Hypercube FrameWork for ACO applied to timetabling

We present a resolution technique of the University course Timetabling problem (UCTP), this technique is based in the implementation of Hypercube framework using the Max-Min Ant System. We presented the structure of the problem and the design of resolution using this framework. A simplification of the UCTP problem is used, involving three types of hard restrictions and three types of soft restrictions. We solve experimental instances and competition instances the results are presented of comparative form to other techniques. We presented an appropriate construction graph and pheromone matrix representation. A representative instance is solved in addition to the schedules of the school of Computer science engineering of the Catholic University of Valparaiso. The results obtained for this instance appear. Finally the conclusions are given.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

عادة ما تكون مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) هي مشكلة تحسين الإندماجية. يستغرق الأمر جهود يدوية لعدة أيام للوصول إلى جدول زمني مفيد ، ولا تزال النتائج غير جيدة بما يكفي. تُستخدم طرق مختلفة من (الإرشاد أو الإرشاد المساعد) لحل UCTP بشكل مناسب. لكن هذه الأساليب عادةً ما تعطي حلول محدودة. يعالج إطار العمل الاسترشادي العالي هذه المشكلة المعقدة بشكل مناسب. يقترح هذا البحث استخدام محسن سرب الجسيمات استنادا على منهجية الإرشاد العالي (HH PSO) لمعالجة مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) . محسن سرب الجسيمات PSO يستخدام كطريقة ذات مستوى عالي لتحديد تسلسل الاستدلال ذي المستوى المنخفض (LLH) والذي من ناحية أخرى يستطيع توليد الحل الأمثل. لنهج المقترح يقسم الحل إلى مرحلتين (المرحلة الأولية ومرحلة التحسين). قمنا بتطوير LLH جديد يسمى "أقل عدد ممكن من الغرف المتبقية"  لجدولة الأحداث. يتم استخدام مجموعتي بيانات مسابقة الجدول الزمني الدولية (ITC)  ITC 2002 و ITC 2007 لتقييم الطريقة المقترحة. تشير النتائج الأولية  إلى أن الإرشاد منخفض المستوى المقترح يساعد في جدولة الأحداث في المرحلة الأولية. بالمقارنة مع LLH الأخرى ، الطريقة LLH المقترحة جدولت المزيد من الأحداث لـ 14 و 15 من حالات البيانات من 24 و 20 حالة بيانات من ITC 2002 و ITC 2007 ، على التوالي. تظهر الدراسة التجريبية أن HH PSO تحصل على معدل خرق أقل للقيود في سبع وستة حالات بيانات من ITC 2007 و ITC 2002 ، على التوالي. واستنتج هذا البحث أن LLH المقترحة يمكن أن تحصل على حل معقول وملائم إذا تم تحديد الأولوياتThe university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed approach generates solutions into two phases (initial and improvement). A new LLH named “least possible rooms left” has been developed and proposed to schedule events. Both datasets of international timetabling competition (ITC) i.e., ITC 2002 and ITC 2007 are used to evaluate the proposed method. Experimental results indicate that the proposed low-level heuristic helps to schedule events at the initial stage. When compared with other LLH’s, the proposed LLH schedule more events for 14 and 15 data instances out of 24 and 20 data instances of ITC 2002 and ITC 2007, respectively. The experimental study shows that HH PSO gets a lower soft constraint violation rate on seven and six data instances of ITC 2007 and ITC 2002, respectively. This research has concluded the proposed LLH can get a feasible solution if prioritized

New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201

Global Optimization Using Local Search Approach for Course Scheduling Problem

Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously

A novel population-based local search for nurse rostering problem

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments

Linear Combinations of Heuristics for Examination Timetabling

Although they are simple techniques from the early days of timetabling research, graph colouring heuristics are still attracting significant research interest in the timetabling research community. These heuristics involve simple ordering strategies to first select and colour those vertices that are most likely to cause trouble if deferred until later. Most of this work used a single heuristic to measure the difficulty of a vertex. Relatively less attention has been paid to select an appropriate colour for the selected vertex. Some recent work has demonstrated the superiority of combining a number of different heuristics for vertex and colour selection. In this paper, we explore this direction and introduce a new strategy of using linear combinations of heuristics for weighted graphs which model the timetabling problems under consideration. The weights of the heuristic combinations define specific roles that each simple heuristic contributes to the process of ordering vertices. We include specific explanations for the design of our strategy and present the experimental results on a set of benchmark real world examination timetabling problem instances. New best results for several instances have been obtained using this method when compared with other constructive methods applied to this benchmark dataset