A matheuristic for customized multi-level multi-criteria university timetabling

Course timetables are the organizational foundation of a university’s educational program. While students and lecturers perceive timetable quality individually according to their preferences, there are also collective criteria derived normatively such as balanced workloads or idle time avoidance. A recent challenge and opportunity in curriculum-based timetabling consists of customizing timetables with respect to individual student preferences and with respect to integrating online courses as part of modern course programs or in reaction to flexibility requirements as posed in pandemic situations. Curricula consisting of (large) lectures and (small) tutorials further open the possibility for optimizing not only the lecture and tutorial plan for all students but also the assignments of individual students to tutorial slots. In this paper, we develop a multi-level planning process for university timetabling: On the tactical level, a lecture and tutorial plan is determined for a set of study programs; on the operational level, individual timetables are generated for each student interlacing the lecture plan through a selection of tutorials from the tutorial plan favoring individual preferences. We utilize this mathematical-programming-based planning process as part of a matheuristic which implements a genetic algorithm in order to improve lecture plans, tutorial plans, and individual timetables so as to find an overall university program with well-balanced timetable performance criteria. Since the evaluation of the fitness function amounts to invoking the entire planning process, we additionally provide a proxy in the form of an artificial neural network metamodel. Computational results exhibit the procedure’s capability of generating high quality schedules

A mathematical programming tool for an efficient decision-making on teaching assignment under non-regular time schedules

[EN] In this paper, an optimization tool based on a MILP model to support the teaching assignment process is proposed. It considers not only hierarchical issues among lecturers but also their preferences to teach a particular subject, the non-regular time schedules throughout the academic year, different type of credits, number of groups and other specific characteristics. Besides, it adds restrictions based on the time compatibility among the different subjects, the lecturers' availability, the maximum number of subjects per lecturer, the maximum number of lecturers per subject as well as the maximum and minimum saturation level for each lecturer, all of them in order to increase the teaching quality. Schedules heterogeneity and other features regarding the operation of some universities justify the usefulness of this model since no study that deals with all of them has been found in the literature review. Model validation has been performed with two real data sets collected from one academic year schedule at the Spanish University Universitat Politecnica de Valencia.Solano Cutillas, P.; Pérez Perales, D.; Alemany Díaz, MDM. (2022). A mathematical programming tool for an efficient decision-making on teaching assignment under non-regular time schedules. Operational Research. 22(3):2899-2942. https://doi.org/10.1007/s12351-021-00638-12899294222

Goal programming approach to solve the timetabling problem at Turkish Military Academy

Cataloged from PDF version of article.The aim of this study is to propose a goal programming model to solve the timetabling problem at Turkish Military Academy. Since the problem is NPcomplete, it’s not easy to find an optimal solution all the time. It takes a lot of time of the people who are responsible to prepare the timetables of TMA. The model consists in all of the requirements, and is tested with the real data provided by Planning and Programming Department. Since the problem is so big to solve at once as a whole, a five-step iterative solution procedure is proposed. There are four priorities, three for teacher preferences and one for teaching loads. The model aims to minimize the deviations from the preferences and teaching loads of the teachers. Solution process produced a feasible and near-optimal timetable after four steps, in a reasonably short time compared to hand-made timetabling procedure. The result was improved by making some modifications in step five. In the conclusion, we mentioned the problems we faced, and presented our suggestions for future research.Şahin, TunaM.S

A Scenario-Based Parametric Analysis of Stable Marriage Approaches to the Army Officer Assignment Problem

This paper compares linear programming and stable marriage approaches to the assignment problem under conditions of uncertainty. Robust solutions should exhibit reduced variability in the presence of one or more additional constraints. Several variations of each approach are compared with respect to solution quality, as measured by the overall social welfare among Officers and Assignments, and robustness as measured by the number of changes after a number of randomized perturbations. We examine the contrasts between these methods in the context of assigning Army Officers among a set of identified assignments. Additional constraints are modeled after realistic scenarios faced by Army assignment managers, with parameters randomized. The Pareto efficient approaches, relative to these measures of quality and robustness, are identified and subjected to a regression analysis. The coefficients of these models provide insight into the impact the different scenarios under study, as well as inform any trade-off decisions between Pareto-optimal approaches

Genetic Algorithm For University Course Timetabling Problem

Creating timetables for institutes which deal with transport, sport, workforce, courses, examination schedules, and healthcare scheduling is a complex problem. It is difficult and time consuming to solve due to many constraints. Depending on whether the constraints are essential or desirable they are categorized as ‘hard’ and ‘soft’, respectively. Two types of timetables, namely, course and examination are designed for academic institutes. A feasible course timetable could be described as a plan for the movement of students and staff from one classroom to another, without conflicts. Being an NP-complete problem, many attempts have been made using varying computational methods to obtain optimal solutions to the timetabling problem. Genetic algorithms, based on Darwin\u27s theory of evolution is one such method. The aim of this study is to optimize a general university course scheduling process based on genetic algorithms using some defined constraints

A heuristic room matching algorithm in generating enhanced initial seed for the university course timetabling problem

The University Course Timetabling Problem (UCTP) such as the curriculum-based course timetabling problem is both an NP-hard and NP-complete scheduling problem.The nature of the problem concerns with the assignment of lecturers-courses to available teaching space in an academic institution.The Curriculum-Based University Course Timetabling Problem (CB-UCTP) has a high conflict-density and searching for an improved solution is not trivial.In this study, the authors propose a heuristic room matching algorithm which improves the seed of the CB-UCTP.The objective is to provide a reasonable search point to carry out any improvement phase and the results obtained indicate that the matching algorithm is able to provide very promising results as the fitness score of the solution is significantly enhanced in a very short period of time

Pemodelan penjadualan waktu kursus universiti menggunakan kaedah pengaturcaraan gol

Kajian ini dijalankan untuk membina sebuah model penjadualan waktu kursus universiti yang cekap berdasarkan pemodelanan matematik yang dibangunkan bagi menghasilkan jadual waktu kursus universiti yang optimal dan bebas konflik. Kajian ini dijalankan di Jabatan Sains Pengkomputeran dan Teori, Universiti Islam Antarabangsa Malaysia (UIAM) dengan melibatkan pengumpukan kursus-kursus wajib ke slot masa dan bilik-bilik kuliah. Pendekatan pengaturcaraan gol digunakan untuk membina model tersebut dan diselesaikan menggunakan perisian LINGO 12. Hasil akhir kajian ini menunjukkan model penjadualan waktu kursus yang dicadangkan ini mampu memenuhi kehendak dan kekangan yang telah ditetapkan serta mencapai matlamat untuk mengumpukkan kursus Tahun 4 ke slot masa pagi. Beberapa penambahbaikan turut dilakukan terhadap jadual waktu kursus yang telah sedia ada

A rectification strategy in genetic algorithms for academic timetabling problem

The university course timetabling problem is both an NP-hard and NP-complete scheduling problem. The nature of the problem concerns with the assignment of lecturers-courses to available teaching space in an academic institution and may take on the form of high school timetabling, examination timetabling or university course timetabling. In this paper, the authors attempt to construct a feasible timetable for a faculty department in a local university in Malaysia which at the present moment; the scheduling task is performed manually by an academic registrar. The feasible timetable is constructed by means of Genetic Algorithm, embedded with a rectification strategy which transforms infeasible timetables into feasible timetables