A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments.Comment: 16 page

Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning

Dynamic environments pose great challenges for expensive optimization problems, as the objective functions of these problems change over time and thus require remarkable computational resources to track the optimal solutions. Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely unexplored. In this paper, we propose a simple yet effective meta-learning-based optimization framework for solving expensive dynamic optimization problems. This framework is flexible, allowing any off-the-shelf continuously differentiable surrogate model to be used in a plug-in manner, either in data-driven evolutionary optimization or BO approaches. In particular, the framework consists of two unique components: 1) the meta-learning component, in which a gradient-based meta-learning approach is adopted to learn experience (effective model parameters) across different dynamics along the optimization process. 2) the adaptation component, where the learned experience (model parameters) is used as the initial parameters for fast adaptation in the dynamic environment based on few shot samples. By doing so, the optimization process is able to quickly initiate the search in a new environment within a strictly restricted computational budget. Experiments demonstrate the effectiveness of the proposed algorithm framework compared to several state-of-the-art algorithms on common benchmark test problems under different dynamic characteristics

Using Dimensional Aware Genetic Programming to find interpretable Dispatching Rules for the Job Shop Scheduling Problem

Dispatching Rules (DRs) have been used in several applications in manufacturing systems. They assign priority to jobs in a queue choosing the next job to be executed. As they are challenging to design, genetic programming (GP) is being used to find better performative DRs. In GP, several different DRs are evolved, and due to some operations and selection processes inspired in nature, the DRs improve. However, little research has been done in trying to reach small and interpretable DRs. Usually, these generated expressions tend to become extremely large, with a couple of hundred terms or more. This work will innovate by using CFG (context-free grammars) methods, particularly CFG-GP and GE (Grammar Evolution), for reaching DRs which are dimensional aware. These methods will be compared as they have several distinct characteristics and were never used for this problem. The objective is that by forcing the syntax of the DRs to be correct, it will be possible to reach smaller and more interpretable DRs. Furthermore, an enumerator was made that found the best possible expression for a small DRs size, which will serve as a baseline to evaluate how well the different algorithms can explore these spaces and give the best possible DRs for a specific size. The results show a significant performance improvement in using DAGP methods for this problem. Moreover, GP/GE and CFG-GP can explore the small DRs optimally or close to optimally, managing to find the best small DRs

Incorporación de conocimiento en algoritmos evolutivos en problemas de scheduling

Los Algoritmos Evolutivos (AEs) son una de las metaheurísticas más ampliamente difundidas y estudiadas [28]. Estas, como muchas otras metaheuristicas, pueden ser mejoradas en su diseño a fin de realizar una exploración más eficiente del espacio de búsqueda. En el caso de los AEs, un adecuado desempeño de los mismos, depende en gran medida de los operadores y/o mecanismos de exploración involucrados y que adecuadamente implementados, pueden dar lugar a versiones más eficientes. En este sentido, la incoporación de conocimiento y/o información en el diseño de los AEs es de gran interés en la actualidad. Por esta razón, existen diversas líneas de investigación en la actualidad que tienen como objetivo principal el diseño avanzado de EAs a través de la incorporación de conocimiento. Esta línea de investigación, aborda diferentes estrategias tales como la incorporación del conocimiento experto a priori o el conocimiento adquirido durante la evolución, conceptos derivados de las teorías de evolución social, y cultural, entre otras.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Incorporación de conocimiento en algoritmos evolutivos en problemas de scheduling

Los Algoritmos Evolutivos (AEs) son una de las metaheurísticas más ampliamente difundidas y estudiadas [28]. Estas, como muchas otras metaheuristicas, pueden ser mejoradas en su diseño a fin de realizar una exploración más eficiente del espacio de búsqueda. En el caso de los AEs, un adecuado desempeño de los mismos, depende en gran medida de los operadores y/o mecanismos de exploración involucrados y que adecuadamente implementados, pueden dar lugar a versiones más eficientes. En este sentido, la incoporación de conocimiento y/o información en el diseño de los AEs es de gran interés en la actualidad. Por esta razón, existen diversas líneas de investigación en la actualidad que tienen como objetivo principal el diseño avanzado de EAs a través de la incorporación de conocimiento. Esta línea de investigación, aborda diferentes estrategias tales como la incorporación del conocimiento experto a priori o el conocimiento adquirido durante la evolución, conceptos derivados de las teorías de evolución social, y cultural, entre otras.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS