut, penjadwalan secara manual akan menjadi sangat sulit dan tidak bisa memberikan solusi yang optimal. Maka perlu adanya suatu model matematis untuk memudahkan permasalahan penjadwalan dengan menemukan solusi yang paling optimal. Permasalahan tersebut lebih dikenal dengan istilah nurse rostering problem (NRP). Secara umum pemodelan nurse rostering atau heathcare staff rostering harus memperhatikan batasan-batasan objek sehinga dapat menghasilkan hasil yang optimal. Masalah lain dari penjadwalan staf merupakan masalah keadilan antar staf yang bertugas, bagaimana pembagian alokasi libur, waktu kerja, atau tempat tugas menjadi dimensi yang perlu dipertimbangkan. Banyak Penelitian menyebutkan bahwa indeks kepuasan suatu perawat atau staf dalam bekerja dipengaruhi oleh tingkat keadilan dalam pembagian jadwal. Untuk menyelesiakan permasalahan tersebut, pada penelitian ini akan dilakukan penjadwalan dengan menggunakan algoritma tabu search hyperheuristic. Algoritma Tabu Search hyper-heuristic akan digunakan memberikan solusi terhadap masukan permasalahan dengan cara menghasilkan heuristik baru dengan menggunakan heuristic yang sudah ada. Hasil optimasi penjadwalan dengan algoritma tabu search hyper heuristics pada Penelitian ini dapat diterima. Semua hard constraint pada setiap unit dapat terpenuhi dan soft contrsaint yaitu nilai jains fairness pada masing-masing unit setelah optimasi dibandingkan hasil jadwal otomatis meningkat mendekati nilai keadilan total yaitu satu. Nilai JFI pada unit Farmasi meningkat sebesar 47%, unit Nicu & Ruang Bayi meningkat sebesar 48%, unit IGD meningkat sebesar 20%, unit SIM & RM meningkat sebesar 2%, unit Gizi & Café meningkat sebesar 23% dan Ruang Operasi meningkat sebesar 2%. ======================================================================================== Scheduling labor hospital or known as staff healthcare rostering is the complex problems that must be faced by each hospital. Hospitals must consider many aspects such as the number of nurses, the division of shifts, the cost, the chance of a holiday or leave of absence and other constraints. Because a lot of these considerati ons, the scheduling manually will be very difficult and can not give the optimal solution. It is necessary the existence of a mathematical model to facilitate the scheduling problems with finding the most optimal solution. The problem is known with the ter m nurse rostering problem (NRP). In general, the modeling of the nurse rostering or hea l thcare staff rostering should pay attention to the boundaries of the object so that it can produce optimal results. Another problem of staff scheduling is a matter of j ustice between the staff on duty, how the division of the allocation of holidays, working time, or place of duty be the dimensions that need to be considered. Many research mention that the satisfaction a nurse or staff in the work influenced by the level of fairness in the division of the schedule. To resolve these problems, this research will be done scheduling using tabu search algorithm hyperheuristic. Algorithm Tabu Search hyper - heuristic will be used to provide solutions to the input problems with how to generate a new heuristic using the heuristic that already exists. viii The results of the optimization of the scheduling with algorithm tabu search hyper heuristics on the research can be accepted. All hard constraints on each unit can be met and soft contr saint i.e. the value of the jains fairness on each unit after optimization compared to the results of the schedule of automatic increases approaching the value of justice, a total that one. The value of JFI on the Pharmaceutical unit increased by 47%, Nicu , & Baby Room increased by 48%, the unit of the Emergency room increased by 20%, SIM & RM units increased by 2%, unit of Nutrition & Café increased by 23% and the Operating Room increased by 2%

Enhancing the Performance of Search Heuristics. Variable Fitness Functions and other Methods to Enhance Heuristics for Dynamic Workforce Scheduling.

Scheduling large real world problems is a complex process and finding high quality solutions is not a trivial task. In cooperation with Trimble MRM Ltd., who provide scheduling solutions for many large companies, a problem is identified and modelled. It is a general model which encapsulates several important scheduling, routing and resource allocation problems in literature. Many of the state-of-the-art heuristics for solve scheduling problems and indeed other problems require specialised heuristics tailored for the problem they are to solve. While these provide good solutions a lot of expert time is needed to study the problem, and implement solutions. This research investigates methods to enhance existing search based methods. We study hyperheuristic techniques as a general search based heuristic. Hyperheuristics raise the generality of the solution method by using a set of tools (low level heuristics) to work on the solution. These tools are problem specific and usually make small changes to the problem. It is the task of the hyperheuristic to determine which tool to use and when. Low level heuristics using exact/heuristic hybrid method are used in this thesis along with a new Tabu based hyperheuristic which decreases the amount of CPU time required to produce good quality solutions. We also develop and investigate the Variable Fitness Function approach, which provides a new way of enhancing most search-based heuristics in terms of solution quality. If a fitness function is pushing hard in a certain direction, a heuristic may ultimately fail because it cannot escape local minima. The Variable Fitness Function allows the fitness function to change over the search and use objective measures not used in the fitness calculation. The Variable Fitness Function and its ability to generalise are extensively tested in this thesis. The two aims of the thesis are achieved and the methods are analysed in depth. General conclusions and areas of future work are also identified

Transformation of the university examination timetabling problem space through data pre-processing

This research investigates Examination Timetabling or Scheduling, with the aim of producing good quality, feasible timetables that satisfy hard constraints and various soft constraints. A novel approach to scheduling, that of transformation of the problem space, has been developed and evaluated for its effectiveness. The examination scheduling problem involves many constraints due to many relationships between students and exams, making it complex and expensive in terms of time and resources. Despite the extensive research in this area, it has been observed that most of the published methods do not produce good quality timetables consistently due to the utilisation of random-search. In this research we have avoided random-search and instead have proposed a systematic, deterministic approach to solving the examination scheduling problem. We pre-process data and constraints to generate more meaningful aggregated data constructs with better expressive power that minimise the need for cross-referencing original student and exam data at a later stage. Using such aggregated data and custom-designed mechanisms, the timetable construction is done systematically, while assuring its feasibility. Later, the timetable is optimized to improve the quality, focusing on maximizing the gap between consecutive exams. Our solution is always reproducible and displays a deterministic optimization pattern on all benchmark datasets. Transformation of the problem space into new aggregated data constructs through pre-processing represents the key novel contribution of this research

Evolutionary multi-objective optimization in scheduling problems

Ph.DDOCTOR OF PHILOSOPH