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

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

DEM Timetabling Project ? Development/implementation of an algorithm to support the creation of timetables

This work presents the development of an algorithm to support the process of creating academic timetables, specifically aimed at solving the University Course Timetabling Problem. To date, this problem is solved manually in Instituto Superior de Engenharia do Porto, where professors and engineers face the complex task of creating timetables based on schedules from previous years. The proposed solution aimed to support the process of creating timetables at ISEP, reducing the time and human resources required for this task. The developed algorithm uses an integer programming approach and can consider a variety of constraints and preferences of both faculty and students. It was designed to adapt and optimize the timetable creation process as needs evolve, ensuring future demands can be easily accommodated. The algorithm implementation was based on the Python programming language and the Pyomo library, offering a flexible and efficient approach to optimizing resource allocation. Additionally, the system is designed to import data from real-world sources, simplifying the integration of crucial information. The result assigned all the 128 one-hour classes among the week, presenting the faculty member, the classroom assigned and the type of class according to each course. This research presents feasible solutions that need improvement on the demanding conditions and restrictions imposed by ISEP. The computational results obtained offered a significantly decrease in the time resource used, compared to the manual work previously done

The Optimum Combination Of Local Searches For Genetic Operators In Memetic Algorithm For The Space Allocation Problem [QA9.58. S624 2008 f rb].

Dalam tesis ini, kami membuat penyelidikan mengenai pengagihan ruang di universiti. Kajian ini memfokus kepada pengagihan ruang dalam penyediaan jadual waktu. This thesis investigates the university space allocation problem, which focuses on the distribution of events among the available venues, without violating any hard constraints while satisfying as many soft constraints as possible and ensure optimum space utilization

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

Modelling Classroom Space Allocation at University of Rwanda-A Linear Programming Approach

Education and training play a key role as the human capital function. This is especially true for tertiary education. However, infrastructure and equipment limitations are some factors that limits levels of students\u27 enrollment in universities. This is moreso the case in developing countries where much of the infrastructure developments are donor-funded. For institutional managers and administrators, the allocating of the limited available classroom space is a constant problem that needs sophisticated approaches to deal with. Linear Optimization technique has shown promise in dealing with this problem. This research seeks to assess the Rwandan education system and highlight strides made to broaden access to tertiary education. Using data accessed from the College of Science and Technology for the 2019/2020 academic year, a linear programming model is formulated to assess the level of usage of the available classroom space at the College. The model is solved using the Dual Simplex algorithm via the Cplex solver implemented in AMPL. A solution analysis shows that, out of the 68 classrooms available on the Nyarugenge campus, only 18 with a seating capacity of 2,147 are being used to facilitate the learning of approximated 4,088 students, and that 50 classrooms with a seating capacity of 1,506 are being underutilized or not being used at all. Relevant recommendations including that the college explores the usage of virtual laboratory platforms to overcome space and material limitations associated with physical laboratories are presented

Using Distributed Agents to Create University Course Timetables Addressing Essential & Desirable Constraints and Fair Allocation of Resources

In this study, the University Course Timetabling Problem (UCTP) has been investigated. This is a form of Constraint Satisfaction Problem (CSP) and belongs to the NP-complete class. The nature of a such problem is highly descriptive, a solution therefore involves combining many aspects of the problem. Although various timetabling algorithms have been continuously developed for nearly half a century, a gap still exists between the theoretical and practical aspects of university timetabling. This research is aimed to narrow the gap. We created an agent-based model for solving the university course timetabling problem, where this model not only considers a set of essential constraints upon the teaching activities, but also a set of desirable constraints that correspond to real-world needs. The model also seeks to provide fair allocation of resources. The capabilities of agents are harnessed for the activities of decision making, collaboration, coordination and negotiation by embedding them within the protocol designs. The resulting set of university course timetables involve the participation of every element in the system, with each agent taking responsibility for organising of its own course timetable, cooperating together to resolve problems. There are two types of agents in the model; these are Year-Programme Agent and Rooms Agent. In this study, we have used four different principles for organising the interaction between the agents: First-In-First-Out & Sequential (FIFOSeq), First-In-First-Out & Interleaved (FIFOInt), Round-Robin & Sequential (RRSeq) and Round-Robin & Interleaved (RRInt). The problem formulation and data instances of the third track of the Second International Timetabling Competition (ITC-2007) have been used as benchmarks for validating these implemented timetables. The validated results not only compare the four principles with each other; but also compare them with other timetabling techniques used for ITC-2007. The four different principles were able to successfully schedule all lectures in different periods, with no instances of two lectures occupying the same room at the same time. The lectures belonging to the same curriculum or taught by the same teacher do not conflict. Every lecture has been assigned a teacher before scheduling. The capacity of every assigned room is greater than, or equal to, the number of students in that course. The lectures of each course have been spread across the minimum number of working days with more than 98 percent success, and for more than 75 percent of the lectures under the same curriculum, it has been possible to avoid isolated deliveries. We conclude that the RRInt principle gives the most consistent likelihood of ensuring that each YPA in the system gets the best and fairest chance to obtain its resources

Some improved genetic-algorithms based heuristics for global optimization with innovative applications

The research is a study of the efficiency and robustness of genetic algorithm to instances of both discrete and continuous global optimization problems. We developed genetic algorithm based heuristics to find the global minimum to problem instances considered. In the discrete category, we considered two instances of real-world space allocation problems that arose from an academic environment in a developing country. These are the university timetabling problem and hostel space allocation problem. University timetabling represents a difficult optimization problem and finding a high quality solution is a challenging task. Many approaches, based on instances from developed countries, have been reported in the literature. However, most developing countries are yet to appreciate the deployment of heuristics and metaheuristics in handling the timetabling problem. We therefore worked on an instance from a university in Nigeria to show the feasibility and efficiency of heuristic method to the timetabling problem. We adopt a simplified bottom up approach in which timetable are build around departments. Thus a small portion of real data was used for experimental testing purposes. As with similar baseline studies in literature, we employ genetic algorithm to solve this instance and show that efficient solutions that meet stated constraints can be obtained with the metaheuristics. This thesis further focuses on an instance of university space allocation problem, namely the hostel space allocation problem. This is a new instance of the space allocation problems that has not been studied by metaheuristic researchers to the best of our knowledge. The problem aims at the allocation of categories of students into available hostel space. This must be done without violating any hard constraints but satisfying as many soft constraints as possible and ensuring optimum space utilization. We identified some issues in the problem that helped to adapt metaheuristic approach to solve it. The problem is multi-stage and highly constrained. We first highlight an initial investigation based on genetic algorithm adapted to find a good solution within the search space of the hostel space allocation problem. Some ideas are introduced to increase the overall performance of initial results based on instance of the problem from our case study. Computational results obtained are reported to demonstrate the effectiveness of the solution approaches employed. Sensitivity analysis was conducted on the genetic algorithm for the two SAPs considered to determine the best parameter values that consistently give good solutions. We noted that the genetic algorithms perform well specially, when repair strategies are incorporated. This thesis pioneers the application of metaheuristics to solve the hostel space allocation problem. It provides a baseline study of the problem based on genetic algorithms with associated test data sets. We report the best known results for the test instances. It is a known fact that many real-life problems are formulated as global optimization problems with continuous variables. On the continuous global optimization category therefore, we focus on improving the efficiency and reliability of real coded genetic algorithm for solving unconstrained global optimization, mainly through hybridization with exploratory features. Hybridization has widely been recognized as one of the most attractive approach to solving unconstrained global optimization. Literatures have shown that hybridization helps component heuristics to taking advantage of their individual strengths while avoiding their weaknesses. We therefore derived three modified forms of real coded genetic algorithm by hybridizing the standard real-coded genetic algorithm with pattern search and vector projection. These are combined to form three new algorithms namely, RCGA-PS, RCGA-P, and RCGA-PS-P. The hybridization strategy used and results obtained are reported and compared with the standard real-coded genetic algorithm. Experimental studies show that all the modified algorithms perform better than the original algorithm

A Classification of Hyper-heuristic Approaches

The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research