Improved local search approaches to solve the post enrolment course timetabling problem

In this work, we are addressing the post enrollment course timetabling (PE-CTT) problem. We combine different local search algorithms into an iterative two stage procedure. In the first stage, Tabu Search with Sampling and Perturbation (TSSP) is used to generate feasible solutions. In the second stage, we propose an improved variant of Simulated Annealing (SA), which we call Simulated Annealing with Reheating (SAR), to improve the solution quality of feasible solutions. SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. SAR eliminates the need for extensive tuning as is often required in conventional SA. The proposed methodologies are tested on the three most studied datasets from the scientific literature. Our algorithms perform well and our results are competitive, if not better, compared to the benchmarks set by the state of the art methods. New best known results are provided for many instances

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

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

Programación matemática binaria por etapas en la elaboración de un horario universitario

Objetivo: establecer una estrategia que permita elaborar un horario universitario en tres etapas, utilizando programación matemática, tomando en cuenta la problemática que enfrentan la mayoría de los centros educativos públicos del nivel superior en México, que incluye la contratación de profesores de forma temporal en cada ciclo escolar. Método: la estrategia contempló la descomposición del problema original en tres modelos matemáticos, considerando variables binarias de dos índices, el uso de subconjuntos en el modelado y el empleo de una heurística. Resultados: se generaron horarios de clase compactos para estudiantes, en los que se aprovecharon los espacios de las aulas y se empleó de manera eficiente a los profesores de la universidad. La estrategia logró la automatización del proceso en la elaboración de horarios. Limitaciones: el trabajo presentado, analiza el caso del Tecnológico Nacional de México en Celaya. Por el momento, no se considera el uso de laboratorios, ni la aleatoriedad de la demanda de grupos y materias. Principales hallazgos: la estrategia expuesta, generó una reducción de al menos 98.34 % en el número de variables, permitiendo a la técnica exacta de ramificación y acotamiento alcanzar tiempos eficientes en la búsqueda de una solución, en un problema clasificado como NP-Duro

A simulation-optimization approach for a service-constrained multi-echelon distribution network

Academic research on (s,S) inventory policies for multi-echelon distribution networks with deterministic lead times, backordering, and fill rate constraints is limited. Inspired by a real-life Dutch food retail case we develop a simulation-optimization approach to optimize (s,S) inventory policies in such a setting. We compare the performance of a Nested Bisection Search (NBS) and a novel Scatter Search (SS) metaheuristic using 1280 instances from literature and we derive managerial implications from a real-life case. Results show that the SS outperforms the NBS on solution quality. Additionally, supply chain costs can be saved by allowing lower fill rates at upstream echelons

An Assignment Problem and Its Application in Education Domain: A Review and Potential Path

This paper presents a review pertaining to assignment problem within the education domain, besides looking into the applications of the present research trend, developments, and publications. Assignment problem arises in diverse situations, where one needs to determine an optimal way to assign n subjects to m subjects in the best possible way.With that, this paper classified assignment problems into two, which are timetabling problem and allocation problem. The timetabling problem is further classified into examination, course, and school timetabling problems, while the allocation problem is divided into student-project allocation, new student allocation, and space allocation problems. Furthermore, the constraints, which are of hard and soft constraints, involved in the said problems are briefly elaborated.In addition, this paper presents various approaches to address various types of assignment problem. Moreover, direction and potential paths of problem solving based on the latest trend of approaches are also highlighted.As such, this review summarizes and records a comprehensive survey regarding assignment problem within education domain, which enhances one's understanding concerning the varied types of assignment problems, along with various approaches that serve as solution

Penyelesaian Penjadwalan Mata Kuliah Menggunakan Metode Hiperheuristik Dengan Hibridisasi Algoritma Tabu Search, Simulated Annealing, Dan Self-Adaptive Pada Lintas Domain Permasalahan

Penjadwalan diperlukan sebagai pengalokasian sumber daya untuk menyelesaikan sebuah pekerjaan dengan batasan-batasan yang telah didefinisikan sehingga dapat memaksimalkan kemungkinan alokasi atau meminimalisir pelanggaran batasan. Salah satu jenis penjadwalan pada bidang pendidikan yaitu Post-Enrollment Course Timetabling (PE-CTT). Tantangan yang dihadapi pada PE-CTT yaitu perbedaan permasalahan, sejumlah batasan, dan persyaratan berbeda pada satu universitas dengan universitas lainnya sehingga sulit untuk menemukan solusi yang umum dan efektif. Salah satu solusi yang dapat mengembangkan sistem yang lebih general dengan menggunakan metode yang lebih murah dan tetap dapat menyelesaikan masalah adalah dengan menggunakan pendekatan Hyper-Heuristic. Pengujian akan dilakukan pada lintas domain yaitu dataset Socha dan dataset ITC-2007. Strategi Self-Adaptive digunakan sebagai strategi untuk memilih Low-Level-Heuristic (LLH) dan Simulated Annealing dan Tabu Search sebagai strategi Move Acceptance (MA) untuk menyelesaikan permasalahan penjadwalan mata kuliah tersebut. Hasil yang didapatkan pada dataset Socha, algoritma SATSSA menghasilkan nilai yg lebih baik dibandingkan dengan algoritma lain pada 2 instance. Algoritma SATSSA mampu mencapai nilai yang paling optimum pada 5 instance dataset Socha. Algoritma SATSSA menghasilkan nilai yang lebih optimum dibandingkan dengan algoritma lain pada 5 instance dataset ITC2007 yaitu instance early1, early2, hidden20, hidden21, dan hidden23. ================================================================================================================================== Timetabling is needed to complete a job with allocating resources and defined boundaries to maximize the possibility of allocation or minimize the violation of boundaries. One type of timetabling in the education field is Post-Enrollment Course Timetabling (PE-CTT). The challenges faced in the PE-CTT are differences in problems, a number of limitations, and requirements that differ from one university to another so that it is difficult to find common and effective solutions. One solution that can develop more general systems by using cheaper methods and still being able to solve problems is the Hyper-Heuristic approach. Testing will be carried out on cross domains namely the Socha dataset and the ITC-2007 dataset. The Self-Adaptive Strategy is used as a strategy for selecting Low-Level-Heuristics (LLH) and Simulated Annealing and Taboo Search as a Move Acceptance (MA) strategy to solve the course timetabling problems. The results obtained in the Socha dataset, SATSSA algorithm produces better values compared to other algorithms in 2 instances. SATSSA algorithm is able to achieve the most optimum value on 5 Socha dataset instances. SATSSA algorithm produces more optimum values compared to other algorithms on 5 ITC2007 dataset instances, namely early1, early2, hidden20, hidden21, and hidden23 instances. Key words : Post Enrollment Course Timetabling, Simulated Annealing, Tabu Search, Self-Adaptive, Socha Dataset, ITC-2007 Datase

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems