It is the efficient use of resources expected from an exam scheduling application. There are various criteria for efficient use of resources and for all tests to be carried out at minimum cost in the shortest possible time. It is aimed that educational institutions with such criteria successfully carry out central examination organizations. In the study, a two-stage genetic algorithm was developed. In the first stage, the assignment of courses to sessions was carried out. In the second stage, the students who participated in the test session were assigned to examination rooms. Purposes of the study are increasing the number of joint students participating in sessions, using the minimum number of buildings in the same session, and reducing the number of supervisors using the minimum number of classrooms possible. In this study, a general purpose exam scheduling solution for educational institutions was presented. The developed system can be used in different central examinations to create originality. Given the results of the sample application, it is seen that the proposed genetic algorithm gives successful results.1
This work was inspired by author experiences with a telescope scheduling. Author long time goal is to develop and further extend software for an autonomous observatory. The software shall provide users with all the facilities they need to take scientific images of the night sky, cooperate with other autonomous observatories, and possibly more. This works shows how genetic algorithm can be used for scheduling of a single observatory, as well as network of observatories.
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems
Digital Port Technologies Lab, School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院数字港口技术实验室）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；Department of Engineering, University of Cambridge（剑桥大学工程学院）；School of Engineering, Westlake University（西湖大学工程学院）
Karadeniz Technical University（卡拉德尼兹技术大学）；Gazi University（加齐大学）
Projects consist of interconnected dimensions such as objective, time, resource and environment. Use of these dimensions in a controlled way and their effective scheduling brings the project success. Project scheduling process includes defining project activities, and estimation of time and resources to be used for the activities. At this point, the project resource-scheduling problems have begun to attract more attention after Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are developed one after the other. However, complexity and difficulty of CPM and PERT proc…
Investigation of the Generalisation Ability of Genetic Programming-evolved Scheduling Rules in Dynamic Flexible Job Shop Scheduling
Centre for Data Science；Artificial Intelligence \& School of Engineering；Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm and Non-dominated Sorting Genetic Algorithm (NSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration, and NSGA for sorting local optimum points with consideration of potential areas. The proposed algorithm is applied to difficult nonlinear continuous multi…
The ever higher complexity of manufacturing systems, continually shortening life cycles of products and their increasing variety, as well as the unstable market situation of the recent years require introducing grater flexibility and responsiveness to manufacturing processes. From this perspective, one of the critical manufacturing tasks, which traditionally attract significant attention in both academia and the industry, but which have no satisfactory universal solution, is production scheduling. This paper proposes an approach based on genetics-based machine learning (GBML) to treat the prob…
Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；School of Engineering and Computer Science（工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）
The weekly maintenance schedule specifies when maintenance activities should be performed on the equipment, taking into account the availability of workers and maintenance bays, and other operational constraints. The current approach to generating this schedule is labour intensive and requires coordination between the maintenance schedulers and operations staff to minimise its impact on the operation of the mine. This paper presents methods for automatically generating this schedule from the list of maintenance tasks to be performed, the availability roster of the maintenance staff, and time w…
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems
Ecole Nationale Supérieure d'Informatique（国家高等信息学院）
National University of Defense Technology（国防科技大学）；Xidian University（西安电子科技大学）
The study of electromagnetic detection satellite scheduling problem (EDSSP) has attracted attention due to the detection requirements for a large number of targets. This paper proposes a mixed-integer programming model for the EDSSP problem and a genetic algorithm based on reinforcement learning (RL-GA). Numerous factors that affect electromagnetic detection are considered in the model, such as detection mode, bandwidth, and other factors. The RL-GA embeds a Q-learning method into an improved genetic algorithm, and the evolution of each individual depends on the decision of the agent. Q-learni…
Guiding Large Language Models with Genetic Programming-Evolved Heuristic Knowledge for Dynamic Multi-Mode Project Scheduling
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；School of Engineering and Computer Science（工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）
Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI
Research Engineering Team REANNZ New Zealand；Centre for Data Science；Artificial Intelligence \& School of Engineering；Computer Science Victoria University of Wellington New Zealand
Future space-based telescopes will leverage starshades as components that can be independently positioned. Starshades will adjust the light coming in from exoplanet host stars and enhance the direct imaging of exoplanets and other phenomena. In this context, scheduling of space-based telescope observations is subject to a large number of dynamic constraints, including target observability, fuel, and target priorities. We present an application of genetic algorithm (GA) scheduling on this problem that not only takes physical constraints into account, but also considers direct human suggestions …
University Sapienza of Rome（罗马第一大学）；Dalian Maritime University（大连海事大学）；Machine Intelligence Research Labs (MIR Labs)（机器智能研究实验室）
In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance o…
Constraint programming model and biased random-key genetic algorithm for the single-machine coupled task scheduling problem with exact delays to minimize the makespan
Institute of Computing, Universidade Federal da Bahia, Salvador, BA 40170-115, Brazil（计算学院，巴西巴伊亚联邦大学，萨尔瓦多，BA 40170-115）
In this paper we study a simple extension of the total weighted flowtime minimization problem for single and identical parallel machines. While the standard problem simply defines a set of jobs with their processing times and weights and assumes that all jobs have release date 0 and have no deadline, we assume that the release date of each job is a decision variable that is only constrained by a single global latest arrival deadline. To our knowledge, this simple yet practically highly relevant extension has never been studied. Our main contribution is that we show the NP- completeness of the …
Active-imaging agile earth observation satellite (AI-AEOS) is a new generation agile earth observation satellite (AEOS). With renewed capabilities in observation and active im-aging, AI-AEOS improves upon the observation capabilities of AEOS and provide additional ways to observe ground targets. This however makes the observation scheduling problem for these agile earth observation satellite more complex, especially when considering multi-strip ground targets. In this paper, we investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite (MOS…
Novosibirsk State Technical University（新西伯利亚国立技术大学）
The article presents a study of the Particle Swarm optimization method for scheduling problem. To improve the method's performance a restriction of particles' velocity and an evolutionary meta-optimization were realized. The approach proposed uses the Genetic algorithms for selection of the parameters of Particle Swarm optimization. Experiments were carried out on test tasks of the job-shop scheduling problem. This research proves the applicability of the approach and shows the importance of tuning the behavioral parameters of the swarm intelligence methods to achieve a high performance.
This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum sol…
School of Computer Science, University of Nottingham（诺丁汉大学计算机学院）
Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly me…
Stevens Institute of Technology（史蒂文斯理工学院）；New York University（纽约大学）；University of Calgary（卡尔加里大学）
The complexity of software tasks and the uncertainty of crowd developer behaviors make it challenging to plan crowdsourced software development (CSD) projects. In a competitive crowdsourcing marketplace, competition for shared worker resources from multiple simultaneously open tasks adds another layer of uncertainty to the potential outcomes of software crowdsourcing. These factors lead to the need for supporting CSD managers with automated scheduling to improve the visibility and predictability of crowdsourcing processes and outcomes. To that end, this paper proposes an evolutionary algorithm…
Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；Indian Institute of Technology（印度理工学院）；University of New South Wales（新南威尔士大学）
The resource constrained project scheduling problem (RCPSP) is an NP-Hard combinatorial optimization problem. The objective of RCPSP is to schedule a set of activities without violating any activity precedence or resource constraints. In recent years researchers have moved away from complex solution methodologies, such as meta heuristics and exact mathematical approaches, towards more simple intuitive solutions like priority rules. This often involves using a genetic programming based hyper-heuristic (GPHH) to discover new priority rules which can be applied to new unseen cases. A common probl…
This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation prob…
In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of gene…
In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance. At its core, AI Programmer uses genetic algorithms (GA) coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer's novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of GAs to include instruction-gene randomization bindings and progr…
ITMO University（圣彼得堡国立信息技术机械与光学大学）；St. Petersburg State University（圣彼得堡国立大学）；AIDEUS；Aix Marseille Université（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）
Universal induction relies on some general search procedure that is doomed to be inefficient. One possibility to achieve both generality and efficiency is to specialize this procedure w.r.t. any given narrow task. However, complete specialization that implies direct mapping from the task parameters to solutions (discriminative models) without search is not always possible. In this paper, partial specialization of general search is considered in the form of genetic algorithms (GAs) with a specialized crossover operator. We perform a feasibility study of this idea implementing such an operator i…
